8 Practical Steps to Run an AI PoC for SMEs

8 Practical Steps to Run an AI PoC for SMEs — From Idea to Real Results

In recent years, Artificial Intelligence (AI) has become a hot topic across the Vietnamese business community. From large enterprises to small and medium-sized businesses (SMEs), many are talking about “bringing AI into operations.” Yet there is often a large gap between the idea and tangible results — many initiatives remain only on slides.

How can an SME test AI with a limited budget and low risk? The answer is a Proof of Concept (PoC): a lightweight, measurable experiment to validate whether an AI solution truly delivers value. Below are eight practical steps to run a PoC that moves you from idea to real results.

1. Define a clear business problem

Before thinking about technology, ask: What exactly should AI solve? Choose one concrete and measurable problem, for example:

Reduce order data entry time.
Automatically classify customer requests.
Forecast inventory to optimise purchasing.

A clear problem helps determine scope, goals, and success KPIs.

2. Assess your available data

AI runs on data. Check what you have:

Is data digitised (Excel, CSV, CRM, ERP)?
Do you have sufficient historical records to train or validate models?
Is data quality consistent or full of errors?

SMEs do not need exhaustive datasets at the start — a representative sample often suffices for a PoC. If data needs cleaning, the PoC is a good opportunity to prepare it.

3. Set PoC scope and KPIs

Keep the PoC short (typically 1–3 months) and limited to one process or department. Define measurable KPIs such as:

Time saved (%)
Error reduction (%)
Conversion or success-rate uplift (%)

Limiting scope reduces cost and simplifies evaluation.

4. Choose technology and partners

Most SMEs do not need to build AI from scratch. Consider:

No-code / low-code platforms (e.g., n8n, Make, Power Automate, or simple API integrations).
Managed cloud AI services (OpenAI, Google Vertex AI, Azure AI, or other providers).
Consultants or freelance experts for the PoC stage.

Pick technology that fits your budget and allows fast iterations.

5. Build a quick prototype

Focus on core functionality. The prototype can be:

An Excel sheet with automated macros,
A chatbot running on an existing platform,
A simple dashboard showing analysis output.

The goal is to prove the concept works with real data — not to build a polished final product.

6. Pilot and collect feedback

Run the prototype in a real environment. Record operation metrics and gather feedback from users (staff or customers). Capture both successes and limitations so you can iterate quickly.

7. Evaluate results with data

Compare outcomes to KPIs: performance improvements, model reliability, and user acceptance. Prepare a concise report that shows quantitative results and lessons learned. A clear report helps decision-makers judge whether to continue.

8. Decide: scale, adjust, or stop

After the PoC you have three options:

Scale: If KPIs and preliminary ROI are positive, plan a phased rollout.
Adjust: If potential exists but results are partial, refine the solution and run another iteration.
Stop: If costs or risks outweigh benefits, stop and document the reasons. A stopped PoC is still a valuable learning.

Practical note: A failed PoC can be a success if it prevents a costly full-scale project and clarifies what the organisation needs (data, processes, or skills).

Conclusion

Deploying AI does not require grandeur. For SMEs, a well-run PoC is the most practical path to validate value while containing risk. Start with a small, measurable use case, iterate quickly, and scale only when the results justify the investment.