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The Two Risks Every Industrial SME Leader Now Faces with AI

Wed, 20 May 2026 19:23:05 GMT

AI risk is no longer optional for SME leaders. It has become a choice of which risk to carry. The job is no longer to decide if AI belongs in the business - it is to decide WHERE to use it and HOW - to decrease risk and increase gains.

This article unpacks the two risks every industrial SME leader is now sitting on, and how to choose between them with the discipline you already apply to safety, quality, and capital decisions.

Operational Overview

The narrative is wrong - SME AI adoption HAS risen sharply, but most use is shallow rather than embedded.

Risk is no longer binary - Adopting AI carries risk. Avoiding AI also carries risk. The job is to choose.

Industrial SMEs face stakes others do not - Safety, downtime, IP, quoting accuracy and compliance change the calculus.

Shadow AI is the silent default - 71% of UK employees already use unapproved AI tools at work, whether leaders know it or not.

Controlled adoption wins - The real divide is not adopters versus laggards. It is controlled adopters versus chaotic ones.

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The two risks every industrial SME leader now faces

The visible symptom is hesitation. The leader is unsure whether to commit, when to commit, or what to commit to. Vendors push. Boards ask. Competitors hint at progress. The leader pauses.

Underneath the hesitation sit two risks. They cannot both be avoided.

Risk A: The risk of uncontrolled adoption. Reckless implementation. Pilots without ownership. Tools without rules. AI applied to workflows that were never mapped. Outputs trusted without validation. Customer data pasted into consumer chatbots. Quoting errors that ripple into contracts. Hallucinated technical advice given to a service engineer in the field.

Risk B: The risk of uncontrolled avoidance. Delay treated as caution. Shadow AI growing beneath leadership visibility. Skills atrophying while competitors compound their gains. Talent walking to firms that have built proper AI capability. A workforce already using AI without policy, without training, without anyone responsible for what happens next.

The "no AI" position does not remove either risk. It often just removes leadership from the conversation. Microsoft's 2025 UK research surveyed 2,003 employees and found that 71% of UK workers had used unapproved consumer AI tools at work, with 51% doing so weekly . Avoidance does not stop AI entering the business. It only stops the leader knowing about it.

The question is no longer "should we use AI." It is "which risk do we want to carry, and how do we manage it."

What is shadow AI?

Shadow AI is the unsanctioned use of AI tools by employees outside any company policy or oversight. It happens when staff use personal or free AI accounts for work tasks - drafting, summarising, quoting - without approval. The tools are not the problem. The absence of rules, training and a named owner is.

Engineer's Note:

I work with SME leaders who are still battling the existential argument about AI - should we, should we not, what are the risks - and have not personally used it themselves in any meaningful way. The leaders I see making real progress have stopped having that argument. They have accepted that the question is not whether AI presents risk. It is which risks they choose to handle, and which ones they let run loose in their business.

The lazy narrative is wrong

The accusation goes like this. SME leaders, especially in industrial sectors, are slow, cautious, behind. They are losing ground to faster-moving competitors. They do not understand the technology. They are scared.

The data tells a different story. ONS Business Insights and Conditions Survey Wave 147 found that 25% of UK businesses now use some form of AI , rising to 44% for firms with 250+ employees. The British Chambers of Commerce/Intuit survey of 1,500+ UK SMEs found that 35% of SMEs are actively using AI, up from 25% a year earlier . The follow-up BCC/Atos study with the University of Essex put the figure at 54% of UK firms now actively using AI , but with only 11% of SMEs using it to a great extent to automate or streamline operations.

SME leaders are not ignoring AI. The Institute of Directors found nearly two-thirds of UK directors personally use AI tools to aid their work , while only half say their organisation uses AI across any function. The accusation of resistance does not survive contact with the evidence.

What is true is that adoption is shallow. The numbers measure the presence of AI. They do not measure whether it has changed how the business runs. That is the gap the lazy narrative misses.

What is the difference between shallow and embedded AI adoption?

Shallow adoption means individuals using generic AI tools for individual tasks - drafting emails, summarising documents, brainstorming. Embedded adoption means AI is built into a defined workflow with a named owner, approved tools, data rules, success metrics, and human oversight. The first changes how a person works. The second changes how the business runs.

Why industrial SMEs carry a heavier risk profile

Industrial businesses are not the same as office-based ones. The risks of AI use are not limited to bad copy, embarrassing emails, or wasted SaaS budget. The risks are operational, contractual, and physical.

A hallucinated technical specification given to a customer can land in a contract. An incorrect maintenance instruction can damage a machine. A quoting error can lose a margin or worse, win a job at a loss. A faulty quality inspection output can let a defective batch reach a customer. A piece of leaked customer data or compromised IP can break a long-standing relationship. A workflow change that disrupts production can cost more in a day than the AI tool saves in a year.

These are not abstract concerns. They are the same concerns that have shaped every other capital and process decision an industrial leader has ever made. Safety, quality, traceability, and accountability are not optional in industrial businesses. They are the operating discipline.

Make UK and Autodesk surveyed 151 UK manufacturers and found that only 16% of manufacturers regard themselves as 'knowledgeable' about AI , while 75% plan to increase AI investment in the next 12 months. The follow-up Make UK Executive Survey 2026 with PwC found that 60% of UK manufacturers cite skills as the major barrier to AI and automation adoption . The DSIT Technology Adoption Review reported that just 7% of UK manufacturers in 2024 were regarded as very knowledgeable about AI applications.

The pattern is clear. Industrial SME leaders are not refusing AI. They are buying time to understand what they are committing the business to. That is rational. It only becomes irrational when the time bought is not used.

There is a second danger here that is easy to miss. Where a leader is worried about the technical errors in the most complex parts of the business, he or she can end up slowing down adoption everywhere else through sheer indecision. The fix is to start small and low risk and build. Every task has its own profile of opportunity and challenge. A task audit is useful here, because a business should generally start with the simple problems and build towards the more complex ones. That way the whole company, in one form or another, can join the journey, build confidence, gain experience, and let the improvements compound.

The shadow AI risk no one chose to take

The most overlooked risk in industrial SMEs is the one no leader formally chose. It is the risk created when AI use exists without policy.

The shadow AI numbers are uncomfortable. ISACA's 2025 European survey of 561 IT and cyber professionals found that only 31% of organisations have a formal, comprehensive AI policy , despite 83% reporting employee AI use. IBM's Cost of a Data Breach Report 2025, based on 600 organisations globally, found that organisations with high levels of shadow AI experienced average breach costs $670,000 higher than those with low or no shadow AI .

If a sales engineer pastes a customer's confidential RFI into a free ChatGPT account to speed up a quote, that is not innovation. It is an unmanaged data exposure. If an operations manager uploads a CAD-derived bill of materials to summarise it, that is a procurement risk. If a finance director uses a personal AI account to draft management commentary on board figures, that is a confidentiality risk.

These are not hypothetical. They are happening in industrial SMEs today, while leaders debate whether the business is ready for AI.

The real framing is this. A "no AI" policy does not remove AI from the business. It often just removes AI from view. The risk is still being carried. The leader is just not seeing it.

How can an SME bring shadow AI under control?

Start by accepting it is already happening, then replace the vacuum with structure. Provide an approved tool, a plain-English policy on what data can and cannot be entered, and basic training. Name a senior owner. Bringing AI into the open is safer than banning it, because a ban only pushes the same usage further out of sight.

From the Field:

A close colleague of mine is living this right now. He is flying high on AI in his own work - not because he has integrated it with his ERP or engineering models, but because he has built simple systems to manage his email, proposals, and tasks. He talks his thoughts into a voice tool, a copilot agent shapes them, and the output comes back to him to review. He is not waiting for the company to roll out AI. He is already three months ahead of his colleagues. Meanwhile his leadership team is still debating whether AI is "good enough to let loose."

Pilot fatigue is rational - and predictable

The second reason industrial SME leaders are cautious is that they have watched a lot of AI pilots go nowhere.

MIT's NANDA initiative reviewed 52 executive interviews, 153 leader surveys, and 300 public AI deployments. The headline finding was that 95% of GenAI pilots delivered no measurable P&L impact . Note the scope - this is enterprise and mid-market data, not UK SME specific, but the pattern is broadly recognised across business sizes. The biggest ROI came from back-office automation, not from the customer-facing transformation projects that consumed most of the budget.

Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls . That is a forecast, not an observed outcome, but it is consistent with the pattern in the live market.

RAND's research on AI project failure identified the root causes. Projects fail because the business problem was misunderstood, the data was insufficient, the infrastructure was weak, or AI was applied to the wrong problem in the first place. The failures are rarely about the AI itself. They are about the surrounding conditions.

The right read is not "AI does not work." The right read is that AI applied without a workflow owner, a clear business problem, usable data, and a success metric does not work. Industrial SME leaders who have seen pilots stall are responding rationally. The problem is not the questions they are asking. The problem is when those questions never lead to a decision.

On the Ground:

I hear a fair few leaders wanting to leap straight into AI agents when they have not yet mastered the basics - structured prompting, conversational prompting, or building their own skills, Custom GPTs or Gems. It is a step too far, too quickly, and that is where it gets dangerous. You end up with a lot of pilot fatigue from projects that were never set up to succeed.

Don't switch on a machine you haven't built

There is an engineering parallel that makes the pilot fatigue point land. Jumping straight to autonomous AI agents before you have built the basics is like walking onto someone else's machine, switching it on to run on its own, and walking away - without having built it, commissioned it, or read the instruction manual.

No engineer would do that. You build the machine. You understand each component. You commission it under supervision. You run it manually before you automate it. Only when it has proven itself under control do you let it run unattended.

AI is no different. Structured prompting, conversational prompting, and building your own Custom GPTs, Projects or Gems are the equivalent of learning the machine before you trust it to run alone. Skip those steps and the agent does not save you time - it quietly creates risk you cannot see until something breaks. Build the foundations first, then automate. That sequence is what separates a controlled rollout from an expensive lesson.

The real divide is structured versus chaotic adoption

The market is now split into two groups. They are not adopters and non-adopters. The numbers no longer support that split.

The real divide is between businesses adopting AI in scattered, unmanaged ways and businesses building disciplined rollout models. McKinsey's State of AI 2025, surveying 1,993 respondents across 105 countries, found that companies the firm classes as "AI high performers" - the ones attributing 5% or more of EBIT to AI - are three times more likely than peers to strongly agree that senior leaders demonstrate clear ownership and commitment to AI initiatives . They are also nearly three times more likely to have fundamentally redesigned workflows as part of their AI efforts.

That is the gap. It is not about which company bought ChatGPT first. It is about which one connected AI to a real workflow with named ownership and a measured outcome.

Builder's Perspective:

I come across SME leaders who have not yet processed the true state of AI. They are still wondering if their company should or should not use it - battling the existential argument and the risk debate, without having fully used the tools themselves. The leaders I work with who have started using AI personally, and finding the savings, are getting to a point where they can change the game. It starts in the office - admin, project management, reporting, customer service, sales and marketing - not to replace people but to augment them.

What controlled AI adoption looks like

Controlled adoption is not slower adoption. It is structured adoption. The difference matters because the structure is what makes scaling possible without breaking the business.

The features of a controlled rollout are consistent across the better evidence base:

A named senior owner sits above the AI work. Not the IT manager. Not the marketing assistant who happens to be interested. A board-level or director-level owner who is accountable for the AI register, the policy, and the outcomes.

A plain-English acceptable-use policy exists. It does not need to be a legal document. It needs to be operational - what can be pasted in, what cannot, when human review is mandatory, which tools are approved. Creating accountability here is also so vital.

Pilots are tied to real workflows. Not "let's see what AI can do." Specific workflows - technical quoting, RFI response, management-pack summarisation, service-knowledge retrieval, customer-service triage with human escalation.

Every pilot has a success metric agreed before it starts. Time saved per quote. Error reduction in a process. Faster turnaround on a defined deliverable. Reduced downtime in a maintenance cycle. Without a metric, the pilot cannot be killed and cannot be scaled. It just runs.

Human oversight is designed into the workflow, not bolted on at the end. In industrial SMEs, this is not a weakness. It is the trust mechanism that makes the rest of the AI usable.

Training is funded alongside the tools. The Make UK skills barrier is not solved by licences. It is solved by structured, role-specific upskilling - especially for the leadership team and the function owners.

The CONTROL framework for SME AI decisions

If you want a single device to anchor every AI conversation in your business, this is the structure I use with industrial SME leaders.

C - Choose a real workflow. Start with a business process that already matters. Quoting. Reporting. Service triage. Document search. Maintenance planning. Sales research. Not a generic "AI strategy." A defined piece of work.

O - Own it at senior level. Assign one accountable leader. AI should not be left entirely to IT, marketing, or one enthusiastic individual. Senior ownership is the single biggest predictor of whether an AI initiative scales.

N - Name the risks. Define what could go wrong before you deploy. Bad data. Hallucinated outputs. Customer-data leakage. IP exposure. Safety risk. Poor customer experience. Naming the risks makes them manageable. Ignoring them makes them inevitable.

T - Train people and set tool rules. Approved tools. Plain-English usage rules. Worked examples of what data must never be pasted into public AI systems. Annual refresh.

R - Review outputs with humans. Decide which outputs need human checking. Legal, safety, technical, customer, financial, and operational outputs almost always do. The trust comes from the review, not from the output itself.

O - Observe measurable outcomes. Track whether the pilot saves time, improves quality, reduces errors, shortens response times, or improves commercial outcomes. Define the metric before you start.

L - Limit scaling until value is proven. Only scale when the workflow, data, governance, and measurement model work. Premature scaling is the most expensive failure mode in AI.

The framework is deliberately simple. The discipline it asks for is not.

Where to start: office before factory

The biggest mistake I see is industrial SME leaders trying to start their AI journey with the most complex, highest-stakes part of the business. ERP integration. Engineering model integration. Predictive maintenance on critical assets. Real-time quality inspection.

These are not wrong destinations. They are wrong starting points.

The most defensible early wins for industrial SMEs sit in the office, not on the shop floor. Document summarisation and knowledge retrieval. Technical quote drafting and RFI response. CRM enrichment and prospect research. Management-pack analysis. Customer-service triage with human escalation. Email management and proposal drafting. Internal reporting.

I have seen huge jumps in productivity in the back office, and this can pave the way to AI adoption in a broader sense. Sales and marketing is also a great starting point - if you use a computer, there is sure to be a use case and a real win waiting to be found.

These workflows have three things in common. The data is usable. The risk is bounded. The outcome is measurable. They are also the workflows where leaders themselves spend a meaningful share of their week, which means the leader can model the behaviour, refine the prompts, and bring the learning into the broader rollout.

This is not about replacing people. It is about augmenting them - giving each function the time and capacity to do the more valuable work that they are already paid to do. Predictive maintenance, computer-vision quality, and ERP-integrated agents are real opportunities. They just sit further along the journey, after the team has built the muscle and the leadership has built the judgement to govern them.

The smartest leaders are not refusing AI

Industrial SME leaders are not anti-AI. They are anti-uncontrolled risk. That is a defensible, professional, leadership-grade position.

It only becomes a problem when the caution does not produce structure. Without governance, caution becomes delay. Without leadership use, caution becomes ignorance. Without function-led pilots, caution becomes shadow AI. Without measured outcomes, caution becomes theatre.

The smartest SME leaders are not asking how to use AI everywhere. They are asking where AI can improve a real workflow without creating unacceptable risk. They are choosing which risks to handle - and refusing to let any of them run loose.

What this means for you

If you are an owner-MD, this article is asking you to swap the existential debate for a workflow conversation. The early wins for you are often the ones that protect your own time - using AI for strategic review, decision support, pressure-testing a board paper, evaluating long reports before a meeting, or building a simple dashboard that pulls your key numbers into one view. Pick one process that matters, put a senior name against it, define what good and bad look like, run a controlled pilot, measure the outcome, and decide from evidence rather than vendor hype or competitor anxiety.

If you are a commercial director, the early wins for you are the ones you can see in your pipeline reports - faster RFI turnaround, better-quality first drafts of technical proposals, sharper prospect research, cleaner CRM data. Start there, model the behaviour, and let your sales engineers see what a controlled workflow looks like before you ask them to change theirs.

If you are an operations director, your early wins are usually in management reporting, document retrieval, scheduling and maintenance-schedule support, interpreting data from your ERP or production reports, and building service-knowledge bases - not in the factory itself. Build the discipline in the office first. Then move it into operations once the team has the judgement to govern it.

Ready to get your AI adoption under control?

Start with our AI for sales and marketing services to build a controlled, measurable pilot in the function where the early wins are easiest to evidence: AI for sales and marketing services . If you have not yet built your sales and marketing strategy, that is the right place to start before any AI rollout - it is what every AI pilot in your commercial function will plug into: sales and marketing strategy .

FAQ

Written by Stefan Buss, founder of Sales & Marketing Engineers . With a background in industrial engineering, having run an exhibition production company for over 7 years and 8 years of agency experience, Stefan brings an engineer's precision to AI adoption decisions.

References

The AEO and GEO Hype: Why Good SEO Still Wins

Tue, 05 May 2026 17:38:25 GMT

7 Search Myths around AEO and GEO and how relevant SEO is still

Every week I see a new acronym and a new agency pitch. AEO. GEO. LLMO and see them over hyped. The message is always the same - SEO is dead, AI has changed everything, and if you're not paying someone to optimise for ChatGPT you're already behind. Meanwhile, most SMEs are sitting on websites that don't rank, don't convert, and don't have the basics right. The hype is dangerous because it pulls budget away from what actually works.

