What is an AI baseline assessment for an SME?

A baseline assessment is a short, structured check of where your business actually stands before starting an AI programme: which tasks are repetitive enough to automate, where your data lives, who in the team already uses AI tools, what's legally required, and what return you can realistically expect. It prevents the most common mistake — starting a big AI programme at the wrong point.

What are the 5 questions an SME should ask before starting with AI?

1) Where is the repetitive work? 2) Where does your data live, and how accessible is it? 3) Who on the team already uses AI tools, even informally? 4) What's legally required for your sector and data? 5) What's the realistic return, in cost savings, efficiency, or customer experience?

Why does a baseline assessment matter before an AI rollout?

Without it, SMEs tend to pick the wrong starting point — a flashy use case instead of the highest-impact one — and underestimate data and legal prerequisites. A short baseline assessment surfaces the real priorities and risks before any budget is committed.

AI Baseline Assessment for SMEs: 5 Questions to Start

The Important First Step: an AI Baseline Assessment

For a mid-sized company with 10 to 50 employees, a full-scale AI programme can feel intimidating. But without a solid baseline assessment first, you'll almost certainly start in the wrong place. Here are five essential questions that help you decide where your AI journey should actually begin.

1. Where is the repetitive work?

Automation is at the core of AI. Look across your business for tasks that are repetitive by nature — these are usually the ones that benefit most from automation. Think administrative processes, data entry, or customer interactions. Mapping these out tells you exactly which processes are the right fit for AI.

2. Where does your data live?

Data is the fuel for every AI application. Take stock of where your data currently sits and how accessible it is. Is it structured and digital, or still largely on paper? A clear picture of your data infrastructure tells you what needs fixing before AI can be used effectively.

3. Who's already using AI?

Part of your team may already be using AI tools without you knowing. Identifying these early adopters can surface useful insight and scaling opportunities — and these enthusiasts often make the best internal ambassadors for wider AI adoption.

4. What's legally required?

It's essential to understand the legal constraints and obligations around AI — from data privacy law to sector-specific rules. Knowing the current regulatory landscape upfront avoids legal trouble later and keeps your rollout compliant from day one.

5. What's the return?

Finally, weigh the likely return of AI projects — in cost savings, efficiency gains, or improved customer experience. Setting clear goals upfront makes it possible to actually measure and justify the ROI of your AI investment.

AI Advies Bureau can help with this baseline assessment and the steps that follow. Get in touch to talk it through.