How to Run a 30-Day AI Proof of Concept

An ai proof of concept in 30 days exists to answer one question: does this work on our data and is it worth the cost? To get there you need a narrow problem, a real sample, metrics fixed on day one, and the nerve to stop if results do not show up. Here is the week-by-week plan.

Prerequisites before you begin

• A specific problem, such as sorting quote emails or extracting fields from PDF purchase orders.
• A business owner with weekly time set aside.
• A sample of 100 to 300 real examples with the correct answer known.
• Permission to use that data, with confidentiality handled.

If any item is missing, fix it first. A POC without a real sample turns into a demo, not a test.

Week 1: scope and metrics

1. Describe the current process: who does it, how long it takes, where it breaks.
2. Write the target: for instance, at least 90 percent correct classifications and half the task time.
3. Define the unacceptable error, the one that kills the solution even if rare.
4. Split the sample in two: one part for tuning, one held back for the final test.

Set a ceiling on time and money. That keeps the POC from growing out of control.

Week 2: build the minimum version

Build the simplest flow that could work: input, AI, output. It can be a language model with well-written instructions and no integration at all. Resist wiring in the ERP, dashboards and notifications now.

Run the first part of the sample and log hits and misses. This week the goal is learning whether the path is viable, not whether it looks polished.

Week 3: tune and face the hard cases

Group the errors by category. Many come from vague instructions, badly formatted data or exceptions nobody documented. Fix, rerun and compare.

Also let someone from operations use the prototype in a controlled real situation. Their feedback exposes friction that a score sheet never shows.

Week 4: final test and decision

Run the held-back sample with no further tweaks. That is the number that counts. Measure accuracy, time, serious errors and estimated cost per use.

What to measure at the end

• Accuracy on the held-back sample.
• Number of serious errors.
• Task time with AI plus human review, versus today's time.
• Cost per processed unit and projected monthly cost.
• Effort required to integrate with your systems.

The go or no-go rule

Decide using the rules written in week one. If the target was met with no unacceptable error, move to a larger pilot. If it came close, define a short improvement cycle. If it fell short or the cost does not add up, stop without guilt: you spent 30 days to avoid an expensive project.

When a POC passes, the next stage is integration and operation, the territory of custom AI automation. If you want help running the test, reach out through contact.

Frequently asked questions

How much does an AI proof of concept cost?

It varies with complexity, but because it is short and narrow it usually costs far less than the final project. Set a time and spending cap before you start.

Why hold back part of the sample?

To keep the final test honest. If you tune the system on every example, the result looks optimistic and will not reflect new data.