The reality is straightforward. Traditional SEO, done well, is still the single biggest driver of qualified traffic for small and medium businesses. Google itself has said there are no special optimisations required to appear in AI Overviews or AI Mode - the same SEO fundamentals apply . We have seen websites grow traffic by over 10x in recent months simply by getting the basics right - fresh content, real expertise on the page, and proper technical foundations. AI search is real and it's growing fast, but it's still under 1% of referral traffic for most businesses . The question is not "SEO or AEO." The question is how to build one modern search programme that serves both.

With this article I wanted to cut through the myths, showing what the research actually says, and gives you a clear view of where to focus your budget over the next twelve months.

Strategic Briefing (In short)

SEO has always been changing - The shift to AI is just the latest in a 15-year chain of algorithm updates, not a sudden rupture.

SEO is not dead - Traditional organic search still drives the vast majority of qualified website traffic for SMEs and it is not falling off a cliff

AEO and GEO are mostly SEO with AI rebranded - The same fundamentals apply, with small adjustments for how AI systems extract and cite content.

AI search is small but high-intent - AI referrals convert 42% better than non-AI traffic , so you need to be visible even if volume is low.

Show real Experience, Expertise, Authority and Trust - Optimising your content to demonstrate genuine experience, expertise, authority and trust is now far more important than chasing keywords. This is what actually delivers 10x traffic gains.

One programme, many surfaces - Build a single modern search programme that makes your content rank, get cited by AI, and show up everywhere your buyers search.

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approach over the next 12 months.

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The Three-Letter Problem: What SEO, AEO and GEO Actually Mean

Before we dismantle the myths, let's get the definitions straight. There's a lot of deliberate confusion in the market because confusion sells services.

SEO (Search Engine Optimisation) is the discipline of getting your website to rank in traditional search results - Google, Bing, and other search engines. It covers technical health, content quality, user experience, and authority signals.

AEO (Answer Engine Optimisation) is the newer term for optimising content so it appears as the answer in AI-generated responses - Google's AI Overviews, Bing's Copilot, Perplexity, and others. In practice, it overlaps almost entirely with good SEO.

What is Answer Engine Optimisation (AEO)?

AEO is the practice of optimising content to appear as the direct answer in AI-generated search results, including Google's AI Overviews, Bing Copilot, and Perplexity. It focuses on clear question-and-answer structure, factual accuracy, and content that AI systems can easily extract and cite. In practice, AEO overlaps heavily with good SEO fundamentals.

GEO (Generative Engine Optimisation) is optimising for generative AI platforms like ChatGPT, Claude, and Gemini - where your content might be cited or referenced in a direct answer. This is where the most genuine differences emerge, but even here, the foundations are still standard SEO.

What is Generative Engine Optimisation (GEO)?

GEO is optimising content to be cited or referenced by generative AI platforms like ChatGPT, Claude, and Gemini when they answer user questions. Research shows that adding citations, quotations, and statistics to content improves GEO visibility by up to 40%. GEO builds on SEO foundations rather than replacing them.

All three disciplines are trying to answer the same question: how do I make my content visible when someone is searching for what I sell? The answer, in 2026, is largely the same answer it was in 2016 - publish genuinely useful, evidence-backed content on a technically sound website with clear authority signals.

Myth 1: SEO Has Suddenly Changed Because of AI

This myth is everywhere right now. The idea that AI has created some sudden, dramatic rupture in how search works - and that everything we knew about SEO has been thrown out.

Reality: SEO has been changing constantly for over 15 years. AI is just the latest chapter in a very long story of evolution. Look at the Wikipedia timeline of Google Search and you'll see dozens of major algorithm updates that each caused panic at the time - Panda, Penguin, Venice, Pirate, Exact Match Domain, Hummingbird, Pigeon, Mobilegeddon, RankBrain, Possum, Fred, Medic, BERT, Page Experience, MUM, the Helpful Content Update, and countless core updates since .

Each of these triggered the same headlines - "SEO is dead," "everything has changed," "the rules have been rewritten." And each time, the businesses that stayed focused on the fundamentals - good content, technical health, genuine authority - came out ahead. The underlying principle has been remarkably consistent since Panda in 2011: reward genuinely helpful content on trustworthy websites, and downgrade thin, manipulative, or spammy content.

AI Overviews and AI Mode are significant shifts, but they sit on top of that same principle. Google has confirmed the same SEO best practices still apply - there are no new magical optimisations required. What's actually new is the interface people see their answers through, not the fundamentals of what Google rewards.

The View From the Workshop: I've watched this pattern for 20 years. Every major update - BERT in 2019, Mobilegeddon in 2015, Panda in 2011, even Florida way back in 2003 - was declared the end of SEO. What actually happened each time is that lazy tactics stopped working, and well-built sites kept winning. Think of it like a factory floor. Every few years, a new piece of equipment comes in, the rules around safety and efficiency get tightened, and a few businesses still running on twenty-year-old kit fall behind. The well-maintained operations adapt and keep producing. The bar has been raised - and that's actually good news for businesses with real expertise to share.

What are Google AI Overviews?

AI Overviews are AI-generated summary answers that appear at the top of Google search results, powered by Google's Gemini model. Launched in May 2024, they synthesise information from top-ranking pages into a single answer with linked sources. They appear above the traditional blue-link results rather than replacing them, and users can still scroll past to see standard search listings.

Why this myth persists:

"Everything has changed" is a more compelling headline than "things are evolving as they always have." It also justifies panic-buying new services.

Myth 2: AI Search is a Brand-New Way of Searching

The current narrative is that AI has finally made search "intelligent" - that ChatGPT and AI Overviews are doing something completely new by understanding full questions instead of just keywords.

Reality: Google has been building this capability for over a decade. The shift from keyword-matching to genuine language understanding didn't start with ChatGPT - it started in 2012 with the Knowledge Graph, when Google announced the move from "things, not strings". That was the foundation of semantic search - associating words and concepts with each other, so a query about "the leader of the country with the Eiffel Tower" could be understood as a question about France's president, not a string of keywords to match.

In 2013, Google rolled this thinking deeper into the core algorithm with Hummingbird , which optimised search for full questions and conversational queries rather than just keyword matching. Before Hummingbird, long-tail questions often returned poor results because Google was looking for exact keyword matches. After Hummingbird, the search engine could understand intent, context, and the relationships between concepts.

Then came RankBrain in 2015 - Google's first machine-learning ranking system, which interpreted unfamiliar queries by understanding their meaning. Then BERT in 2019, which used transformer-based natural language processing - the same family of models that power ChatGPT and Gemini today. Then MUM in 2021, multimodal and 1,000 times more powerful than BERT.

In other words, "AI search" is not new. Google has been quietly running large language model thinking through search results for years. What's new is that AI is now visible to the user in a chat-style interface, rather than working invisibly behind the blue links. The underlying behaviour - search engines understanding meaning rather than matching keywords - has been the direction of travel for over 13 years.

Why this myth persists:

ChatGPT made AI feel sudden and visible. But the search infrastructure that powers AI Overviews has been quietly evolving since 2012. Businesses that have been writing for meaning and context rather than keyword density already had a head start - they just didn't know what to call it.

What is semantic search?

Semantic search is a method of finding information based on the meaning and intent behind a query, rather than matching exact keywords. Google introduced semantic search in 2012 with the Knowledge Graph, allowing it to understand relationships between concepts, people, places, and things. It is the foundation that makes modern AI-driven search possible.

Myth 3: SEO is Dead - AI Has Killed Search

This is the loudest myth and the most wrong. Every LinkedIn post claiming "SEO is over" is either selling a replacement or hasn't looked at the data.

Reality: Traditional search is still the dominant driver of website traffic by an enormous margin. Similarweb data shows Google Search drove 191 billion referrals in a single month, versus 1.13 billion from all AI platforms combined. BrightEdge's 2025 analysis found AI search accounted for less than 1% of referral traffic, while organic search remained the primary driver for most businesses .

More importantly for SMEs - good SEO still produces dramatic results. We have personally grown our own website traffic at Sales and Marketing Engineers grow by over 10x (round 11x actually) in recent months , driven by nothing more exotic than solid Experience, Expertise, Authority and Trust signals, technically sound pages, and genuinely fresh, useful content. No special AEO tricks. No GEO magic - only minor tweaks. Just the fundamentals, done properly.

This is where E-E-A-T comes in. Google originally introduced this as E-A-T - Expertise, Authoritativeness and Trust - as part of its quality rater guidelines, and then in December 2022 added an extra E for Experience, making it E-E-A-T . The addition of "Experience" was a quiet but significant shift. Google now wants evidence that whoever wrote the content has actually done the thing they're writing about - not just researched it.

From the Field: I had a client convinced their SEO was "finished" because of AI Overviews. We did a technical audit, fixed the parts of the website that were jamming up search engines, added the right structured data, rewrote their cornerstone pages with real first-hand expertise and case studies, and published fresh content weekly against their core commercial keywords. Within four months their organic traffic was up 10x and their enquiries had tripled. SEO isn't dead - neglected SEO is dead.

What is E-E-A-T?

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It's Google's framework for evaluating content quality, originally introduced as E-A-T and updated to E-E-A-T in December 2022 to add an emphasis on first-hand experience. For SMEs this means showing real case studies, named authors with credentials, specific knowledge, and clear company information - the opposite of generic AI-generated filler.

Why this myth persists:

AI feels new and exciting. "SEO is dead" is a more clickable headline than "SEO still works if you do it properly." Agencies selling AEO/GEO services have a commercial incentive to make you think the rulebook has changed. It mostly hasn't.

Myth 4: AEO and GEO Need Separate Budgets and Specialists

This is where the hype becomes expensive. Multiple agencies now sell AEO and GEO as separate services on top of SEO, often for considerable monthly fees.

Reality: Google has stated directly that no additional requirements or special optimisations are needed to appear in AI Overviews or AI Mode - standard SEO best practices still apply . To be cited by an AI system, your page still needs to be crawled, indexed, and eligible to appear in normal search results. If the SEO foundations are broken, no amount of AEO optimisation will help.

The academic research backs this up. The foundational GEO paper found that what actually improves visibility in generative engines is adding citations, quotations, and statistics to your content - boosting visibility by up to 40% - while keyword stuffing did almost nothing. That's not a new discipline. That's just writing better, more evidence-backed content, which is what good SEO has always rewarded.

Why this myth persists:

Specialists sell specialisms. If an agency can convince you that AEO is a separate field requiring separate expertise, they can sell you a separate retainer. For the vast majority of SMEs, the right answer is one modern SEO programme that happens to include AI considerations - not three separate services stacked on top of each other.

Myth 5: You Need AI Optimisation Before Fixing Your SEO

This is the mistake that costs SMEs the most money. They see the AI hype, panic, and start paying for advanced optimisation before the basics are in place.

Reality: AI systems cannot cite a page that isn't indexed. They cannot extract an answer from content that doesn't exist. They cannot trust a domain with no authority signals. Getting the foundations right is not optional - it's a prerequisite.

For most SMEs, the practical priority list looks like this:

Fix technical SEO - site speed, crawlability, indexation, mobile
Add structured data (schema) so search engines and AI know what each page is
Build out cornerstone pages for each core service
Establish E-E-A-T through named authors, case studies, and credentials
Publish fresh, evidence-backed content with real references
Develop content rich in the topics, products and concepts you want to be known for
Earn genuine backlinks through expertise and PR
Make content answer-ready with clear structure

What is schema markup?

Schema markup is structured code added to a website that tells search engines and AI systems exactly what your content means - for example, identifying a page as a product, article, FAQ, review, or local business. It helps search engines display rich results, improves how AI systems extract and cite your content, and is a key technical layer for both traditional SEO and AEO.

Only once those are in place does it make sense to think about finer AI-specific optimisations - which in practice mostly means making your content easier for AI systems to extract and cite. That's a small adjustment on top of good SEO, not a separate programme.

The View From the Workshop: Think of it like commissioning a production line. You wouldn't bolt advanced automation onto a line where the conveyor keeps jamming and the inspection station is broken. You'd fix the foundations first, then optimise. SEO works the same way. Get the base system reliable, then refine for the new channels on top.

Not sure if your SEO foundations are strong enough to support AI search visibility?

Book a free SEO audit and we'll stress-test your current setup - technical health, content quality, structured data, expertise signals, and AI readiness. You'll get a clear priority list of what's working, what's broken, and what to fix first.

Myth 6: If You Appear in AI Overviews You'll Lose All Your Traffic

This is the "zero-click" panic. The idea that AI Overviews will answer everything and nobody will ever click through to your site again.

Reality: The data shows a more nuanced picture. Pew Research found users clicked a traditional result in 8% of searches where an AI summary appeared, compared to 15% when no AI summary appeared - so clicks do drop, but they don't disappear . Meanwhile, only about 1% of users click a link inside the AI summary itself.

That sounds bad until you look at the other side. Adobe's Q2 2026 data shows AI-referred visitors converted 42% better than non-AI traffic in March 2026 . AI traffic to US retail sites grew 393% year on year in Q1 2026 . For B2B specifically, 51% of software buyers now start research with an AI chatbot more often than Google, and 69% chose a different vendor than initially planned because it appeared in chatbot recommendations .

In plain terms: AI search is a smaller volume channel, but it delivers higher-intent buyers. You lose some informational traffic. You gain some commercial traffic. For most SMEs, that trade is favourable - you don't really want someone idly researching the basics clicking through, you want the ready-to-buy customer who already knows what they need.

Why this myth persists:

Headline-grabbing stats get shared without context. "Clicks down X%" spreads faster than "but the clicks you're getting are better quality."

Myth 7: You Can't Measure AI Visibility

A common objection from sceptical Commercial Directors - "if I can't measure it, I can't justify budget for it."

Reality: AI visibility is measurable, though the tooling is still catching up. Google Search Console now reports AI-feature performance inside normal web search data. Bing has gone further and launched an AI Performance dashboard in Webmaster Tools that reports total citations, average cited pages, grounding queries, and page-level citation activity . Third-party tools now track how often your brand is mentioned in ChatGPT, Perplexity, and Claude responses.

The bigger measurement shift is that "ranking" and "clicks" are no longer the only KPIs that matter. Citation frequency, brand mentions in AI responses, and conversion quality of AI-referred traffic are all becoming standard metrics. For SMEs building their 2026 reporting stack, these need to sit alongside - not instead of - traditional SEO metrics.

Why this myth persists:

Many marketing dashboards were built around ranking and clicks. Updating them takes time, and some agencies haven't bothered. That's a reporting problem, not a visibility problem.

The New SEO: Search Everywhere Optimisation

Here's where the conversation gets useful. Instead of inventing yet more acronyms, a small group of practitioners are quietly redefining what the original SEO acronym actually stands for.

The phrase "Search Everywhere Optimisation" was coined by Ashley Liddell, founder of Deviation, who trademarked " Search Everywhere " in 2023 and has been refining the framework ever since. It has since been adopted by Rand Fishkin at SparkToro and gained wider traction across the SEO community as a sensible alternative to the AEO/GEO/LLMO acronym soup.

What is Search Everywhere Optimisation?

Search Everywhere Optimisation is the practice of building visibility across every platform where your audience searches and researches - including Google, AI tools like ChatGPT and Perplexity, social platforms like LinkedIn, YouTube and TikTok, marketplaces like Amazon, and community sites like Reddit. The acronym SEO stays the same; the scope expands from "Search Engine" to "Search Everywhere." The term was coined by Ashley Liddell, founder of Deviation.

The logic is simple. Search is no longer something that only happens on Google. People search inside LinkedIn for technical experts, inside YouTube for "how it works" videos, inside Reddit for honest opinions, inside ChatGPT for synthesised answers, and yes - still inside Google for most things. Google still holds roughly 70% of all desktop search activity when non-traditional platforms are counted, but that means around 30% is happening elsewhere. For SMEs, the "elsewhere" includes LinkedIn (where many B2B decisions get researched), YouTube (where buyers go for explanations), and increasingly AI platforms.

The Search Everywhere mindset doesn't mean trying to be on every platform. It means recognising that visibility now has to be built where your specific buyers actually look - and for most SMEs that's still mainly Google, with LinkedIn, YouTube, and AI platforms as the secondary surfaces that matter. The acronym stays the same. The scope expands.

What SMEs Should Actually Focus On

Stripping out the hype, here's what the evidence actually tells SMEs to do in 2026.

Get the SEO foundations right first

No page rank equals no AI citation. This is the non-negotiable base layer - technical SEO, indexation, page speed, mobile experience, and clean site structure. If these are broken, everything else is wasted effort. Like running a workshop with the doors locked - it doesn't matter how good the work inside is if nobody can get in.

Double down on real expertise

Show your Experience, Expertise, Authority and Trust on every page. That means named authors with real credentials, genuine case studies, specific knowledge from people who've actually done the work, and clear company information. Google's own guidelines now explicitly reward "first-hand experience" as part of E-E-A-T . For most SMEs this is a hidden advantage - you have real expertise that big content factories simply can't fake.

Build content rich in the topics that matter to your buyers

Modern search is increasingly about meaning, not keywords. That means your content needs to clearly cover the topics, products, processes, and people that connect your business to what your buyers are actually searching for. Adding structured data (schema) on top tells search engines and AI systems exactly what each page represents. Together, these are a huge part of what's keeping SEO so effective right now.

Publish fresh, evidence-backed content

The GEO research is clear - citations, quotations, and statistics significantly improve source visibility . That's also what humans trust. The traditional informative blog written without references is becoming noticeably less effective in the AEO era. Evidence-led content, properly referenced, is now the baseline.

Use AI to deepen research, not replace thinking

AI has dramatically expanded what's possible in content research. You can now analyse competitor content at scale, surface supporting evidence, and stress-test arguments far more thoroughly than was practical before. Used properly, AI makes your content better - more rigorous, better researched, more useful. Used lazily to churn out generic filler, it makes your content worse. Think of AI as a power tool - in skilled hands it accelerates great work; in unskilled hands it just produces bad work faster.

