AI tools are available to everyone. But the workflow thinking, governance design, and operating model change that turn tools into real adoption — that requires expertise most SMEs don't have in-house.
Smaller organisations face different constraints and different opportunities than large enterprises. The AI adoption challenge is real — but so is the advantage.
Simpler workflows mean the AI opportunities are often clearer. A small marketing team knows exactly where it's losing time. A small operations team knows which processes are eating cycles. You don't need a six-week discovery to identify where the value is.
Less governance infrastructure means less internal friction but also more risk of getting it wrong. You can move faster when the decision-making is simpler. But you also need to be careful not to deploy AI in a way that creates liability or breaks a process that customers depend on.
Less capacity is the binding constraint. You can't run a six-month transformation programme whilst also running the business. An engagement has to be focused and realistic about what your team can absorb.
Competitive pressure is real. Larger competitors are adopting AI and the productivity gap is growing. But smaller organisations also have an advantage — there's less politics, fewer layers of approval, and a leadership team that can make decisions quickly. That speed can be worth more than scale.
Mike led enterprise-wide AI transformation at Verimatrix — a publicly listed global SaaS company — under direct ExCom oversight. The governance frameworks, workflow redesign patterns, and adoption disciplines developed at enterprise scale translate directly to smaller organisations. The questions are the same. The answers are sized differently.
That enterprise transformation included converting shadow AI into governed adoption, building AI-assisted workflows across engineering, sales and support, and designing a Responsible AI governance framework that was institutionalised across a regulated EU-listed company. The patterns and disciplines are the same ones smaller organisations need — just applied at a scale that fits.
The results at Verimatrix were measurable — not theoretical. And the same structured approach that delivered them applies whether you have 5,000 people or 50.
SMEs don't need to tackle everything at once. The value is in finding the right starting point — the workflow where AI creates the most impact relative to the effort required.
Proposal drafting, client research, CRM updates, follow-up sequences. For small sales teams, AI assistance can meaningfully increase pipeline capacity without adding headcount. The productivity gains compound when the workflows are properly designed rather than ad hoc.
Content creation, social media, email campaigns, market research. Small marketing teams often have more ideas than capacity. AI-assisted workflows can close that gap — but only when the brand voice, quality controls, and review processes are built into the workflow design.
Document processing, reporting, scheduling, knowledge management. These are the workflows where AI delivers the fastest time savings — often with the lowest governance complexity. For many SMEs, this is the natural starting point.
Research, analysis, report drafting, quality assurance. AI as a productivity multiplier for the work your clients actually pay for. The key is designing the human oversight so quality doesn't slip whilst throughput increases.
Invoice processing, expense management, regulatory compliance, financial reporting. Structured, repetitive workflows where AI assistance reduces errors and frees time for the decisions that actually need human judgement.
AI coding assistants, test generation, documentation, debugging. For smaller development teams, AI tools can deliver significant productivity uplift — but they need governance: approved tool lists, code review processes, and IP protections built in from the start.
Engagements are sized to what's appropriate for your organisation. More compact and faster than for a large enterprise. Honest about what you actually need rather than pushing a standard package.
Whether you're a growing professional services firm, a small technology company, a family business exploring automation, or an ambitious startup — the approach starts with understanding your specific constraints and opportunities, then designing something realistic.
A compact 1–2 week diagnostic that identifies the two or three workflows where AI can create the most value, and what would need to be true to pilot each. You don't need a comprehensive ecosystem map. You need a clear, practical recommendation — not a strategy document. Most SME engagements start here.
A 4–8 week pilot focused on one specific part of the business, properly designed and measured. Not a tool evaluation — a genuine pilot with workflow redesign, governance controls, and a clear success measure. You're testing whether the change works, whether your people can adopt it, and whether it delivers the value you expected.
Particularly well-suited to SMEs whose leadership team doesn't yet have AI expertise and doesn't need — or want — a full-time hire. One to three days a month. The focus is on the decisions that matter: which workflows to tackle first, how to think about governance and risk, how to build the skills in your team, what needs to change in how you operate.
Some SME leaders just want a senior, experienced perspective — someone who has done this before and can help them think through the decisions without committing to a programme. This can be a single session or ongoing. The questions that come up in a 20-person firm are structurally the same as the ones that come up in a 2,000-person enterprise. The answers are different. The questions aren't.
Whether you're exploring whether AI makes sense for your organisation, or you already know which workflow to tackle first — let's start with a conversation about what would work for you.