AI Buying Checklist

AI Proof of Concept Evaluation Checklist for Pilot Teams

Use this AI proof of concept evaluation checklist to design a pilot with real examples, acceptance thresholds, reviewer feedback, risk checks, ROI evidence, and rollout decision criteria.

Updated 2026-06-243 buying gates5 red flags

Pilot design

A useful AI POC tests the real workflow, not a polished vendor demo.

Select historical examples that include normal cases, edge cases, exceptions, and failure-prone inputs.
Define acceptance thresholds for accuracy, time saved, reviewer effort, error rate, escalation, and user adoption.
Assign business, technical, security, and reviewer owners before testing starts.

Measurement and review

Capture both quantitative scores and qualitative reviewer evidence.

Track baseline time, AI-assisted time, correction time, exception rate, and confidence by case type.
Record false positives, false negatives, unsafe outputs, missing sources, and user override reasons.
Separate vendor support quality, implementation effort, and integration friction from model output quality.

Go or no-go decision

A POC should produce a clear rollout, redesign, or rejection decision.

Compare pilot value against total cost, adoption effort, support burden, and governance risk.
List blockers, acceptable gaps, required remediation, owner commitments, and rollout timeline.
Decide whether to approve, extend the pilot, negotiate, or reject the vendor.

Red flags

The pilot uses only vendor-provided examples.
There is no baseline for time, cost, accuracy, or error rate.
Reviewers cannot inspect source evidence behind AI outputs.
The POC ignores security, data policy, and integration friction.
The final decision is based on enthusiasm rather than measured acceptance criteria.

Evidence to collect

Pilot dataset, baseline metrics, acceptance criteria, reviewer notes, output examples, error categories, and adoption feedback.
Security findings, integration findings, support issues, pricing impact, and total cost assumptions.
Final decision memo with go, no-go, extend, or renegotiate recommendation.

How to use it

Turn the checklist into a buying decision

Step 1
Define the POC before vendor implementation starts.
Step 2
Use the same dataset and metrics for every vendor in the shortlist.
Step 3
Score pilot evidence with the vendor scorecard calculator.
Step 4
Turn accepted evidence into a business case and rollout checklist.

Related buyer paths

Use the next artifact

AI Software Buyer Guides

Open commercial AI software categories after the checklist identifies the workflow and owner.

AI Buying Templates

Turn checklist answers into an RFP, scorecard, security questionnaire, POC plan, or business case.

AI Governance Guides

Plan governance frameworks, risk assessments, vendor risk, model risk, compliance automation, and policy management.

AI Cost Guides

Estimate AI software, implementation, RAG, agent, chatbot, and document automation cost before approval.

AI ROI Guides

Calculate ROI, payback, automation savings, chatbot savings, agent ROI, and AI business case readiness.

AI Services Buyer Guides

Evaluate consultants, implementation partners, automation agencies, integration services, and enterprise AI advisors.

AI Vendor Scorecard Calculator

Convert evidence, risk, fit, and pilot results into a weighted vendor decision.

AI Proof of Concept Plan Template

Copy the full POC plan structure for owners, dataset, metrics, timeline, and acceptance criteria.

AI Software ROI Calculator

Translate pilot results into first-year ROI, payback, and finance approval assumptions.

How do you evaluate an AI proof of concept?

Evaluate an AI POC with real examples, baseline metrics, acceptance thresholds, reviewer feedback, security findings, integration effort, support quality, cost assumptions, and a written go or no-go decision.

How long should an AI POC run?

Many AI POCs can run for 30 to 60 days if the dataset, owners, integrations, and success metrics are defined before launch. High-risk regulated workflows may need longer review.