Make content answer-ready

Use clear structure. Include direct answers to specific questions. Use headings that match real search queries. Add FAQ sections and structured data. This helps both AI extraction and human scanning.

Think Search Everywhere, not just Google

For most SMEs, Google is still where the bulk of buyer research happens - but LinkedIn, YouTube, and AI platforms are growing parts of the picture. Make sure your expertise is visible where your buyers actually look.

Build measurement across both traditional and AI search

Update your reporting stack. Track organic rankings, clicks, conversions from organic, brand mentions in AI platforms, and citation activity. Don't let "we can't measure it" become an excuse for not showing up.

The 12-Month Plan: Where to Invest Your Budget

For a Commercial Director or business owner looking at the next twelve months, here's the sensible allocation based on what the data shows.

First 90 days - Foundations: Technical SEO audit, fix crawlability and indexation issues, improve site speed, implement structured data, audit existing content for E-E-A-T signals. This is where the 10x traffic gains usually come from for SMEs, because the baseline is often poor.

Days 90-180 - Content and Authority: Build out cornerstone pages for each core service, publish fresh evidence-based content weekly, develop topic-rich content clusters, add named authors and case studies, earn backlinks through PR and thought leadership. This compounds over time - like interest on a deposit, the longer it runs the more it pays back.

Days 180-365 - Refinement and Search Everywhere Readiness: Structure content for answer extraction, expand structured data coverage, monitor AI citations, extend visibility into LinkedIn and AI platforms where your buyers research, and update reporting to include AI visibility metrics. Treat this as a layer on top of solid SEO, not a replacement for it.

Throughout the whole twelve months, the single most important thing is consistency. SEO compounds. AI visibility builds on SEO foundations. The SMEs winning in 2026 are the ones who started properly in 2024 and kept going.

Closing

The acronyms will keep changing. LLMO will replace GEO, and something else will replace LLMO. What won't change is the underlying principle - search, in all its forms, rewards genuine expertise published on technically sound websites and made visible wherever your buyers look. Your business has real expertise. The job is to engineer your website and content to expose that expertise to search engines, AI systems, and the wider Search Everywhere universe, consistently, over time.

What This Means for You

If you're a Commercial Director watching your pipeline metrics, the practical takeaway is this - don't let the AEO/GEO hype pull budget away from the SEO fundamentals that are still driving the bulk of your organic enquiries. The 10x traffic gains are hiding in plain sight, in the technical SEO, structured data, content quality, and expertise signals you may have been neglecting while worrying about AI.

If you're a business owner looking at ROI, the good news is that doing SEO properly in 2026 also sets you up for AI visibility and Search Everywhere visibility. You don't need to pick between three programmes. You need one modern programme that handles all three - with clear reporting so you can see what's working. See our SEO services for how we approach this.

Ready to Stress-Test Your SEO?

Ready to find out where your website actually stands? Book a free SEO audit and we'll review your technical foundations, structured data, content quality, expertise signals, and AI readiness. You'll leave with a clear priority list and a realistic view of what good SEO can deliver for your pipeline. Book your SEO audit .

FAQ

Written by Stefan Buss , founder of Sales & Marketing Engineers. Stefan is a qualified industrial engineer with over 20 years in the creative industry, including hands-on SEO, PPC and analytics experience delivering 10x traffic gains for SMEs.

References

Wikipedia - Timeline of Google Search covering all major algorithm updates including Panda, Penguin, Hummingbird, RankBrain, BERT, MUM, Helpful Content, AI Overviews and AI Mode
Google - Introducing the Knowledge Graph : things, not strings, the foundation of semantic search announced in 2012
Wikipedia - Google Hummingbird , the 2013 algorithm update that brought semantic search into the core ranking
Google Search Central - Quality rater guidelines update adding the Experience E to make E-E-A-T, December 2022
Google Search Central - No additional requirements or special optimisations needed for AI Overviews or AI Mode
Semrush with Ashley Liddell of Deviation - Search Everywhere Optimisation framework
BrightEdge - AI search accounted for less than 1% of referral traffic in Jan-Aug 2025
Similarweb - AI platforms drove 1.13 billion referrals vs 191 billion from Google Search in June 2025
Pew Research Center - Clicks on traditional results dropped from 15% to 8% when AI summaries appeared
Adobe Digital Insights - AI-referred visitors converted 42% better than non-AI traffic in March 2026
Adobe - AI traffic to US retail sites grew 393% year over year in Q1 2026
Aggarwal et al ., GEO paper - Citations, quotations and statistics boosted source visibility by up to 40%
Microsoft Bing Webmaster Tools - AI Performance dashboard reporting citations and grounding queries
G2 - 51% of B2B software buyers start research with an AI chatbot more often than Google

Apple Turns 50: What Steve Jobs Taught Us About Product Marketing Strategy

Tue, 07 Apr 2026 08:46:37 GMT

He Didn't Just Sell Products - He Engineered a Buying System

I've studied a lot of marketers over the years. None of them made me rethink everything quite like Steve Jobs did.

Not as a tech founder - but as a marketing mind operating at a completely different frequency. As Apple's 50th anniversary came around recently, it felt like exactly the right moment to take stock of what made him so extraordinary. And more importantly, what the rest of us can actually learn from it.

Because the thing that strikes me most about Jobs isn't any single product or campaign. It's the strategic architecture underneath all of it. Every move was connected. Every decision was made with the end-user journey in mind, long before the phrase "customer journey" became a marketing cliché.

This is what genuine product marketing looks like. Not just promoting what you've built - but adapting what you build, how you price it, and how you sequence it, so that the right people enter your world and stay there.

Quick Links

The iMac: Saving a Company Through Design and Positioning

When Jobs returned to Apple in 1997, the company was haemorrhaging money - losses of $878 million that year alone . His first major move wasn't a campaign. It was a product decision.

He stripped the product line back to four computers, redesigned the consumer desktop from scratch, and launched the iMac in 1998. Encased in translucent Bondi Blue plastic, it looked like nothing else on the market. It sold 278,000 units in its first six weeks, and 800,000 before the year was out - making it the fastest-selling computer in Apple's history at that point. Nearly half of those buyers were first-time computer owners .

That's product marketing. Not a logo refresh. Not a social media campaign. A product repositioned for a new audience, launched with theatrical precision, and designed to turn Apple's losses into a $414 million profit within twelve months.

But Jobs didn't stop there. He knew the iMac alone wouldn't pull Apple into mass-market dominance. For that, he needed a different kind of hook.

The iPod: Not Just a Product. A Pipeline Strategy.

Here's the thinking that separates a tactician from a strategist.

By the late 1990s, Jobs had seen the iMac stabilise Apple's finances. But Apple still had a fraction of the PC market. Competing head-to-head with Dell, HP and Compaq on their terms wasn't going to work. He needed Apple to own a new category entirely - and pull a new audience into the ecosystem from the side door.

Enter the iPod, launched in October 2001.

The iPod wasn't the first MP3 player on the market. But it was the first one that was genuinely simple to use, beautifully designed, and - crucially - locked into Apple's own software ecosystem. At a price point significantly lower than a Mac, it gave millions of people their first taste of the Apple experience .

By 2005, Apple held over 70% of the entire digital music player market, and analysts noted clearly that iPod users were far more likely to go on and purchase other Apple products - Mac computers included. This became known as the "halo effect." Jobs had designed it that way.

That's not product design. That's a pipeline strategy dressed up as a music player.

More than 100,000 iPods were sold before the end of 2001, just weeks after launch. By April 2007, Apple announced it had sold its 100 millionth iPod - making it the best-selling digital music player of all time.

The engineering parallel here is worth spelling out. Think of the iMac as your core product - your manufacturing capability, your main offer. The iPod was the entry point, the lower-cost, lower-barrier product that brought people into the system before they were ready to commit to the bigger buy. Every technical SME should be asking themselves: what's our iPod?

iTunes: Rescuing an Industry by Redesigning the Product

The music industry had a piracy problem. Napster and LimeWire had made free music normal. Labels were panicking and suing teenagers. Nobody had solved it.

Jobs solved it by redesigning the product itself.

He went to the labels, negotiated rights, and built iTunes - a closed, encrypted platform that sold individual tracks for 99 cents. Not albums. Singles. He effectively reintroduced a format the industry had abandoned, and made it work because the friction of using it was lower than the friction of piracy for most people.

The numbers speak for themselves. In its first week alone, the iTunes Music Store sold over one million songs - over half of them as complete albums, disproving fears that individual track sales would kill the album format entirely.

By July 2004, iTunes had sold its 100 millionth song - just over a year after opening its doors.

By June 2008, the iTunes Store had surpassed 5 billion downloads and become the world's largest music retailer .

This is product marketing at its most consequential. Jobs didn't market his way around a failing industry - he adapted the product to fit both the audience's behaviour and the commercial reality. The result was an entirely new digital economy that we still live inside today.

The Product Launch as a Marketing Format

Jobs didn't just create products. He created a new way of introducing them to the world.

The live keynote - theatrical, precise, stripped of unnecessary complexity - became its own marketing instrument. No feature stickers. No spec sheets read aloud. Just a carefully constructed story, ending with a reveal designed to create a moment.

Every tech company in the world now mimics this format. Apple invented it as a brand asset.

What Jobs understood was that the launch event is part of the product experience. The anticipation, the theatre, the communal watching - all of it built the sense that owning an Apple product meant belonging to something. That's not a campaign mechanic. That's brand architecture.

The Developer Community: Seeding Adoption from the Inside Out

Before the general public got anywhere near a new Apple product, Jobs had already cultivated the people who would build value on top of it.

Developers were brought into beta releases early, treated with care, given the tools they needed, and made to feel central to the Apple mission. This was deliberate. By the time a product launched publicly, a community of creators had already built the ecosystem that made the product worth having.

It's the same principle that drives any successful platform strategy: build the supply before you stimulate the demand. Too many businesses do it the other way around.

The Apple Store: Experience as the Sales Strategy

When Apple opened its first retail stores in May 2001, the received wisdom was that it would fail. Gateway had tried the same thing and pulled out.

Jobs designed the Apple Store around a different premise entirely. The store wasn't there to sell. It was there to let people experience.

No price tags on the products. No pushy floor staff. No feature stickers. Just beautifully lit demos in open space, trained staff whose job was to answer questions and let the product do the work. It felt more like a gallery than a shop.

This is what the best sales environments do - they remove the barriers to the decision rather than pressure people into it. The store was a physical expression of Apple's value proposition: simplicity, quality, trust.

Pixar: The Art of Repositioning

Jobs's strategic instincts weren't limited to Apple.

When he acquired Pixar in 1986, it was a struggling hardware company. Jobs saw something different - a storytelling studio with the potential to redefine animation, if the technology could catch up with the vision.

He kept funding it through years of losses, repositioned it entirely around long-form narrative, and in 1995 Toy Story became the first fully computer-animated feature film - and a cultural phenomenon. He later sold Pixar to Disney for $7.4 billion.

The lesson isn't just about patience. It's about the willingness to redefine what a business is for, rather than optimising what it already does. That's product marketing in its purest form.

Design as a Conversion Tool

Jobs's obsession with minimalism wasn't aesthetic vanity. It was commercial logic.

He understood that every unnecessary element in an interface - every button, every menu, every step in a process - is a point where a user can disengage. Friction kills adoption. Friction kills conversion. The job of design was to remove every obstacle between the customer and the outcome they wanted.

This thinking runs through everything from the iPod click wheel to the one-button iPhone. Simplicity wasn't easier to build - it was harder. But it was worth it because it scaled.

For any business designing a sales process, a website, or a customer journey, this is the principle that matters most. The fewer the steps between interest and conversion, the more of your pipeline survives. See how we engineer this thinking into our clients' marketing systems at https://www.salesandmarketingengineers.co.uk/sales-and-marketing-strategy

The Caveat That Matters

I've spent several hundred words examining what made Jobs extraordinary as a marketer and strategist.

But the paradox of genius is real, and it would be dishonest not to name it.

His list of failures as a human being is as long as his list of achievements as a marketer. The denial of family. The manipulation. The way he managed people. The lifting of others' ideas without credit. He was not a role model for leadership or management - quite the opposite.

I think you can learn from someone without worshipping them. Take the marketing brilliance. Study the vision. Leave the rest.

And it's also worth noting that the era after Jobs tells its own story. Under Tim Cook, Apple has been enormously successful at executing on the ecosystem Jobs built. But it has struggled to define new categories, and has found itself meaningfully behind in the AI race - a gap that carries real strategic risk.

Dominance, it turns out, requires constant reinvention. Jobs knew that. The question for any business - and for Apple now - is who's doing that thinking next.

What This Means for Your Business

You're not Apple. But the underlying principles Jobs operated by aren't reserved for trillion-dollar companies.

Every business needs to think about product marketing - not just promotion. How is your product or service designed to be sold? What's your iPod - the lower-barrier entry point that pulls people into your world? Where is friction costing you sales? How does your audience experience your business before they buy?

These are strategy questions, not campaign questions. And they're the questions we work through with engineering and technical businesses every day.

If you want to start at the beginning - as Jobs always insisted on doing - take a look at how we approach sales and marketing strategy here .

And if you want to go deeper on the full Jobs story, the biography by Walter Isaacson is genuinely one of the best business reads out there. The contradictions alone are worth it.

FAQ

Stefan Buss - Sales & Marketing Engineers
We help engineering and technical SMEs design joined-up sales and marketing systems that deliver consistent, measurable growth. Simple, strategic, systemised and scalable.

Backup and Transferring your AI Data

Tue, 10 Mar 2026 18:46:34 GMT

AI Data Risk: Why You Should Never Store Everything in One Platform

Your AI Platform is Not a Storage Vault - Stop Treating It Like One

There's a pattern I see with businesses and myself when getting serious about AI. It always goes the same way.

You start with a few prompts. A quick draft here, a bit of research there. Then you discover custom instructions, projects, uploaded documents. You refine your prompts. You build workflows. You start generating real outputs - proposals, content, process documents, competitive analysis.

The prompts and outputs and ideas scale as quickly as the technology itself. And before you know it, you have notes, prompts, templates, system instructions and half-finished workflows everywhere - and nowhere - except buried inside your AI platform's chat history.

That's not a system. That's a single point of failure.

This article explains why treating your AI platform like a storage vault is a risk your business can't afford - and gives you a practical framework and suggested tools for keeping your AI knowledge safe, organised and portable.

Quick Links

The Risks You're Not Thinking About

Right now, there's a wave of users migrating between AI platforms. People are switching from ChatGPT to Claude for political reasons given the recent stand off with the Department of War. Gemini is growing fast - its web traffic share jumped from 5.7% to 21.5% in just 12 months . New tools are appearing constantly. And every time someone switches, the question is the same: what happens to everything I built inside the old platform?

It's a question worth asking, because the risks are bigger than most people realise.

Platform viability

The AI market is young, volatile and burning cash at a staggering rate. OpenAI's own internal forecasts predict a $14 billion loss in 2026 - roughly three times worse than 2025. Financial analysts have warned the company could run out of cash by mid-2027 . They've started introducing ads into ChatGPT - something the CEO once called a "last resort."

This isn't just an OpenAI story. The entire AI sector is navigating enormous infrastructure costs, uncertain revenue models and intense competition. Platforms can pivot, restructure, merge or fold. None of them are guaranteed to exist in their current form in three years' time.

Infrastructure and cyber risk

We live in a volatile world. Political instability, financial uncertainty and an escalating cyber threat landscape all play a part. The UK's National Cyber Security Centre handled 204 nationally significant cyber incidents in 2025 - more than double the previous year. High-profile outages at AWS, Cloudflare and Microsoft Azure disrupted millions of users globally at the end of 2025. Data centres are not immune to cyberattacks, power failures or physical events - and the AI platforms you depend on sit inside those same data centres.

Terms of service and vendor lock-in

Platforms can change how they handle your data at any point. Privacy policies shift. Features get removed. Pricing restructures overnight. The more you build inside one platform - your prompts, your projects, your documents, your workflows - the harder and more painful it becomes to move. Or to work across two or three platforms, which is increasingly what serious AI users need to do.

You wouldn't store your only copy of production drawings inside a single machine on the shop floor. If that machine goes down, you've lost the lot.

Your AI platform is no different.

FAQ

Memory vs Context - A Quick Clarification

There's a common misunderstanding here that's worth clearing up quickly.

Most people assume their AI platform "remembers" everything from previous conversations. It doesn't. AI platforms have two separate systems: memory and context. Memory is a small set of personalisation notes stored between sessions - your name, your preferences, maybe your role. It's limited and selective. Context is the much larger window of information the AI can work with during a single conversation - the documents, instructions and data you provide in that session.

The practical point is this: to get the most from the context window - which is where the real power sits - you need well-organised, up-to-date knowledge files that you can bring in and out of sessions and update as your business evolves. System instructions, prompt libraries, brand guides, process documents - all stored outside the platform, ready to be loaded when needed.

Your AI memory is not a substitute for proper knowledge management. We'll cover how to retrieve, influence and manage your AI memory settings in more detail in a future article. For now, know that your memory data can be exported too - and the download guide below covers how.

Tools for Organising Your AI Knowledge

Once your AI use ramps up, you need a proper system for storing everything it produces. Here are three approaches, depending on how far along you are.

Manual copy and paste

The simplest option. After any significant AI session, copy your key outputs, refined prompts and useful responses into your existing filing system. It costs nothing and it works. The key is building the habit - treat it like saving your work on any other tool.

Platform connectors and integrations

Most AI platforms now offer integrations with cloud storage and productivity tools. Connect your AI workspace to Google Drive, OneDrive, SharePoint or Dropbox so that project files and outputs sync where possible. Check your platform settings for available connectors - these vary by plan and provider. Some platforms also allow two-way syncing, meaning changes in your cloud storage can feed back into your AI projects.

