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What is it?
A proof of concept is a small-scale, scoped execution of an AI idea, designed to prove that it works technically and delivers value in the specific context of an organisation. It is not a finished product, but a focused experiment with a clear question: does this work, and is it worth building further?
A POC is deliberately limited in scope: a single process, a small dataset, a defined team. That boundary keeps it fast and inexpensive, and prevents organisations from making large investments based on assumptions that do not hold in practice.
Why it matters for SMEs
For SMEs, a POC is the most responsible way to introduce AI. Most AI failures are not technical failures but implementation failures: the idea was sound, but the reality was different. A POC surfaces that gap early, when course correction is still cheap.
- Limit risk before the main investment: a POC shows whether the approach works before you commit budget, time, and people to a full rollout. What does not work, you stop in time.
- Build internal support: a tangible working result convinces internal decision-makers more effectively than a presentation. A successful POC makes the step to implementation politically easier.
- Calibrate expectations: the POC shows exactly what AI can and cannot do in your specific context, so the follow-up conversation is grounded in realistic agreements rather than assumptions.
A POC is not an unnecessary delay; it is an accelerator in the longer run. Organisations that start with a POC build better and faster than those that jump straight into a large implementation.
How it works
A good POC follows a tight process: select a scoped use case, define what success looks like, build the minimum working version, measure the result, and decide whether to continue. The emphasis is on learning, not on a perfect end product.
- Choose a scoped use case: select a single process with a clear start and end, sufficient volume, and a measurable outcome.
- Define success criteria: agree upfront what a positive result looks like, so the verdict after the POC is objective and not decided retrospectively.
- Build the minimum version: implement only what is needed to answer the question. No extra features, no polished integrations.
- Measure and document: record what the AI did, how well it worked, and where it fell short. That data is the basis for the next step.
- Decide: continue, adjust, or stop. A POC that shows an idea does not work is a successful POC.
A POC ideally takes two to six weeks. Longer, and the scope is too broad or the question too vague. Shorter, and there is not enough data to form a well-grounded judgement.
Example in practice
Picture a staffing agency that wants to use AI to screen incoming CVs for suitability for standard logistics roles. Rather than building a full system straight away, the team runs a POC: the AI screens all new CVs for a single job category over three weeks and assigns each candidate a suitability score with a short explanation. A recruiter assesses the same CVs independently. After three weeks the team compares the scores: where do AI and recruiter agree, where do they diverge, and are the divergences acceptable or concerning? That outcome determines whether and how the system is deployed more broadly.
Comparison and misconceptions
A POC tests whether an idea works in practice; a pilot already runs the idea as a real service at small scale. A POC is focused on learning and deciding; a pilot is focused on refining and scaling. Always start with a POC before setting up a pilot.
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