What's the difference between a PoC and a pilot?

A PoC (proof of concept) validates technical feasibility—can AI actually do this? A pilot validates organizational fit—will it work for our team and customers? PoC comes first (1-2 weeks), then pilot (4-8 weeks). PoC answers 'can we?' while pilot answers 'should we?'

How do I know if my PoC succeeded?

Define success criteria before starting: AI achieves >X% accuracy on test cases, average response time <Y seconds, handles Z% of cases without human help, or produces output indistinguishable from human. If all criteria met, proceed to pilot. If not, adjust approach or abandon the use case.

What data do I need for a PoC?

PoC needs representative sample data: 50-100 example inputs (questions, documents, tasks), expected outputs for each (correct answers), edge cases (unusual inputs), and variety (don't just test the easy stuff). You don't need all your data, just enough to validate the approach works.

How Do I Run an AI Proof of Concept?

Q: What data do I need for a PoC?

PoC needs representative sample data: 50-100 example inputs (questions, documents, tasks), expected outputs for each (correct answers), edge cases (unusual inputs), and variety (don't just test the easy stuff). You don't need all your data, just enough to validate the approach works.

A proof of concept answers one question: can AI actually solve this problem? Here's how to run one properly.

PoC vs Pilot vs Production

Stage	Question	Timeline	Cost
PoC	Can AI do this?	1-2 weeks	¥100k-300k
Pilot	Will it work for us?	4-8 weeks	¥500k-1M
Production	How do we scale?	4-12 weeks	¥1M-5M

PoC Steps

Define the problem: What specific task should AI handle?
Set success criteria: How will you measure success?
Prepare test data: Representative examples
Run tests: AI processes test cases
Evaluate: Did it meet criteria?
Decide: Proceed, pivot, or stop

Defining Success Criteria

Be specific before you start:

Use Case	PoC Criteria
FAQ Chatbot	>80% accuracy on 100 test questions
Email drafting	70% of drafts accepted with minimal edit
Document extraction	>90% fields extracted correctly
Classification	>85% correct categorization

Preparation: Sample Data

You need representative test cases:

Sample size: 50-100 examples minimum
Variety: Easy, medium, hard cases
Edge cases: Unusual inputs that might break AI
Ground truth: Correct answers for each test case
Real data: Actual examples, not made up

Running the PoC

Evaluation process:

Process test cases: Feed inputs to AI
Capture outputs: Save all AI responses
Human evaluation: Compare to expected outputs
Categorize: Correct, partially correct, wrong
Error analysis: Why did failures happen?

Interpreting Results

What different outcomes mean:

>90% success: Ready for pilot
70-90% success: Proceed with optimization
50-70% success: Needs improvement, may not be viable
<50% success: Wrong approach or not AI-suitable

Common PoC Issues

Issue	Diagnosis	Fix
Low accuracy	AI can't handle task	Different model or approach
Inconsistent results	Prompt instability	Better prompt engineering
Missing context	Knowledge base gaps	Add more documentation
Too slow	Model/architecture issue	Switch to faster model

Go/No-Go Decision

After PoC evaluation:

Go: PoC met success criteria → proceed to pilot
Iterate: Close to criteria → optimize and re-test
Pivot: Different approach might work → new PoC
No-Go: Fundamentally doesn't work → abandon path

Documenting the PoC

Capture for stakeholders:

Test cases and results
Success metrics achieved
Error patterns identified
Recommendations for next steps
Estimated timeline and cost for pilot

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