Linked note-taking systems

For anyone whose AI use has moved beyond casual prompting, dedicated note-taking platforms offer something flat file structures like Google Docs or Microsoft Office simply can't - multi-dimensional linking between ideas, notes, prompts and outputs.

Two platforms stand out:

Obsidian - a local-first knowledge base that stores everything as plain markdown files on your own machine. You own your data completely. Notes link to each other, creating a web of connected knowledge rather than a stack of isolated documents. Strong for individual users and anyone who values data ownership.

Notion - a cloud-based workspace that combines documents, databases, wikis and project management. Excellent collaboration features and a solid option for teams who need shared access to AI knowledge.

Both platforms let you build a connected knowledge system where your prompts link to your outputs, which link to your process documents, which link to your system instructions. It's the difference between a filing cabinet and a wiring diagram - everything connects, and you can trace the thread from one idea to another.

This kind of structure is particularly valuable when you start working across two or three AI platforms - which, as your usage matures, you almost certainly will. Having your knowledge stored centrally means you can load the right context into whichever platform you're using for that particular task.

A Suggested Structure for Your AI Knowledge Base

Whether you use Obsidian, Notion or another tool, here's a folder and tag structure to get you started.

Core folders

Prompts - master prompts, templates, tested and refined prompts
Outputs - key AI-generated content worth keeping
Workflows - step-by-step AI processes for repeatable tasks
System Instructions - custom instructions, project briefs, personas, style guides
SOPs - standard operating procedures that involve AI steps
Ideas and Research - raw thinking, AI-assisted brainstorms, research outputs

Tagging system

By platform: #chatgpt #claude #gemini
By status: #draft #tested #approved #archived
By use case: #content #sales #proposals #strategy #operations
By project or client name

The tags let you search across folders. The folders give you structure. The links between notes give you context. Together, it's a proper system - not a graveyard of forgotten chat threads.

FAQ

Backing Up and Transferring Your AI Data

This is the practical bit most people skip entirely. And it matters, because no AI platform currently offers a regular, automatic backup system for your work. There is no "set it and forget it" option. You need to build your own backup routine.

Every major AI platform lets you export your data - conversation history, custom instructions, uploaded files and memory settings. The problem is that the process is different for each platform, the settings are buried in menus, and most people don't know what to ask for or how to find it.

Here's what you should be backing up regularly:

Conversation history - or at minimum, the conversations containing valuable outputs
Custom instructions and system prompts
Project files and uploaded documents
Memory data - the personalisation notes your platform has stored about you
Any custom GPTs, Gems or Projects you've built

You should also know how to transfer this information between platforms. If you switch from ChatGPT to Claude, or add Gemini alongside your existing tools, you want to be able to rebuild your setup quickly rather than starting from scratch. And if you're working across multiple platforms - which serious users increasingly do - having a central knowledge store means you're never locked in and never exposed.

We're putting together a practical download guide that covers exactly this - platform by platform backup instructions, the right settings to check, the prompts you need to retrieve and export your data, and how to manage your memory settings across ChatGPT, Claude and Gemini.

Download: The AI Backup and Transfer Guide [Link to the Guide attached for now]

FAQ

What This Means for Your Business

If you're an MD or operations director reading this, none of this is about being anti-AI. It's about applying the same operational discipline to your AI tools that you already apply to your production systems.

You'd never run your manufacturing process on a single supplier with no backup plan. You'd never store your only copy of a critical specification inside a machine you don't own. You'd never build a production line around equipment that might not be available next year.

Apply the same thinking here.

The businesses getting the most from AI are the ones treating it as a tool within their system - not the system itself. They back up their work. They organise their knowledge. They can switch platforms, work across multiple tools, or rebuild their setup in a day if they need to. That's not paranoia - it's basic operational discipline applied to a new technology.

Summing up

Your AI platform is a workshop tool, not a filing cabinet. Use it hard, get value from it, build your skills and your workflows - but keep your knowledge, your data and your intellectual property where you control it.

Back up. Organise. Secure. And build your own system around the AI, not inside it.

A well-maintained system doesn't rely on a single component - it's designed so that no one part can bring the whole thing down.

Ready to build AI into your business the right way? Book a free strategy audit and we'll help you systemise your AI adoption with the right guardrails in place.

Want Help with AI? We have training, mentoring, strategy and do if for you services - see more here... SME AI Services

Written by Stefan Buss , founder of Sales & Marketing Engineers. With a background in industrial engineering and a practical approach to AI adoption, Stefan helps technical SMEs build systems they can control and trust.

The Ultimate LinkedIn Posting Guide for 2026

Mon, 02 Mar 2026 21:39:54 GMT

What's Changed, What Works, and What to Do About It

I've been using LinkedIn since I moved to the UK in 2010. It's been my networking backbone, my learning platform, and the source of more business conversions than I can count. It connected me with people I'd never have met otherwise, taught me things about my industry I didn't know I needed to learn, and opened doors that cold calling never could. But something has shifted. Posts that once reached thousands are now reaching hundreds - and if you've noticed the same thing, you're not imagining it.

LinkedIn has fundamentally changed how it decides who sees your content. The reach has dropped. The rules are different. And most B2B companies - particularly in engineering and technical sectors - haven't adjusted their approach. This article breaks down exactly what's happened, what the research says is working now, and gives you a practical playbook for both personal profiles and company pages in 2026.

Quick Takeaways - In Plain English

Before we get into the detail, here's what you need to know right now:

Your LinkedIn reach has probably dropped by around 50%. That's normal. It's happened to almost everyone.
The algorithm has shifted from counting followers and likes to reading your content and deciding if it's genuinely relevant to the people seeing it.
Saves are now the most valuable engagement signal - roughly 5 times more powerful than a like.
Personal profiles outperform company pages by over 500% in reach. People trust people, not logos.
Company page organic content now makes up just 1-2% of the LinkedIn feed. Down from 7% in 2021.
Carousels, frameworks, and document posts are the top-performing formats.
LinkedIn is still the most powerful B2B platform on the planet - 80% of all B2B social leads come from it.
This is no longer just a marketing task. Subject matter experts, directors, and team leaders need to be the ones posting.
Posting 2-5 times per week is the sweet spot. Quality over quantity, every time.

Quick Links

The Paradox: More Powerful Than Ever, Less Reach Than Before

Here's the contradiction that catches most people off guard.

LinkedIn is said to generates most of all B2B leads from social media, outperforming Facebook, X, and Instagram combined. Research found LinkedIn to be 277% more effective for B2B lead generation than Facebook and Twitter. LinkedIn ads deliver conversion rates up to 2 times higher than other platforms and a 28% lower cost-per-lead compared to Google AdWords40% of B2B marketers say it's the most effective channel for generating high-quality leads, and 85% claim it delivers the best value among all social media platforms. 89% of B2B marketers use LinkedIn for lead generation, and 62% say it produces leads for them effectively.

And yet, organic reach has dropped by approximately 50% year-over-year for the vast majority of users. AuthoredUp's tracking of over 621,000 posts found that 98% of users experienced a reach decline. Median impressions fell from 1,211 per post in June 2024 to 636 per post by May 2025 - a 47% drop .

So the platform is more valuable than ever for B2B - but fewer people are seeing your content. That sounds like a problem. It is a problem if you don't understand why. But once you do, it's actually an opportunity.

The reason is simple: LinkedIn changed how its algorithm works. And that change favours the kind of content most B2B companies should have been creating all along.

What Actually Changed - The Algorithm Shift in Plain English

LinkedIn's algorithm has evolved through three distinct phases. First, it was follower-driven - the more connections you had, the more people saw your posts. Then it shifted to engagement-driven - posts that got quick reactions got pushed further. Now, it's moved to something fundamentally different: relevance-driven distribution.

The technical name behind this shift is 360Brew. It's a 150-billion-parameter AI model developed by LinkedIn's Foundation AI Technologies team, described in a research paper published in January 2025. The paper was later withdrawn for licensing reasons, not quality concerns, and LinkedIn hasn't officially confirmed full deployment. But the direction of travel is well documented and the effects are visible everywhere.

Here's what matters in practical terms. The old algorithm tracked metadata - clicks, hashtags, connection graphs, post timestamps. The new system reads your content semantically. It understands what your post is actually about. It cross-references your post content against your profile - your headline, your About section, your experience history - to evaluate whether you have genuine expertise in what you're talking about. It analyses the viewer's recent engagement history to generate personalised relevance scores. And it evaluates whether comments on your posts are substantive or performative.

That last point is important. It's not just counting engagement anymore. It's assessing the quality of that engagement.

It's also worth noting that this isn't about followers being irrelevant. They still matter - but in context. A well-connected profile with genuine relationships in a defined niche will outperform a larger, more scattered network. The algorithm rewards relationship density, not audience size.

FAQ

What LinkedIn Rewards Now - The New Engagement Hierarchy

The engagement signals that drive distribution have been completely reshuffled. Based on converging data from Richard van der Blom's Algorithm InSights 2025 report analysing 1.8 million posts across 60+ countries, AuthoredUp's analysis of 3 million+ posts , and multiple practitioner studies, here's what now carries the most weight:

Saves

This is the single most powerful signal. One save drives approximately 5 times more reach than a like and 2 times more than a comment. A save tells the algorithm this content has lasting reference value - exactly what the new system is designed to reward.

Meaningful comments

Comments of 15 words or more carry 2.5 times more algorithmic weight than short ones. Generic comments like "Great post!" are now classified as engagement noise and may actually be penalised. The algorithm can distinguish between a genuine response and a performative one.

Dwell time

How long someone actually reads or watches your content matters enormously. Posts generating 31-60 seconds of dwell time achieve maximum distribution. This is the silent multiplier - you can't see it in your analytics, but it's one of the most powerful signals driving your reach.

Relationship signals

This is where it gets interesting for anyone serious about networking. Commenting once on someone's post creates an 80% chance you'll see their next post. Sending a DM creates a 90% chance they'll appear in your feed. Visiting someone's profile boosts their visibility to you by 60%. And if an author responds to comments within the first 30 minutes, that post receives 64% more total comments and 2.3 times more views.

What gets penalised

Engagement bait, engagement pods (now detected algorithmically), hashtag stuffing, mass tagging of more than 10 people, and AI-generated formulaic content. That last one is significant - Brixon Group's data from over 500 B2B profiles found AI-generated content receives 47% less organic reach. Using more than 5 hashtags triggers a 68% reach reduction. Hashtags have gone from being a primary discovery mechanism to having minimal impact.

The message is clear. Create content people want to save. Write things that make people stop scrolling and actually read. And engage genuinely - both on your own posts and on other people's.

Which Formats Get the Best Reach

Not all content formats perform equally under the new algorithm. Here's what the data shows from van der Blom's 2025 report and Socialinsider's analysis of 1 million posts :

Documents and carousels

These remain the powerhouse format. Multi-image document posts drive a 6.60% engagement rate - the highest of any format. The best-performing documents have 8-10 slides with clear visual storytelling and generate 15-20 seconds of dwell time. The reach multiplier sits at 1.45 times average. If you're only going to invest in one format, this is it.

Frameworks and how-to posts

Posts that present a clear process, a step-by-step method, or a structured framework consistently outperform opinion pieces without structure. This makes sense under a save-driven algorithm - people bookmark save things they can refer back to, representing one of the highest multiplier in organic reach . A carousel that walks through your five-step approach to solving a specific problem is exactly the kind of content this algorithm was designed to surface.

Polls

Highest reach multiplier at 1.64 times, yet they represent only 1.4% of all content on the platform. That's a significant untapped opportunity. Use them strategically, not as throwaway engagement bait.

Short vertical video

Video usage surged 69% year-over-year, but here's the catch - video reach actually declined 35-72% because completion rates are typically low. The algorithm penalises content people don't finish. Short vertical video (30-90 seconds) with captions is the exception - vertical video reach increased 80%, while horizontal video reach declined 18%.

Multi Image Posts

These have also been reported by a large study to have more engagement each year, as it increases dwell time as the scroller is wanting to see all the images posted.

Newsletters and articles

Reach climbed nearly 48% for these formats. The algorithm favours authority-driven long-form content. If you have genuine expertise to share, a newsletter is a strong strategic play and subscribers get notified on the platform as well as an email to their inbox by default.

Text-only posts

The weakest format. Usage down 41%, engagement down 18%. A text-only post with no visual element is fighting an uphill battle.

Unique points of view

It has also been shown that posts with unconventional or unique points of view can get 165% more organic reach too - so doing your homework and coming up with personal and different view points can benefit your posts.

Links

Too many spammy links flags the algorithm but links that help the post and give readers more information is good and helps boost reach .

FAQ

Personal Profiles - What the Data Says and What to Do

Here's the good news hidden inside the reach decline: while raw views are down approximately 50%, engagement per post is actually up 12%. Fewer people are seeing each post, but those who do are more relevant to the content and engage more meaningfully.

That's not a bug. That's the whole point of the algorithm change. LinkedIn is showing your content to people who actually care about your topic rather than blasting it across your entire follower base.

Your profile is now a ranking signal

This is perhaps the most important tactical shift to understand. The algorithm cross-references your post content against your profile headline , About section, and experience. If it can't categorise your expertise, it can't distribute your content properly. Profiles with a Social Selling Index above 75 achieve 2.8 times higher content performance than those below 60 - and that correlation has strengthened 37% since 2023.

Your profile isn't just a digital CV anymore. It's an active input to the algorithm that determines whether your posts get seen.

Choose 2-3 topic pillars and stay inside them

The algorithm needs to know what you're about. If you post about manufacturing processes on Monday, dog training on Wednesday, and cryptocurrency on Friday, the system can't build a coherent picture of your expertise. Pick 2-3 areas that align with your professional positioning and commit to them for at least 90 days.

For a B2B professional in engineering or technical services, that might look like: your domain expertise (the specific problems you solve), your approach or methodology (how you solve them), and proof or case work (results and lessons learned).

This is a social network - not a broadcast channel

I've said this for years and the data now backs it up completely. LinkedIn is a networking tool. People want to hear your personal views on things. They want to see your face. They want to understand your career journey and how it connects to the expertise you're sharing.

Posts with personal experience, your own photographs, and genuine opinion consistently get more reach than corporate-sounding content pushed out as promotional material. That doesn't mean every post needs to be deeply personal - but it does mean your voice, your perspective, and your human experience should come through in everything you share.

The algorithm is designed to reward authenticity. Use it.

Connections matter - but not in the way you might think

Reach decline varies by account size. Accounts under 5,000 followers experienced the steepest drop at 78%. The sweet spot sits around 15,000-25,000 followers, where reach dropped only 43%. Larger accounts with 50,000+ followers saw a 62% decline.

But here's what I've observed personally working on client accounts: I've seen directors with a few hundred connections get more reach and engagement than people with several thousand. The quality of posts and the engagement beyond those posts - commenting on other people's content, building genuine relationships, having real conversations - are enormously powerful. There's quite a lot of movement around reach at any particular follower level. A smaller, highly engaged network built around your genuine professional community will outperform a large, passive audience every time.

FAQ

Company Pages - The Structural Problem and How to Respond

This is where the data gets uncomfortable.

Organic company page content now represents just 1-2% of the LinkedIn feed , down from 7% in 2021. Company page posts reach approximately 1.6% of followers - a 15% decline from late 2023. The overall organic reach decline for company pages sits at 60-66% from 2024 to early 2026, significantly steeper than the 50% decline for personal profiles.

One study shows that personal profiles generate 561% more reach and 8 times more engagement than company pages sharing identical content. The Refine Labs study found that employee posts generated 2.75 times more impressions and 5 times more engagement than the company page, despite employees having 46% fewer followers . And 92% of B2B buyers trust employee recommendations over traditional advertising. Yet another study of over 150k posts and $million impressions shows it’s more like 14x times more engagement .

Van der Blom's feed composition analysis tells the full story. In 2022, organic company content made up 7% of the feed and top creator content was 15%. By 2025, organic company content had shrunk to 2% while top creator content had grown to 31%. LinkedIn ads account for 11% and promoted company content takes up 28%.

LinkedIn is engineering a pay-to-play model for company visibility. That's the reality.

What to do about it

Stop treating your company page as your primary distribution channel. It's not. It's now your credibility anchor - your shop window, not your megaphone.

Your company page still matters for brand legitimacy, recruiting content, major announcements, and as a platform for paid amplification through Thought Leader Ads (which promote personal profile posts with paid targeting). When you do post organically, prioritise carousel and document posts - they maintain the strongest performance on company pages. Text-only posts are effectively dead on company pages with a 0.42 times reach multiplier.

The real distribution engine for your company is now the people within it. Invest your content creation energy into the personal profiles of your directors, subject matter experts, and team leads. That's where the reach is. That's where the trust is. And that's where the conversions happen.

FAQ

Beyond Posts - Sales Navigator, Newsletters, and Going Live

LinkedIn is not only about posting and commenting. There are several other features that, when used strategically, turn the platform into a complete B2B growth system.

Sales Navigator

This is where inbound and outbound come together on one platform. Your team can use Sales Navigator to identify and connect with your exact target audience - by industry, company size, job title, geography, and buying signals. Once those connections are made, your content appears in their feed naturally. The outbound prospecting builds the network. The inbound content builds the trust. Combined, they create a pipeline that neither approach achieves alone.

Newsletters

Newsletter reach climbed nearly 48% under the new algorithm. When someone subscribes to your newsletter, they get a notification every time you publish. That's a direct line to your audience that doesn't depend on the algorithm at all. For B2B professionals with genuine expertise to share, a regular newsletter is one of the most reliable distribution channels available on the platform.

LinkedIn Live and events

Going live on LinkedIn creates real-time engagement that the algorithm rewards. Live sessions also serve as content that can be repurposed into shorter posts, carousels, and clips. LinkedIn Events allow you to promote, host, and follow up with attendees - turning a single session into a full engagement cycle.

Connections as engagement, not just broadcasting

Every connection request, every DM, every profile visit is a relationship signal that feeds into the algorithm. Connecting is not just about growing a number. It's about building a network of people who are genuinely relevant to your business - and who will see, engage with, and respond to your content over time.

We'll go deeper into each of these in a future blog - how to build a complete LinkedIn system that combines content, outreach, and relationship-building into one coordinated approach. But the key point here is this: if you're only thinking about posts, you're only using a fraction of what LinkedIn offers.

The Playbook - This Isn't Marketing's Job Anymore

Here's the shift most companies haven't made yet: LinkedIn strategy in 2026 is not something you hand to the marketing department and walk away from.

The algorithm rewards authentic expertise. It reads your profile. It evaluates whether your content matches your professional background. It assesses whether comments are genuine. AI-generated formulaic content gets 47% less reach. Generic corporate posts from brand pages reach 1-2% of the feed.

What does get rewarded? Real people sharing real expertise in their own voice. Directors. Technical leads. Subject matter experts. The people who actually know the work and can talk about it with authority and personality.

That doesn't mean every director needs to become a full-time content creator. What it means is this:

Subject matter champions lead the content

The ideas, opinions, and insights need to come from the people who have them. A technical director who shares their perspective on a manufacturing challenge will always outperform a polished corporate post about the same topic. CEO content generates 4 times more engagement than average employee. Only 3% of employees share company content, yet those shares account for roughly 30% of total company engagement and convert at 7 times the rate of other channels - from a study of more than 150k posts.

Co-ordination and AI reduce the time commitment

Here's where it becomes practical. Your subject matter experts don't need to write every word themselves. A 15-minute voice note about a recent project can be transcribed using AI, shaped into a post or carousel by a marketing coordinator, and reviewed by the expert before publishing. The authentic ideas, the genuine tone of voice, and the personal engagement in comments - those must come from the real person. But the production process can be streamlined significantly.

Engagement beyond your own posts is essential

Spend 15 minutes after publishing commenting substantively on 3-5 relevant posts in your network. Respond to every meaningful comment on your own posts within 90 minutes. Visit the profiles of people you want to build relationships with. Send genuine DMs. These relationship signals directly determine whose content appears in whose feed.

Build a consistent format mix

Don't post the same format repeatedly - the algorithm penalises this by up to 20%. Rotate between carousels, text-and-image posts, polls, and occasional video. Prioritise frameworks and save-worthy content. Every post should pass a simple test: would someone bookmark this to refer back to later?

FAQ

Closing Summary - Strategy First, as Always

LinkedIn has changed. The reach has dropped. The rules are different. But the platform remains the single most powerful B2B networking and lead generation tool available - and the companies that adapt will have a significant advantage over those still posting generic company updates hoping the algorithm will be kind.

The shift from follower-based to engagement-based to relevance-based distribution is not something to fear. If you're a technical company with genuine expertise, real domain knowledge, and people who can talk about their work with authority and personality, this algorithm was designed to reward exactly that.

But you can't wing it.

Just like any well-founded marketing process, your LinkedIn approach needs a strategy behind it. Look at what your competitors are doing on the platform. Understand who you're trying to reach and what they care about. Define your topic pillars. Decide who in your organisation should be your voices on the platform. Determine the format mix, the posting cadence, and the engagement routine. Build a plan, test it, measure the results, and refine.

You wouldn't design a production system without engineering it properly. Your LinkedIn presence deserves the same discipline.

Your technical operations run like clockwork because you engineered them that way. Your presence on LinkedIn - arguably your most powerful business development channel - shouldn't be any different. Strategy first. Plan second. Execution third. Measure and refine fourth.

Ready to build your LinkedIn strategy? Talk to us about how we can engineer a practical, measurable LinkedIn approach for your business - one that turns your expertise into a consistent pipeline. Book a free strategy audit and let's build it properly - together.

Written by Stefan Buss , founder of Sales & Marketing Engineers. Stefan has been using LinkedIn as a networking, learning, and business development platform since 2010 - and has spent the last 15 years helping sales and marketing teams companies turn their expertise into consistent growth.

AI Adoption in Business in 2026

Thu, 26 Feb 2026 16:15:10 GMT

Where Companies Really Are and What to Do About It

The Hype vs The Reality

If you've been reading the headlines, you'd think every business in the country has an AI-powered sales team, a fleet of automated assistants and a custom-built intelligence system running the show.

The reality is very different. A massive adoption lag - I see it a lot myself in business I speak to and the survey I carried out at the end of the year echoes this.

Almost half of all workers globally haven't used AI at all. Not once. In the UK, only around a quarter of businesses are using it in any form. And among those that are, most are doing little more than asking it to check their emails or rewrite a paragraph.

Meanwhile, the noise keeps getting louder. Agentic AI. Autonomous workflows. Custom knowledge hubs. Every conference, every LinkedIn post, every vendor pitch makes it sound like you're already five years behind.

You're probably not. And that's exactly why this matters.

This article gives you a clear, research-backed picture of where businesses actually are with AI in 2026 - and a practical framework for moving forward without the overwhelm. No jargon. No hype. Just what you need to know as a business leader to make sensible decisions about AI.

Quick Links

The Real Numbers Behind AI Adoption

Let's start with the data, because it paints a very different picture from the one most people assume.

Gallup surveyed over 20,000 workers in late 2025 and found that 49% had never used AI at work. Just 12% were daily users - up only slightly from 10% the year before. A further 26% used it a few times a week. That's progress, but it's hardly the revolution the headlines suggest Gallup Workplace AI Study .

The UK picture is even more sobering. The Office for National Statistics found that just 25% of UK businesses were using AI by late 2025, but more recently BCC/Paragon Bank (March 2026) shows figures now at 31-35%. Among larger companies with 250 or more employees, that figure rose to 44% - but for SMEs, adoption remains significantly lower ONS Business Insights Survey .

At a global level, the numbers look more encouraging on the surface. McKinsey found that 88% of organisations say they use AI in some form. But only 1% describe their rollout as "mature" and just 6% qualify as high performers seeing meaningful financial returns McKinsey Global Survey: The State of AI in 2025 .

We ran our own SME Engineering AI Adoption Survey 2025 at the Advanced Engineering Show in late 2025 and found a similar pattern. 67% of engineering organisations were either not using AI at all or engaged in what we'd call unstructured experimentation - trying things out with no plan, no measurement and no coordination. Over 70% were not using AI regularly in any meaningful way. Nobody - not a single respondent - identified as a power user with advanced skills.

The gap between what people think is happening and what's actually happening is enormous.

What People Are Actually Using AI For

Here's the part that often surprises people. Even among those who are using AI, the level of sophistication is remarkably low.

In our engineering survey, 94% of AI users were using it to generate text - help me write this email, proof this document, draft a LinkedIn post. Half were using it for some form of marketing intelligence and 37% for basic competitive analysis. But only 14% had ever run a deep research project - the kind of structured, multi-source analysis that AI platforms like ChatGPT, Gemini and Claude are now very capable of.

This lines up with what OpenAI found when they analysed how people actually use their platform. Roughly three-quarters of all conversations are basic guidance, information-seeking and writing tasks. The dominant behaviour is still "ask a question, get an answer" rather than using AI as an integrated part of a workflow OpenAI: The State of Enterprise AI 2025 Report .

Microsoft's 2025 Work Trend Index put a number on this. They found that 47% of employees still treat AI as a command-based tool - type a question, get a response, move on. The other half are starting to use it as more of a thinking partner, and that second group is seeing significantly better results. The split is almost exactly down the middle, but the productivity difference between the two groups is substantial Microsoft 2025 Work Trend Index . Using it as a co-worker and where prompting is a conversation rather than a highly polished prompt or two is a massive unlock for how to use and get the most of AI.

There's an interesting pattern in who uses AI and how much. Gallup found that leaders and senior managers use AI nearly three times more frequently than individual contributors - 44% compared with 23%. This makes sense when you think about it. Leadership roles naturally involve more of the tasks AI is good at - strategic thinking, synthesising information, drafting communications, delegation and planning. But it also means the people on the ground, doing the day-to-day work where AI could save the most time, are the ones using it least.

The sophistication gap is as wide as the adoption gap. Most people aren't even close to using AI in the ways it's designed to be used.

Marketing Is Leading the Way

One area worth calling out is marketing, which consistently shows higher adoption rates than almost any other business function.

Research from the Social Media Examiner found that 60% of marketers now use AI every day - a significant jump from 37% in 2024. 84% increased their usage over the past year and 82% plan to increase it further Social Media Examiner AI Report 2025 . This makes marketing one of the fastest-moving functions when it comes to AI adoption.

Our own survey backed this up. In engineering companies, 66% had used AI to help with marketing strategy - though primarily for text generation rather than deep research or competitive analysis.

This matters for two reasons. First, if you're a business leader looking for the best place to start with AI, sales and marketing is one of the most natural entry points. The tasks are well-suited to it - research, content creation, competitive analysis, customer profiling, messaging. Second, marketing adoption tends to lead broader business adoption. When teams see real results in one function, it builds confidence to expand into others.

If your marketing team isn't using AI yet, it's worth asking why - because your competitors' marketing teams almost certainly are. See our AI for sales and marketing services for how we help engineering and technical companies get started. Taking the lessons from marketing and applying this to the rest of the company can be transformational. If you work in front of a screen - the applications are numerous and can come in so many different forms.

Why People Aren't Adopting - The Real Barriers

This is where it gets interesting, because the barriers aren't what most people expect.

When we asked engineering companies what was stopping them from using AI more, the number one answer wasn't budget. It wasn't the technology. It wasn't data quality or compliance concerns.

It was skills and change management - at 61%. Followed by accuracy concerns at 33%, data quality at 28% and scepticism at 17%. Budget came in at just 11% SME Engineering Survey Report .

The barrier is human, not technical. People don't know how to use it properly. They don't have the training. They're not sure what's safe to use it for and what isn't. And in many cases, nobody in the organisation is taking responsibility for figuring it out.

Slack's research confirmed this pattern at a broader level. They found that only 7% of desk workers consider themselves expert AI users. 61% had spent fewer than five hours learning how to use AI. And 30% had received zero training whatsoever Slack: Fall 2024 Workforce Index .

Think about that for a moment. We're in the middle of what many describe as a technology revolution, and nearly a third of the workforce has had no training at all. Most have spent less time learning AI than they'd spend watching one film.

This lack of training has consequences. MIT research found that 95% of enterprise AI pilots are failing - not because the technology doesn't work, but because of flawed integration and the gap between what AI can do and what organisations are ready for Fortune / MIT Report .

And perhaps the most telling finding - an NBER survey of over 6,000 executives found that while 70% are actively using AI, over 80% of businesses report no measurable productivity impact. The tools are being used, but the results aren't showing up at a company level NBER Executive Survey .

That's a staggering gap. And it points directly to the real problem - it's not the technology, it's how people are approaching it.

The Hidden Problem - People Aren't Talking About It

There's another dimension to this that doesn't get enough attention. Many people who are using AI aren't telling anyone.

Gallup found that 66% of remote workers use AI compared with just 32% of those who work on-site. Some of that is explained by the types of work people do remotely. But there's a reasonable argument that part of it is about visibility - it's easier to use AI when nobody is watching.

This is backed up by Slack's finding that 48% of employees feel uncomfortable telling their manager they use AI for common tasks Slack: Fall 2024 Workforce Index .

Nearly half the workforce feels like using AI is something to hide. That's a cultural problem, not a technology problem.

When people feel like using AI is cheating - or that admitting to it might reflect badly on their abilities - you get a workforce where adoption happens in secret, without coordination, without shared learning and without any of the benefits that come from a structured, team-wide approach.

This is one of the biggest missed opportunities in business right now. If your team is quietly using AI on their own but nobody is talking about it, sharing what works, building on each other's discoveries or aligning their usage with business goals, you're leaving enormous value on the table.

As a leader, the single most powerful thing you can do is make it safe to talk about AI openly. Acknowledge that people are experimenting. Encourage them to share what they've found. Create space for learning rather than judgement.

Our own survey found something encouraging here: regular AI users were more than twice as likely to feel excited about AI rather than cautious or concerned. Experience builds confidence. Fear decreases through hands-on use, not theory.

Don't Get Distracted by the Shiny Stuff

This is the part where I want to be direct with you.

There is an enormous amount of noise right now about agentic AI, autonomous systems and fully automated workflows. The vendor pitches are slick. The case studies are compelling. The LinkedIn posts make it sound like everyone is building AI agents that run entire departments.

The research tells a different story. McKinsey found that 62% of organisations are experimenting with AI agents, but fewer than 10% have scaled them in any function. Only 23% are actively building towards agentic systems, and fewer than 5% of enterprise applications include agentic features in 2025 McKinsey Global Survey: The State of AI in 2025 .

OpenAI's own data shows that among enterprise customers, custom GPT and Projects usage grew 19 times year-to-date - which sounds impressive until you realise that 20% of enterprise messages now flow through these custom setups. That means 80% of enterprise AI usage is still basic chat OpenAI: The State of Enterprise AI 2025 Report .

Agentic AI, custom knowledge bases, integrated data systems and automation - these are all coming. They matter. They will reshape how businesses operate. But jumping to those before your team can confidently use a basic AI chat is like installing a CNC machine before your operators can read engineering drawings. The sequence matters.

The companies that will genuinely benefit from advanced AI are the ones that first built the foundation - access, skills, confidence and a strategy for how AI fits into their operations. Without that, advanced tools just become expensive experiments that nobody uses properly.

As for AI app -there are hundreds - all customising and tailoring how AI is used. Many of these functions can be done using a handful of tools, frontier models like chat GPT, Gemini, Claude can do more than people think. Starting with these and leveraging them fully first is the way to go and where your workflows and needs start developing and your adoption is maturing, the individual apps may be the way to go. Either way the frontier models can give you guidance on this.

"Waiting for the next update is not a strategy, it's falling behind."

A Practical Path Forward - Audit, Strategy, Training

Here's the good news. You don't need to understand agentic AI. You don't need custom data pipelines or automated systems. You don't need to hire an AI specialist or invest in expensive enterprise platforms.

What you need is a structured approach to three foundational stages that most businesses haven't done yet. Get these right and you'll see meaningful productivity gains and workflow improvements - without the complexity.

Stage 1 - Audit Where You Are

Before you do anything else, take stock. You need an honest picture of where your organisation currently sits with AI.

This means understanding who is using it and who isn't, what they're using it for, how confident they feel, what tools they have access to, and what barriers are getting in the way. It also means looking at your business workflows and identifying where AI could genuinely add value - not where it sounds impressive, but where it would make a practical difference to how your team works day-to-day.

An audit doesn't need to be complicated. It can be a structured survey, a series of conversations or a facilitated workshop. The point is to move from assumptions to facts. Most leaders are surprised by what they find - both by how little is happening in some areas and how much quiet experimentation is happening in others.

Companies with a formal marketing strategy in place were 40% more likely to have piloting or operational use of AI within their business, according to our survey. Strategy creates the structure and clarity needed to identify where AI adds value. Without it, you're guessing.

Stage 2 - Build an AI Strategy

An audit tells you where you are. A strategy tells you where you're going and how to get there.

This doesn't mean a 50-page document. It means answering a few practical questions: what are the three to five workflows where AI could make the biggest impact? What tools and platforms do we need? What are the rules - what's acceptable to use AI for and what isn't? Who is responsible for driving this forward? How will we measure whether it's working?

The companies that are seeing real results aren't the ones with the most advanced AI tools. They're the ones with a clear plan for how to use them. BCG found that just 5% of companies have achieved genuine AI maturity, while 46% remain at an early experimental stage - but the mature companies are seeing 1.7 times greater revenue growth BCG: The Widening AI Value Gap .

The difference isn't the technology. It's the strategy behind it.

Stage 3 - Personalised, Role-Specific Training

This is where most companies fall down completely. They give people access to AI tools - maybe a ChatGPT licence, maybe a Copilot subscription - and then expect them to figure it out on their own. Some run a generic webinar or share a few links to articles. Then they wonder why nothing changes.

Effective AI training needs to be personalised to what each person actually does. A sales director needs to learn different things from an operations manager. A marketing coordinator will use AI differently from a finance lead. Generic "introduction to AI" sessions might raise awareness, but they rarely change behaviour. Yes a basic foundation course is great, but taking the application to a more personalised level thereafter is a real game changer.

Training should be hands-on and practical. Show people how to use AI for their specific tasks - their proposals, their reports, their research, their customer communications. Give them frameworks for briefing AI properly, not just typing questions. Teach them how to review and refine AI outputs so the quality is consistent. And importantly, teach them what AI isn't good at - so they develop judgement alongside skill.

Remember the stat: 30% of the workforce has had zero AI training and 61% have spent fewer than five hours on it. The bar is incredibly low. Even a focused half-day of practical, role-specific training puts your team ahead of the vast majority.

And the effect of training goes beyond skills. Our survey showed that regular AI users were more than twice as likely to be excited about AI rather than cautious. Experience doesn't just build capability - it builds confidence and shifts sentiment. Training is the single fastest way to move people from fear to enthusiasm.

Depending on your company, a pilot training and use case approach with the employees who are keen may just show all the other sceptics in the company the benefits. Not all employees are equal. Gaining an insight into the attitudes beforehand around AI is also key, as transforming your business with AI is as much a cultural, change management process as it is a systems and technology one!

What This Means for Your Business

If you're reading this as an MD, commercial director or operations director of an SME, here's what all of this data actually means for you.

You're probably not as far behind as you think. The majority of businesses - including most of your competitors - are either not using AI or using it in the most basic way possible. The headlines are misleading. The reality is that most organisations are still at the starting line.

But that doesn't mean you can afford to wait. The gap between those who are taking structured steps and those who aren't is widening fast. BCG found that the top 5% of AI-mature companies are pulling away from everyone else, and the advantage compounds over time.

The opportunity right now isn't about buying the best tool or building the most advanced system. It's about doing three things that most of your competitors haven't done: auditing where your organisation is, building a clear strategy for adoption, and training your people to use AI with confidence and purpose.

These three stages alone - without touching data integration, automation or agentic systems - can produce meaningful productivity gains and fundamentally change how your team works. Faster proposals, better research, sharper messaging, more consistent output, less time on repetitive tasks.

The problem isn't the technology. The problem is fear of the unknown, a lack of investigation and a lack of coordinated action. The businesses that face it, take stock and start building from the ground up will be the ones with the advantage - not in five years, but right now.

How to Future-Proof Your Organisation

The pace of AI development isn't slowing down. New models are released every few months. Capabilities that seemed futuristic 12 months ago are now standard features. The tools will keep getting better, faster and more accessible.

But tools without skills are just expensive subscriptions. And skills without strategy are just random productivity boosts that never compound into something bigger.

Future-proofing your organisation doesn't mean chasing every new development. It means building the capability to absorb and use whatever comes next. That means a team that's comfortable with AI, confident in how to evaluate and use new tools, and working within a clear framework that connects AI usage to business goals.

Start with the audit. Build the strategy. Invest in proper, personalised training. Then iterate - review what's working, expand what's delivering results, and keep developing your team's skills as the technology evolves.

The companies that win in the next three to five years won't be the ones with the fanciest AI systems. They'll be the ones that built the skills, the confidence and the structure to use whatever comes next - and started that process now.

Your technical operations run with precision because you engineered them that way. Your approach to AI deserves the same discipline. Strategy first. Skills second. Tools third. Everything else follows.

Ready to take the first step? Book a free strategy audit and let's work out exactly where your business is with AI - and build a practical plan to move forward. Or explore our AI for sales and marketing services to see how we help engineering and technical SMEs adopt AI with purpose.

FAQs

Written by Stefan Buss , founder of Sales & Marketing Engineers. With a background in industrial engineering and over two decades helping technical companies systemise their growth, Stefan brings an engineer's precision to sales, marketing and AI adoption strategy.

Why Market Fit Drives Your Successful Sales and Marketing Direction

Thu, 05 Feb 2026 19:15:13 GMT

Why understanding market demand and environment before building your strategy transforms your entire business direction - not just your marketing.

You wouldn't commission a complex production line without first understanding throughput requirements, material availability, and output specifications. You wouldn't invest in new machinery without assessing whether the demand exists to justify the capital. Yet every day, engineering and technical SMEs jump straight to promotion - LinkedIn posts, new websites, trade show stands - without ever properly analysing the market forces shaping demand for what they offer.

The result? Scattered activity, wasted budget, and the frustrating conclusion that "marketing doesn't work for us."

This article explains why market analysis must come before tactics - and gives you practical methods for understanding the demand landscape so you can steer your entire business direction, not just your marketing.

Quick Links

The Strategy Gap Nobody Wants to Acknowledge

Here's the uncomfortable truth I've observed working with engineering and technical SMEs: most business owners don't want to do proper market analysis. Not because they're lazy, but because they're afraid of what they might find or they think it would just tell them what they already know.

The statistics paint a stark picture of how little attention market analysis receives.

The second most common reason why of startup failures cite lack of market need as the primary cause of failure - and followed by being outcompeted (20%) and a flawed business model (19%). All of which could benefit from better market analysis, and competitive analysis - see our blog on that.

Yes - this is for startups but the same applies for businesses failing to grow or in decline

Research from London & Partners reveals equally sobering findings: 46% of businesses launched without any market research to validate demand. From the same report, 49% never analysed their competition before launching - and 54% of early-stage London founders had no business plan at all.

When founders who've experienced failure were surveyed, 58% wished they had conducted more market research before launching - the same percentage who wished they'd developed stronger business plans. This retrospective insight suggests the market research gap isn't merely an academic concern but a practical oversight that founders themselves recognise only after it's too late.

The disconnect between what technical companies communicate and what buyers need to make decisions creates a fundamental market intelligence gap.

FAQ

What I've Observed in Engineering SMEs

In my experience working with technical business owners, I find most have a decent grasp of their market - but that understanding typically dates back to when they first started the business or from their experience and from limited sources.

They knew their sector intimately when they launched. They understood the pain points, the buying patterns, the key players. That knowledge gave them the confidence to take the leap.

But here's what I've noticed: almost none have a regular or annual way of keeping up with market changes in a central document. The market understanding that drove their early success gradually becomes outdated, held loosely in the founder's head rather than systematically tracked and updated.

Meanwhile, the market shifts. Technology evolves. Customer priorities change. New regulations reshape buying decisions. And the business keeps operating on assumptions that may no longer hold true.

The irony is that proper market analysis rarely delivers the doom-laden verdict entrepreneurs fear. What it typically reveals is far more nuanced: a balanced picture of market forces at play, opportunities you hadn't considered, and demand patterns that might favour certain parts of your offering over others.

That intelligence doesn't kill your idea. It steers it. It helps you position your business where demand actually exists, rather than where you hope it exists.

And here's the thing - if you do this analysis critically, or even better have it done by someone outside your business, you may be surprised what emerges. Fresh eyes see patterns that familiarity blinds us to.

What Market Analysis Actually Covers

Market analysis is often confused with competitive analysis, but they serve different purposes within your sales and marketing strategy .

Market analysis takes a macro view of the landscape. It examines the broader forces shaping demand in your sector.

Technology trends

What new technologies are emerging? What's becoming obsolete? Where is innovation creating new opportunities or disrupting existing approaches?

Customer behaviour shifts

What are buyers prioritising now that they weren't two years ago? What problems are they actively trying to solve? What's gaining popularity in your sector?

Economic and political factors

Interest rates, regulation changes, government incentives, trade conditions - how do these affect investment decisions in your market?

Funding and investment patterns

Where is capital flowing? Which sub-sectors are attracting investment? What does this signal about future demand?

Industry growth trajectories

Is your market expanding, contracting, or fragmenting? Which segments show the strongest momentum?

Essentially, market analysis is trying to assess the demand and market forces that can accelerate or slow down growth in your industry - so you can leverage this in your business strategy as well as your sales and marketing.

Competitive analysis , by contrast, examines specific players in your space - their positioning, strengths, weaknesses, and strategies. We cover this in detail in our competitive analysis guide.

Audience profiling focuses on your ideal customers - who they are, what they care about, and how they make buying decisions.

All three - market analysis, competitive analysis, and audience profiling - sit within the research phase of a complete sales and marketing strategy. Together, they form the intelligence foundation that everything else builds upon.

FAQ

Where Market Analysis Fits in Your Strategy

If you're wondering which part of your business to invest in, develop, and promote more heavily, market analysis provides an objective way to assess the terrain for the business as a whole.

Before you create the blueprint for your sales and marketing systems, you need solid intelligence about market conditions. Think of it like commissioning a new production facility: you'd assess raw material availability, energy costs, labour markets, and output demand before specifying the equipment. Market analysis performs the same function for your go-to-market approach.

It answers fundamental questions:

Is demand for your core offering growing, stable, or declining?
Which of your services or products sit in expanding market segments?
What external forces might accelerate or constrain your growth?
Where should you concentrate resources for maximum return?
What market conditions should inform your pricing and positioning?

Without this foundation, every tactical decision you make - from your website messaging to your sales outreach - rests on assumptions rather than evidence.

Practical Tips for Engineering SMEs

Market research is no longer a luxury reserved for corporations with dedicated research teams. Modern tools and methods have made sophisticated analysis accessible to resource-constrained SMEs. Here's how to approach it systematically.

Deep Research with AI Models

The technology landscape for market intelligence has shifted dramatically. You can now use frontier AI models like ChatGPT, Claude, or Perplexity to synthesise publicly available information about your market.

Start with prompts like:

"What are the key trends affecting [your sector] in the UK over the next three years?"
"What technology shifts are reshaping demand in [your industry]?"
"What government policies or regulations are likely to impact [your market] in 2025-2026?"
"Where is investment capital flowing in [your sector] - which sub-segments are attracting funding?"

AI won't replace your judgement, but it accelerates the research phase dramatically. Use it to gather, synthesise and check each others figures - then apply your own expertise to interpret the findings.

I found from my engineering survey at the end of 2025, that not many sales and marketing experts knew how to use the deep research function in Gemini or Chat GPT - this goes beyond the normal search function of an AI chat and really goes deep into creating a research document.

FAQ

SEO and Search Data Analysis

Search engine optimisation analysis provides a window into actual demand. By examining what people are searching for - and how search volumes are trending - you can see which of your services or products attract the most active interest.

Tools like Google Trends (free), SEMrush, ubersuggest or Ahrefs reveal:

Relative search volumes across your different service areas
Whether interest in specific solutions is growing or declining
Seasonal patterns in demand
Geographic variations in interest

This data helps you understand where genuine demand exists - not where you assume it exists. If searches for "industrial automation consultancy" are climbing while "traditional machinery maintenance" is flat, that signals where market momentum lies.

Customer Surveys - Multiple Channels

Your existing customers are your best source of market intelligence, yet most SMEs never systematically ask them about market trends, buying criteria, or sector conditions.

Consider three approaches:

In-person conversations

When you're on site or meeting clients, ask open-ended questions about their challenges, what they're investing in, and what trends they're seeing in their own markets.

Sales team debriefs

Your salespeople can be a great asset here if they hear about budget pressures, project priorities, and industry shifts every week. Formalise this by implementing structured call notes and weekly intelligence sessions, or specific questions they ask their customer base. This should be done in both a subjective ( their gut feel) as well as objective methods (specific questions to be answered). This can also be received in a positive light - that you care and are gathering data about your industry.

Email surveys

Send a short survey (10 minutes maximum, 25-30 questions - or shorter) to your top 20-30 customers. Focus on market conditions, not just satisfaction - what are they prioritising? What's changing in their sector?

Research shows that companies who close the loop after surveys generate three times more promoters in subsequent measurements - so use survey responses as conversation starters, not just data points.

Industry Exhibitions - Strategic Intelligence Gathering

Trade shows remain high-value intelligence opportunities when approached as market research exercises rather than just sales or networking events.

Before attending

Identify trend talks and keynote sessions that address market direction. Note which exhibitor categories are expanding - new entrant clusters often signal market shifts.

On site

Attend presentations on industry outlook, technology trends, and sector forecasts. Speak to people on the ground floor - engineers, technicians, procurement specialists - about what's changing in their day-to-day work.

Listen for recurring themes across multiple conversations - what challenges keep coming up? What solutions are generating buzz? Observe which stands draw the most traffic - this often indicates where buyer interest is concentrated.

The goal isn't to gather competitive intelligence (that's a separate exercise) but to understand broader market dynamics and demand patterns.

Free Data Sources

Government and industry data provides substantial secondary research value at no cost:

ONS statistics for sector trends, construction output, and economic indicators
Make UK reports for manufacturing-specific intelligence and forecasts
Institution of Engineering and Technology publications for technical sector insights
Trade association reports for industry-specific trend analysis
Google Trends for demand validation and trend identification

FAQ

Put It in Writing - And Open It to Debate

Many business owners do some market analysis informally - conversations with customers, casual industry reading, gut feelings about trends. But keeping it in your head limits its value.

Writing your market analysis down achieves several things:

It forces clarity - vague assumptions become specific, testable statements
It communicates your research and reasoning to partners and employees
It opens your conclusions to debate and challenge
It creates a baseline you can revisit and update

This last point matters more than most realise. Encouraging feedback on your market analysis engages your team in strategic thinking. It sets a culture where decisions have factual or researched influence, rather than relying solely on the founder's instinct.

Market analysis is a mixture of robust research and some estimation from trends and sentiment. Acknowledging that explicitly - "here's what the data shows, here's what we're inferring" - makes your analysis more credible, not less.

Elevating Sales and Marketing to Strategic Functions

When market analysis is combined with competitive analysis and audience profiling, something important happens: sales and marketing stop being reactive cost centres and become strategic functions that shape business direction.

This research foundation informs not just your promotional activity but your business plan and operations as a whole. It helps you decide which services to develop, which markets to enter, and where to concentrate resources.

Research indicates that companies with strong sales-marketing alignment achieve 20% annual growth compared to 4% revenue decline for misaligned organisations . That alignment starts with shared market understanding - a common view of where demand exists and how the landscape is shifting.

For engineering and manufacturing SMEs, the combination of long sales cycles, complex buying committees, and technical product considerations creates both heightened intelligence requirements and unique opportunities. You already understand systems, processes, and continuous improvement. Apply that same systematic thinking to understanding your market position.

FAQ

Assess the Terrain Before You Build

Your technical operations run like clockwork because you specified them properly. Your market understanding deserves the same rigour.

Before you invest time and budget into marketing that might miss the mark - before you even develop your competitive positioning - build a clear picture of the market forces at play. Understand where demand is growing, what's shifting in buyer priorities, and which external factors might accelerate or constrain your growth.

Market analysis isn't about validating what you already believe. It's about stress-testing your assumptions before the market does it for you - and using what you learn to steer your entire business direction.

Ready to build market analysis into a complete strategy? Our sales and marketing strategy service includes comprehensive market research specifically designed for engineering SMEs. Or start with a free strategy audit to identify where your market understanding might have gaps.

Written by Stefan Buss, founder of Sales & Marketing Engineers. With a background in industrial engineering and over two decades helping technical companies systemise their growth, Stefan brings an engineer's precision to sales and marketing strategy.

The Two Poles of Thinking about AI

Mon, 19 Jan 2026 19:33:10 GMT

Why Dismissing It Misses the Point (and So Does Blind Faith)

I find the attitude towards AI and AI-assisted work remarkably polarised.

I'll be honest with you - I'm fascinated by AI. I've shown AI outputs to many people over the past year, and I consistently get two types of responses.

There's the "Wow, that's amazing - tell me more about how you did that" crowd. Genuinely curious, leaning forward, wanting to understand the process. That is me generally.

Then there's the dismissive response: "Oh, so you just pressed a button and got AI to do that."

That second reaction interests me more than the first. Because underneath it lies a fundamental misunderstanding about how AI actually works - and what it takes to use it well.

The Button-Pressing Myth

The dismissive camp seems to believe AI does all the heavy lifting. You type a prompt or two, press enter, and out comes the finished product with little or no input from the user. They treat it like a vending machine. Insert request, receive output.

Some seem genuinely suspicious. They assume the options are already there - a template thing, as if AI came up with all the ideas beforehand and just moulds them for your request. There's often an underlying anxiety too. A sense that AI will take over, that it cheapens real work, that it's somehow cheating.

What they don't realise is that it still takes significant strategic thought from the user. The right questions. The right context. And critically - the idea to do it in the first place.

AI doesn't replace thinking. It amplifies it.

A Lesson from the Photography Club

This reminds me of something I experienced years ago when I started at a photography club.

I began with slide film, like everyone else. It was the trusted technology - been around for decades, reliable, proven. Then digital cameras came along, specifically the digital SLR. Photography club standard, so to speak.

At first, digital was frowned upon. Now you could take as many photos as you liked without the cost of developing. You could see your shots instantly. You could edit on a PC. It seemed like an unfair advantage - too good to be true. It challenged the established way of doing things.

The club split into two camps. Sound familiar?

What the challengers didn't realise was that all those advantages came with new challenges. You could take more pictures, but then you had to organise and sort through hundreds to find the best one. You could edit photos, but you had to learn Photoshop or Lightroom and go through months of trial and error to develop any real skill. You could shoot without limit, but that freedom demanded new discipline.

Another set of skills emerged. A different craft developed. But at the end of the day, digital gave you more options and more opportunity - if you were willing to put in the work to master it. It

AI Is the Same - But Amplified

The same is true with AI, albeit amplified and applied to far more areas of work.

It takes more skills, more know-how, and more discipline to get genuinely good results. You need to understand what AI can and can't do. You need to frame your questions properly. You need to know when to push back on outputs and when to refine your approach. You need a system that's sustainable, not just impressive for one demo.

Technology makes things faster - but it doesn't make them easier. Not if you want quality, relevance and scalability.

Here's what I've learned from working with AI across sales and marketing projects: the best outputs come from the best inputs. That means clarity on your strategy, your audience, your positioning. It means understanding the context deeply enough to guide the AI properly - your tailored sales and marketing strategy.

You can't shortcut thinking. You can only accelerate its execution.

The Blind Faith Problem

But on the polar opposite the dismissive crowd aren't entirely wrong to be cautious. There's another camp I haven't mentioned yet: the blind faith adopters.

These are the people who trust AI outputs without question. They copy, paste, and publish without proofing. They accept the first response as gospel. They stop thinking because the machine has thought for them.

This is just as problematic as dismissing AI entirely - perhaps more so.

The research backs this up. A landmark study from Harvard Business School and Boston Consulting Group found that consultants using AI for tasks outside its capabilities were 19% less likely to produce correct solutions compared to those working without AI. The researchers described how users essentially "switch off their brains" and outsource their judgment - what Harvard's Fabrizio Dell'Acqua termed falling asleep at the wheel . In a related study, Dell'Acqua found that recruiters using high-quality AI became lazy, careless, and less skilled in their own judgment - ultimately missing brilliant applicants and making worse decisions than those using no AI at all. ( Big Think )

More recently, the MIT Media Lab's "Your Brain on ChatGPT" study used EEG monitoring to measure brain activity during writing tasks. The findings were stark: participants using ChatGPT showed the weakest neural connectivity, while those writing without AI exhibited the strongest, most distributed brain networks. Perhaps most concerning, when ChatGPT users were later asked to write without AI assistance, they showed reduced brain engagement - suggesting a form of cognitive debt that accumulates with sustained AI reliance.

AI makes mistakes. It hallucinates facts. It misses context. It can produce plausible-sounding nonsense that slips past you if you're not paying attention. I've seen it confidently cite statistics that don't exist and reference companies that were never founded.

Blind faith leads to laziness. And laziness leads to costly mistakes - a client presentation with fabricated data, a blog post with incorrect claims, a strategy built on assumptions the AI invented.

The real skill isn't trusting AI or distrusting it. It's knowing when to lean on it and when to push back. It's treating AI as a capable colleague who needs supervision, not an oracle that delivers truth.

Question the outputs. Check the facts. Apply your own judgement. That's not slowing the process down - it's the process working properly.

The Opportunity for Those Willing to Invest

For SME owners, particularly in technical and engineering sectors, this matters.

AI isn't a silver bullet. It won't fix broken processes or unclear positioning. If your sales and marketing lacks direction, AI will just help you produce scattered content faster. That's not progress - it's accelerated chaos.

But if you've done the work - if you have a clear strategy , defined your audience, built your value proposition - then AI becomes a genuine multiplier. You can do more with less. You can test ideas faster. You can maintain consistent activity without burning out your team.

Our survey at the Advanced Engineering Show found that 66% of engineering companies used AI when creating their marketing strategy - but primarily for basic text generation, not strategic research or competitive analysis. ( SME Engineering Survey Report )

That tells me most companies are only scratching the surface. They're using AI as a word processor with extra features, not as a strategic tool. The opportunity lies in going deeper.

Beyond the Polarised Debate

I'm not suggesting everyone needs to become an AI enthusiast. Healthy scepticism has value. But dismissing AI as "just pressing buttons" misses something important about the future of work.

Every major technology shift has created winners and losers. The winners aren't usually the early adopters who jumped in without strategy. They're the ones who understood the tool well enough to use it systematically - who built new disciplines around new capabilities.

Photography clubs eventually stopped arguing about film versus digital and realised it was all about the image!

The craft evolved. The standards evolved. The skilled photographers remained skilled - they just had different tools.

AI in sales and marketing will follow the same pattern. The question isn't whether to use it. It's whether you'll invest the time and strategic thought to use it properly.

For engineering and technical SMEs, that investment could be the difference between consistent growth and another year of random activity producing random results.

PS The image above is genuinely mine - taken on the east coast of the North Island at the Mangawhai Heads in New Zealand - with my phone. It took about 7 pictures to get the right pic by the way. It’s amazing what technology we now have in our pockets and can take better pics in some instances than my digital SLR!

Written By Stefan Buss

Strategy First: Start Your Sales and Marketing at the Beginning

Mon, 12 Jan 2026 15:32:42 GMT

Why jumping straight to tactics costs you time, money, and confidence - and what to do instead.

You wouldn't build a machine without a blueprint. You wouldn't pour concrete without surveying the ground first. Yet every day, SME owners skip straight to the 'doing' in sales and marketing - posting on LinkedIn, building a new website, running ads - without ever designing the system those tactics should fit into.

The result? Scattered activity, wasted budget, and the frustrating conclusion that 'marketing doesn't work for us'.

This year, start at the beginning. Strategy first, then plan, then tactics, then implementation. That sequence matters more than any individual tool or channel you choose.

The Planning Gap Nobody Talks About

The data is stark. A UK survey of nearly 2,000 SME decision-makers found that 54% of businesses don't have a documented business plan, and 67% don't have a marketing action plan. The researchers explicitly linked this to 'random acts of marketing' - activity without an integrated plan.

Our own survey of engineering companies at the Advanced Engineering Show confirmed this pattern. Only 48% had a formal marketing strategy in place - and that was at an exhibition where attendees already prioritise marketing more than the broader sector. The true sector average is likely much lower.

Why does this matter? Because companies with formal sales and marketing strategies were 110% more likely to report clear alignment between the two functions. Strategy creates a shared language and direction - the foundation for joined-up revenue generation.

The LinkedIn Trap: A Familiar Story

Let me paint a picture you might recognise.

You've been thinking about it for months. LinkedIn. Everyone says you should be on it. Your competitors seem to be posting regularly. So finally, you sit down one evening and create your first post. It feels awkward, but you do it. Then another. And another.

A few weeks pass. You're posting, but nothing's happening. No enquiries. No messages. The likes feel hollow.

So you watch some YouTube videos. Hours of them. You subscribe to a course. You learn about content pillars and posting schedules and hashtag strategies. You create a plan to roll this out properly over the next few months.

Three months later, still nothing. You give up. LinkedIn doesn't work.

Except it does work. Just not the way you were using it.

Fast forward a couple of years. You finally hire a marketing manager. They do something you never did: they start with strategy. They research your market. They profile your ideal clients. They map the buyer's journey.

And they discover something useful. Only a specific segment of your ideal clients actually use LinkedIn - and they only engage with it at a certain stage of their buying journey. Posting alone was never going to convert them. But LinkedIn's search function, InMail, and targeted connection requests? Those could work as part of a broader prospecting system.

All that time spent posting into the void could have been avoided if you'd started with the question: who exactly am I trying to reach, and where do they actually look?

The Seduction of 'Doing'

Here's the thing. Tactics are seductive. They feel productive. You can see a LinkedIn post. You can touch a new brochure. You can measure website traffic.

Strategy, by contrast, feels abstract. It doesn't produce anything you can hold. It requires thinking rather than doing. And for technically-minded business owners who've built companies through action, that can feel uncomfortable - even wasteful.

But here's what the data tells us. CoSchedule's marketing strategy research found that only 17% of marketers document their entire strategy, while 14% have no documented strategy at all. Of those who do plan, just 17% always proactively plan campaigns - the rest are winging it to varying degrees.

The Content Marketing Institute's B2B research shows the cost of this approach: 58% rate their content strategy as only 'moderately effective'. Just 22% say their content marketing is extremely or very successful. And crucially, where nobody 'leads as a strategic approach', teams default to reactive, scattershot tactics.

Here's the correlation that matters: among top performers, 74% say their content strategy is extremely or very effective - compared to just 29% overall. The research suggests a clear link between companies that strategise and those that succeed with their marketing. Strategy isn't overhead. It's the differentiator.

Random activity produces random results. That's not a marketing problem - it's a systems problem.

Marketing is More Than Promotion

Most people think marketing equals promotion. A post here, an ad there, maybe some SEO. But sustainable marketing runs much deeper.

Consider the 6Ps: Product, Price, Place, People, Process, and Promotion. Promotion is just one piece. Without clarity on the other five, your promotional efforts scatter - lots of noise, little impact.

Before you write a single post or design a single ad, you need answers to fundamental questions. Who exactly is your ideal client? What specific problems do they face? Why should they choose you over a competitor? Where do they actually look for solutions? What's their buying journey from awareness to decision?

Without these answers, you're firing into the dark. With them, every tactical decision connects to measurable outcomes.

The Right Sequence: Strategy, Plan, Tactics, Implementation

Think of building a bridge. You wouldn't start by ordering steel. You'd survey the terrain, calculate load requirements, specify materials, and design the structure before laying a single girder.

Sales and marketing work the same way.

Strategy: The Scoping Project (Your Redprint)

If your plan is the blueprint - the detailed design drawings - then strategy is the redprint: the scoping project that comes before any design work begins. It answers the 'why' and 'who'. It defines the parameters everything else must work within. This is where you survey the landscape, understand the constraints, and establish the foundations.

Plan: The Design Drawings (Your Blueprint)

Your plan translates strategy into specifics. Which channels will you use? What's your marketing mix? How do sales and marketing connect? What's the budget? What are the milestones? This is your engineering specification - the detailed drawings that guide construction.

Tactics: The Components

Tactics are the individual activities - the LinkedIn posts, the website pages, the email sequences, the sales calls. Each one should serve the plan, which serves the strategy. Without that connection, tactics become random acts of marketing.

Implementation: The Build

Implementation is where the work happens - consistently, systematically, with measurement and refinement. But note where it sits in the sequence. Last, not first.

What Your Strategy Should Actually Include

So what goes into a proper marketing strategy ? Here's the framework, broken into five essential sections.

1. Research: Survey the Landscape

Before you can position your business or choose your channels, you need to understand the terrain. This is the foundation everything else builds upon.

Market Analysis - Industry trends affecting your sector: what's growing, what's declining, what's emerging
Competitive Analysis - Who's doing what well and not so well, and where the gaps lie
Audience Profile - Your ideal customers, partners and stakeholders, their challenges and desires
Customer Buying Journey - The acquisition funnel (how they find and choose you) and the retention funnel (how they stay and refer)

2. Positioning: Define Your Place in the Market

With research complete, you can make informed decisions about how to differentiate and present your business.

Business Plan Alignment - Your high-level 3-5 year objectives that marketing must support
Offering Clarity - Your services and products, and which to prioritise based on demand and margin
Value Proposition - Your USP, taglines, and elevator pitch that capture why you're different
Market Positioning - Where you sit competitively: specialist vs generalist, premium vs value
Style and Tone of Voice - The personality and language that resonates with your audience and sets you apart

3. Go-to-Market: Choose Your Channels and Campaigns

Now you can decide where and how to reach your audience - based on evidence, not assumption.

Marketing Mix - Which channels align with where your audience actually looks
Outbound vs Inbound - The balance between prospecting and attraction for your sales cycle
Campaign Types - Awareness, lead generation, nurture, and retention campaigns
Asset Requirements - The website pages, content, collateral, and sequences you'll need

4. Plan: Resource and Sequence Your Implementation

Strategy without a realistic plan stays on the shelf. This bridges strategy to action.

Resource Assessment - Time, people, tools, and partners: what you have and what you need
Budget Allocation - Total investment and how it splits between setup and ongoing activity
Implementation Sequence - What to build first, with dependencies mapped
Timeline - Quarterly milestones, monthly priorities, and clear ownership

5. Continual Improvement: Measure, Learn, Refine

Marketing is never 'done'. This establishes how you'll iterate and improve.

Experimentation Framework - How you'll test new channels, messages, and offers
Analytics and Measurement - Tools, dashboards, and reporting cadence
Key Driving Indicators - Leading indicators (traffic, enquiries) and lagging indicators (conversions, revenue)
Review and Adaptation - Scheduled strategy reviews and triggers for course correction

Can AI Write Your Sales and Marketing Strategy?

Can you have your strategy written by AI? Hell no. But can it assist you?
Absolutely.

Strategy sits at the intersection of research and future projection. AI can accelerate much of the research - market trends, competitor analysis, audience insights, channel options. It can help you assess alternatives, stress-test assumptions, and explore scenarios you might not have considered. For the analytical heavy lifting, it's a genuine multiplier. But it needs a good steer!

But here's what AI cannot do: it cannot understand the nuances of your business, your relationships, your ambitions, or your constraints. The overall direction - where you're heading and why - needs to be discussed, debated, and chewed over by the people who actually run the business. Strategy isn't a document you generate; it's a position you arrive at through genuine deliberation. That's what transforms generic frameworks into a tailored approach that fits your specific situation.

The way AI and human thinking need to weave together is an art, much like strategy itself. And this brings me to a broader point I've seen play out time and time again: marketing strategy cannot be left to one person alone. Yes, someone may champion it, drive it forward, and hold the pen. But good strategy is important enough to be baked into the culture and leadership of the company. When strategy lives only in the marketing manager's head - or worse, in a document nobody else has read - it fails to align the business. When it's genuinely owned by leadership, debated in meetings, and referenced in decisions, it becomes the compass that keeps everyone moving in the same direction. See our AI services if you need help with a holistic plan from auditing, training to structured case studies and full implementation.

What This Means for Your Business

If you're reading this as an MD, Commercial Director, or Operations Director, here's the practical takeaway.

Before you commission that new website, before you hire that marketing person, before you launch that LinkedIn campaign - ask yourself: do we have a documented marketing strategy?

Not a plan. Not a list of activities. A strategy. One that defines your ideal client profile, your competitive positioning, your value proposition, and how sales and marketing will work together as one revenue engine.

If the answer is no, start there. Everything else can wait.

The time you invest in strategy upfront will save you months of scattered activity, wasted budget, and the frustration of wondering why nothing seems to work.

Engineer Your Growth

Your technical operations run like clockwork because you engineered them that way. Your revenue generation deserves the same precision.

Strategy first. Plan second. Tactics third. Implementation fourth.

That's not just a sequence. It's the difference between building a proper system and hoping for rain.

But here's the difference - writing your own strategy is hard. Not because you don't know your business, but because you know it too well. You're inside it every day. Internal biases creep in. Assumptions go unchallenged. You end up running in circles, too close to see the full picture.

That's where outside help makes the difference. Not someone to "do it" for you - strategy that lives in an agency's folder isn't strategy at all. But a third party who can facilitate the process, do the research heavy lifting, ask the uncomfortable questions, and help you look at your business from the outside in. Someone who can guide the conversation, challenge your thinking, and then help bake the strategy into your leadership team so it actually sticks.

The best strategies aren't handed over in a PDF. They're built collaboratively, debated properly, and owned by the people who have to execute them.

This year, start at the beginning. And if you need a hand getting there, don't go it alone.

Ready to build your strategy? Book a free strategy audit and let's engineer your growth properly - together.

9 Ways to Market When Times Are Tough

Wed, 08 Oct 2025 13:57:56 GMT

There are two types of companies when the economy tightens.

The first type stops marketing. They see it as an expense, something to cut when the pressure mounts.

The second type leans in. They see marketing as a revenue engine, something to refine, focus, and optimise when every pound counts.

The difference between these two approaches determines who weathers the storm and who gets left behind.

Money vs Effort: Understanding the Real Investment

Spending money on marketing and focusing effort on it are two different things.

If there's commitment from the top, real commitment, not just lip service, then limited budget doesn't mean limited results. Where there's a will, there's a way.

Here's the reality: most companies let campaigns run on autopilot when things are working. They don't refine. They don't optimise. They just let it tick over.

When tough times force a scale-back on spend, it doesn't mean you scale back on effort and focus. In fact, stopping focus creates long-term damage. Restarting momentum later is difficult and expensive. Keeping cadence is smarter.

The Hidden Opportunity in Tough Times

Difficult periods force a reset, and that's actually valuable.

Tough times present the perfect opportunity to review your strategy, reassess your audience, and analyse your competition. The landscape shifts in moments like these. Your competitors change their approach. Customer priorities evolve. Standing still isn't neutral, it's falling behind.

There's an old saying: "If you do what you've always done, you'll always get what you've always got."

I disagree with that. In a world changing this fast, if you stand still and do what you've always done, you'll slowly get less and less from it.

So what can you actually do when times are tough and business isn't flowing like it was?

9 Things You Can Do in Marketing When Times Are Tough

1. Review Your Audience Profile

When did you last really talk to your customers?

Now's the time to investigate properly. Run surveys. Reach out to contacts and ask their opinions on the issues making times tough for them too.

This doesn't just update your CRM or increase your social connections, it keeps you connected to lapsed clients and dormant contacts. These conversations matter. They rebuild relationships when budgets are frozen and buying decisions are on hold.

2. Clean Up Your CRM

While you're reconnecting with your audience, take the opportunity to audit your database.

Remove dead contacts. Update job titles and company details. Tag contacts properly so you can segment and target more effectively.

A clean CRM means your outreach is relevant, your data is reliable, and your sales team isn't wasting time chasing ghosts. It's unglamorous work, but it pays dividends when you need precision over volume.

3. Analyse Your Competitors

When last did you do a full competitive review?

This is the time to look closer, spot opportunities, update yourself with best practice, and spark ideas that are leftfield.

Keeping your marketing visible while others go quiet is a massive competitive opportunity. It might not yield immediate conversions, but it builds relationships. When the money returns to the market, the alliances are already in place.

4. Work on Building Relationships

Monitor your closest competition carefully. If they stumble or fall, you need to be positioned to step in and serve the clients left behind.

But more importantly, use this time to build genuine connections with prospects and dormant accounts. The companies that stay present and helpful during difficult times are the ones that win the work when budgets open up again.

Relationships built under pressure tend to be stronger. People remember who showed up when times were hard.

5. Reassess Your Products and Services

Often companies don't connect marketing with the products and services they offer, but they should.

Strategically positioning or redesigning your services can massively improve your market fit and amplify your marketing efforts. Sometimes the best marketing move isn't a new campaign, it's a smarter service offering.

6. Keep Communication Flowing

Staying top of mind with your audience and your community is one of the most cost-effective things you can do in marketing.

Emails, social posts, newsletters, and well-timed calls are relatively easy and inexpensive, and they should not be stopped in tough times.

Consistency beats perfection. A regular, helpful presence builds trust and keeps your name in the conversation.

7. Learn More About AI

AI can help with many of the tasks above, but you need to understand where it fits.

The mistake people make is thinking AI can handle an entire marketing process from start to finish. That's where it gets complicated, and you end up down a rabbit hole.

But there are certainly parts of the process where tools like ChatGPT can help you easily, if you know how. There will always be human intervention needed, and outside perspective to keep you grounded. Knowing which is which is the key.

8. Refine What's Already Working

Before you cut anything, analyse what's actually delivering results.

Double down on the channels, messages, and tactics that are working. Stop the things that aren't. Tough times demand efficiency, not guesswork.

Test, measure, refine. Optimise your existing campaigns rather than starting from scratch. Sometimes the best growth comes from improving what you already have by 20%, not chasing something entirely new.

9. Keep Outside Help Close

Marketing agencies tend to have a reputation for being expensive, but what you're paying for is experience, perspective, and the management of implementation.

Scaling back doesn't mean you have to say goodbye to your agency. Keep them close. There's a deal to be done that leverages both their skills and your internal resources.

Perhaps shift from full implementation to mentorship or consulting. Keeping some outside perspective will always help your strategy stay diverse and alive.

Final Thought: Don't Go Dark

Tough times separate the reactive from the strategic.

Companies that disappear from the market might save money short-term, but they lose momentum, relationships, and visibility, all of which are expensive to rebuild.

The companies that stay visible, stay connected, and stay focused? They emerge stronger when the market recovers.

Your pipeline shouldn't depend on perfect conditions. Build systems that keep turning even when times are tough, because that's when the real opportunity lies.

Need help staying visible without overspending? Let's talk about what's possible with the resources you have. Sometimes it's not about doing more, it's about doing the right things consistently.

Competitive Analysis: How to Gain a Competitive Advantage

Fri, 19 Sep 2025 04:42:06 GMT

Want to stand out? First, you’ve got to know what you’re up against.

"We are not worried about our competition - we want to do things our way" - is a common approach I hear from companies.

Your brilliant engineered products and services deserve brilliant positioning. But first, you need to know what you're up against, or you risk doing exactly what your competitors do - or missing out on what actually works.

Most engineering SMEs think they can build better products and customers will find them. That's like designing a bridge without surveying the terrain first - you might create something magnificent, but will it actually get people where they need to go?

Why Competitive Analysis is Your Strategic Redprint

Before you create the blueprint for your company's sales and marketing systems, you need a strong strategy (redprint) based on solid competitive intelligence.

Think of competitive analysis as reverse-engineering the market. Just as you'd dismantle a competitor's product to understand how it works, you need to dismantle their entire go-to-market strategy to see where the weak joints are.

This isn't just about knowing who's in your space. It's about stress-testing their approach to find where they'll crack under pressure - and where you can build something stronger.

Here's the reality check: only 53% of UK SMEs regularly collect and use competitive data . Yet those same businesses believe 37% of their competitors are using business intelligence most or all of the time. That gap between perception and practice creates opportunity for those willing to do the work.

The Five Forces Framework: Understanding Your Competitive Landscape

Before jumping into competitor websites and social media, step back and examine the structural forces shaping your industry. Porter's Five Forces provides a systematic way to understand competitive dynamics:

1. Competitive Rivalry How intense is the competition in your sector? In engineering and manufacturing, this often comes down to technical capability, relationship strength, and pricing. Assess how many competitors serve your target market and what differentiates the leaders.

2. Threat of New Entrants How easy is it for new players to enter your market? High barriers (specialist expertise, certifications, capital requirements) protect established players. Low barriers mean constant disruption risk.

3. Bargaining Power of Suppliers Are you dependent on a few key suppliers? Do they have leverage over your costs? This force often gets overlooked in competitive analysis but directly impacts your pricing flexibility.

4. Bargaining Power of Buyers How much power do your customers have? Can they easily switch to competitors? Are they price-sensitive or value-driven? Understanding this shapes your positioning strategy.

5. Threat of Substitutes What alternatives exist to your product or service? For engineering companies, this might include in-house solutions, different technologies, or even doing nothing at all.

A systematic review of Porter's Five Forces applied to SMEs found that industry rivalry and customer bargaining power were the most influential forces affecting performance, particularly in competitive sectors where differentiation and cost leadership play crucial roles.

The SME Competitive Analysis Framework

Now let's get practical. Here's a structured framework for engineering your competitive analysis:

Phase 1: Landscape Mapping

Before diving deep, map the terrain. Identify:

↳ Direct competitors - businesses offering similar products or services to similar customers

↳ Indirect competitors - those solving the same problem differently

↳ Aspirational competitors - larger players whose approach you might learn from

↳ Emerging threats - new entrants or adjacent businesses moving into your space

Start with 5-7 key competitors. You can always expand later, but depth beats breadth in the early stages.

Phase 2: Digital Presence Audit

Your competitors' websites, social profiles and content tell you more than they realise. Systematically examine:

Website Analysis

Messaging and positioning - what problems do they claim to solve?
Service offering structure - how do they package and present their work?
Credibility signals - case studies, testimonials, certifications
Call-to-action approach - how do they convert visitors?
Content depth - are they investing in thought leadership?

Social Media Assessment

Platform presence - where are they active?
Content themes - what topics do they prioritise?
Engagement levels - what resonates with their audience?
Posting frequency - how consistent is their activity?

SEO and Content Footprint

Keywords they rank for
Content types performing well
Backlink profile strength
Local vs national visibility

Tools like SimilarWeb, SEMrush, and Ahrefs can accelerate this analysis. But don't skip the manual review - sometimes the most valuable insights come from simply reading their content as a potential customer would.

Phase 3: Marketing Strategy Deconstruction

Go deeper into how your competitors generate and convert leads:

Channel Mix

Where are they investing budget? (SEO, PPC, social, events, partnerships)
What's the balance between inbound and outbound?
Are they using content marketing or relying on relationships?

Lead Generation Approach

What offers do they use to capture interest? (guides, consultations, audits)
How sophisticated is their nurture sequence?
What's their follow-up process?

Sales Process

How quickly do they respond to enquiries?
What's their qualification approach?
How do they handle proposals and pricing?

One effective technique: become a prospect. Request their brochure, sign up for their newsletter, ask for a quote. Experience their customer journey firsthand.

Phase 4: Customer Intelligence

Your competitors' customers are talking - you just need to listen:

Review Mining

Google Reviews, industry directories, Trustpilot
What do customers praise? What frustrates them?
Look for patterns across multiple reviews

Social Listening

Comments on their posts
Mentions in industry groups
Discussions on LinkedIn

Direct Feedback

When prospects choose a competitor, ask why
When you win against a competitor, understand the differentiators
Talk to industry contacts who work with multiple suppliers

Customer feedback reveals gaps that no website analysis can uncover.

Phase 5: AI-Accelerated Analysis

Modern AI tools have transformed competitive intelligence from a quarterly project to an ongoing system. Here's how to use them effectively:

Deep Research Prompts Use AI to synthesise publicly available information. Start with prompts like:

"Analyse [competitor website URL] and summarise their positioning, target audience, and key differentiators"
"Compare these three competitors on pricing approach, service offering, and messaging tone"
"Based on this competitor's content, what customer pain points are they addressing?"

Automated Monitoring Tools like Crayon, Owler or Klue can track competitor changes automatically - new product launches, pricing shifts, leadership changes, press coverage. Set up alerts for your key competitors.

Trend Analysis AI can help identify patterns across multiple competitors:

"What topics are my top 5 competitors all creating content about?"
"What service offerings are trending in my sector based on competitor activity?"

Research shows that approximately 35-39% of UK SMEs are now actively using AI-powered tools , a significant increase from 25% in 2024. Those who adopt systematic approaches to AI are seeing measurable time savings and better decision-making.

A word of caution: AI accelerates analysis but doesn't replace strategic thinking. Use it to gather and synthesise - but apply your own judgement to interpret findings and make decisions.

Turning Intelligence into Action

Here's where most businesses fail - they gather intelligence but don't engineer it into actionable strategy. Your competitive analysis is only valuable if you systematically apply what you've learned.

Apply What's Proven to Work

When you spot competitors succeeding with specific tactics, don't ignore them - improve them. If a competitor's case studies are generating leads, create better case studies. If their webinar strategy is working, engineer a more systematic approach to educational content.

This isn't copying - it's taking proven components and engineering them to work better in your system.

Engineer Solutions for Market Gaps

The real opportunity lies in what competitors aren't doing. Look for:

↳ Service gaps - where customer complaints aren't being addressed

↳ Communication gaps - where technical concepts aren't being explained clearly

↳ Channel gaps - where competitors aren't reaching certain audience segments

↳ Process gaps - where their customer journey has friction points

↳ Content gaps - topics your audience needs that nobody is covering well

Build Your Differentiation Matrix

Create a simple comparison grid with your company and your key competitors listed across the top. Down the side, list the factors that matter most to your buyers - things like target market focus, core positioning, key differentiator, pricing approach, content quality, digital presence, and service breadth.

Work through each cell systematically, noting how you and each competitor perform against these factors. Be honest - the point isn't to convince yourself you're better, but to see clearly where you're genuinely different and where you're just one of many.

This visual comparison reveals positioning gaps and opportunities that aren't obvious when you look at competitors individually.

Test and Calibrate Your Position

Like any engineered system, your competitive positioning needs testing and refinement. Monitor how your differentiation performs in the market and adjust accordingly.

Track these indicators:

↳ Win rate against specific competitors

↳ Common objections in sales conversations

↳ Sources of lost deals

↳ Customer feedback on why they chose you

Your competitive advantage isn't a one-time build - it's a system that requires ongoing maintenance and improvement.

Competitive Analysis Cadence

One-off analysis becomes outdated quickly. Build competitive intelligence into your ongoing operations:

Monthly (30 minutes)

Quick scan of competitor websites for major changes
Review competitor social media activity
Note any new offerings or messaging shifts

Quarterly (2-3 hours)

Deep dive into one or two key competitors
Update your differentiation matrix
Review win/loss patterns against competitors

Annually (half day)

Full competitive landscape review
Reassess who your key competitors are
Update your positioning strategy based on market shifts

The engineering companies that will win in the next five years aren't the ones with the flashiest marketing - they're the ones who engineer their competitive intelligence with the same precision they apply to their products.

Systematic Market Advantage

Your engineering expertise gives you a natural advantage in competitive analysis - you understand systems, processes, and continuous improvement. Apply that same systematic thinking to understanding your market position.

Before you invest time and budget into marketing that might miss the mark, engineer a clear understanding of your competitive landscape. Know what's working, identify the gaps, and build your strategy accordingly.

Remember: you're not just trying to be different - you're engineering a sustainable competitive advantage that your rivals will struggle to replicate.

Ready to stress-test your competitive position?

Our Industrial Growth Audit includes comprehensive competitive analysis specifically designed for engineering SMEs. We'll help you identify exactly where to position your business for maximum market impact.

Or start with strategy - see our sales and marketing strategy service to understand how competitive analysis fits into building your complete revenue system.

How to Turn Dormant Leads into Sales Opportunities

Tue, 05 Nov 2024 06:15:37 GMT

Unlocking the Hidden Potential in Your CRM

As a solopreneur or small business owner, you've likely spent a lot of time building up your database of contacts. You’ve networked, attended events, and collected business cards. But how many of those contacts are actively engaged with your business? If your CRM looks a little quiet, it’s time to dig deeper.

Here’s the good news: your CRM could be sitting on a treasure trove of potential leads. The trick is knowing how to find them, segment them, and re-engage with them. In this blog, we’ll explore why your CRM may be underutilised, the benefits of re-engaging your database, and how to get started.

"The CRM Misconception: It’s Not Just a Storage System - It's your Sales Asset"

Many small business owners view their CRM as a glorified address book—a place where contacts go to gather dust. In reality, your CRM is a powerful tool for driving sales, but only if you use it strategically. Every contact in your CRM is a potential customer or referral partner, and with the right approach, you can turn dormant leads into real sales opportunities.

Benefits of Unlocking Your CRM’s Potential:

Cost-Effective Lead Generation Acquiring new leads can be costly and time-consuming. By focusing on your existing CRM, you’re tapping into a resource that’s already in your hands. According to the UK’s Data & Marketing Association , retaining a customer can be up to 5 times cheaper than acquiring a new one , making it crucial to re-engage those already in your database.
Warmer Leads Leads in your CRM may already know your business, making them warmer than cold prospects. Even if they haven’t purchased from you before, they’ve expressed interest at some point. By re-establishing communication, you can build on that previous engagement.
Stronger Customer Relationships Regularly engaging with your database helps you build stronger relationships with your contacts. By sending relevant, valuable information, you can nurture these leads over time and position yourself as the go-to solution when they’re ready to buy.

How to Maximise Your CRM’s Potential

Now that we’ve covered the benefits, let’s dive into how to actually make it happen. Here’s a step-by-step guide to turning those dormant contacts into opportunities.

1. Segment Your Database

Not all contacts in your CRM are the same, and not all of them will have the same needs. The first step is to segment your database. This allows you to tailor your messaging and offers to the specific needs of different groups.

Start by creating segments based on criteria like:

Industry
Previous interactions (e.g., past customers, event attendees)
Lead source (where did you meet them?)
Time since last contact

Once you’ve grouped your contacts, you can create targeted campaigns that resonate with each segment.

2. Re-engage with a Value-First Approach

Now that you’ve segmented your database, it’s time to re-engage with your contacts. But here’s the key: don’t sell right away . Instead, lead with value. Offer helpful advice, free resources, or exclusive content that demonstrates your expertise and builds trust.

For example:

Send a personalised email offering a free consultation
Share a blog or case study relevant to their industry
Offer an exclusive discount for past clients

According to HubSpot , companies that use email marketing effectively see a 3800% ROI . Re-engaging your contacts with thoughtful, value-driven content can yield impressive returns.

3. Automate Follow-Ups

One of the biggest challenges for small business owners is time. Luckily, modern CRMs allow you to automate your follow-up process. Create automated email sequences that nurture leads over time without requiring constant manual input. This ensures that no contact is forgotten, and your business stays top of mind.

4. Monitor and Adjust

As you re-engage with your database, track the results of your efforts. Which segments are responding best? Are there particular emails or offers that are driving the most engagement? Use this data to refine your approach and continually improve your re-engagement strategy.

Success Stories: CRM as a Growth Engine

Here’s a real-world example:

Another one of our clients, a tech business, had about 1000 contacts in their CRM they had not had time to manage so some prospects had been contacted whilst others hadn't. So we put in a traffic light colour code system ie Hot Medium Cold to enable them to prioritise leads.

Final Thoughts: Your CRM Is Your Secret Weapon

Your CRM isn’t just a storage system—it’s a powerful sales tool. By segmenting your database, offering value-first re-engagement, and automating follow-ups, you can turn dormant contacts into sales-ready leads. It’s not about adding more contacts; it’s about making the most of what you already have.

At SME, we help small businesses like yours develop sales-focused marketing strategies that turn databases into revenue. From segmentation to mentoring, we offer a hands-on approach to help you maximise your CRM’s potential.

Ready to unlock the power of your CRM? Get in touch today and let’s turn those dormant leads into paying customers.

Breaking the Yo-Yo Cycle: How to Stabilise Your Small Business Sales

stefsdigital@gmail.com (Stefan Buss) — Sat, 21 Sep 2024 06:14:12 GMT

Are you tired of your sales fluctuating like the British weather? You're not alone...

In fact, it’s a common issue for small businesses in the UK. According to the UK's Department for Business and Trade , there were 5.51 million small businesses at the start of 2023, accounting for 99.2% of the total business population. Many of these businesses experience the same rollercoaster of sales highs and lows, and the root cause is often the same—a lack of a systematic process for filling the sales pipeline.

But it doesn’t have to be this way. By creating a solid, repeatable sales and marketing strategy, you can stabilise your sales and build a foundation for long-term growth. Here's how:

1. Create a Solid Sales and Marketing Plan

Start with a comprehensive plan that includes an audit of your current processes, a competitive analysis, and a deep dive into your target audience and value proposition. Think of this as the blueprint for your business’s success. A well-crafted plan gives you direction and ensures every marketing pound is spent wisely. Marketing is not a thing you do here or there - it's a process and it all starts with a good strategy, and kept updated too.

2. Implement Regular Marketing Activities

Consistency is key. Regular marketing activities keep your brand top of mind for potential customers. Whether its social media updates, email newsletters, or blog posts, keep a steady stream of content flowing. The FSB Small Business Index for 2024 indicates that while revenue for small businesses was weak at the start of 2024, there was a surge in growth aspirations. This shows the importance of consistent marketing efforts to convert these aspirations into reality.

3. Target Your Audience

Your products and services are not for everyone—and that’s okay. Focus on understanding who your ideal customer is. Create buyer personas and tailor your marketing messages to speak directly to their needs. The more specific you are, the more likely you’ll attract high-quality leads. Many companies say to us - "Well anyone could buy our products.." - but targeting everyone is very expensive, so narrow down who is most likely to buy, in order to target them first

4. Use Both Inbound and Outbound Approaches

Don’t put all your eggs in one basket. Combine inbound strategies like content marketing and SEO with outbound methods such as cold emailing, networking and direct mail. Inbound attracts customers organically (with marketing that attract prospects to get in touch with you), while outbound helps you proactively reach out to potential clients (getting in touch with individual contacts or companies directly).

5. Prospect Continually

Prospecting should be a continuous process, not just something you do when sales are low. Set aside time each week to identify and reach out to new potential customers or have someone to do this for you consitently. This way, you'll always have fresh leads entering your pipeline, reducing the risk of sales slumps.

6. Develop a Sales Pipeline

A well-structured sales pipeline gives you a clear view of where each prospect is in the buying process. This allows you to nurture leads effectively and move them towards a purchase systematically. Remember, building a sales pipeline isn’t just about making sales—it’s about building relationships.

7. Leverage Automation and Outsourcing

Automation tools can help streamline your marketing and sales processes, saving you time and ensuring consistency. Whether it's automating email follow-ups or using a CRM to manage customer relationships, leverage technology to do the heavy lifting. Outsource tasks that are outside your expertise to keep your focus on growing the business.

8. Build Relationships, Not Just Sales

Your goal should be to build lasting relationships with your customers. Focus on providing value, engaging with your audience, and offering exceptional customer service. Happy customers are not only more likely to buy again, but they’re also more likely to refer you to others.

Final Thoughts

It's clear that to break the cycle of inconsistent sales, you need a robust sales and marketing plan paired with consistent activities, targeted efforts, and relationship-building. According to the FSB , 52% of small businesses in the UK are looking to grow over the next 12 months—the highest in over two years. By implementing these strategies, you can join this growth-minded group and set your business on the path to stable, long-term success.

Ready to stabilise your sales? Let’s chat about crafting a plan that works for your business.