Running an AI Proof of Concept: A Guide to Successful Pilots

Proof of Concept (POC) is where AI claims meet reality. A well-designed POC reveals whether a vendor can actually deliver against your requirements. A poorly designed one wastes time and produces misleading results. This guide ensures your POC generates actionable insights.

Executive Summary

• POC validates vendor claims with your actual data and use cases before major investment
• Define success criteria upfront—don't evaluate POC results against undefined standards
• Use your real data (or realistic synthetic data)—vendor sample data proves nothing
• Time-box POCs (2-4 weeks typically) to prevent scope creep and maintain momentum
• POC is not free implementation—it's a focused test of specific capabilities
• Involve end users in POC evaluation—technical success doesn't equal adoption success
• Be prepared to fail POC vendors—that's better than failed production implementations
• POC results inform negotiation—strong results justify investment; weak results provide leverage

Why This Matters Now

Vendor demos are choreographed performances. Marketing claims are optimistic. Customer references show best-case scenarios. POC is your chance to see how the AI actually performs with your specific data and requirements.

Skipping or shortcutting POC leads to:

• Discovering limitations after contract signing
• Implementations that don't meet expectations
• Damaged credibility for AI initiatives
• Expensive course corrections

The cost of a thorough POC is trivial compared to the cost of a failed implementation.

Definitions and Scope

Proof of Concept (POC): A small-scale test to validate that a solution can achieve desired outcomes in your environment.

Pilot: A more extensive deployment to a subset of users, often following POC.

Production: Full deployment to all intended users.

Scope of this guide: Designing and executing POCs for AI vendor evaluation—not internal AI development POCs.

POC Decision Framework

When to Run a POC

POC recommended:

• Significant investment (>$50K annually)
• Core business process dependency
• Unproven vendor or technology
• Complex integration requirements
• Performance claims that need validation

POC may be optional:

• Low-cost, low-risk point solution
• Well-established vendor with strong references
• Simple, well-understood use case
• Similar successful implementation at peer organization

When Not to Run a POC

Avoid POCs that:

• Test capabilities you don't actually need
• Don't have clear success criteria
• Can't use representative data
• Lack committed evaluation resources
• Are designed to fail to justify a predetermined decision

Step-by-Step POC Guide

Step 1: Define POC Scope

Questions to answer:

• What specific capabilities are we testing?
• What will prove the solution works for us?
• What's out of scope for this POC?

Scope elements:

• Use cases to test (prioritize 2-3)
• Data sets to use
• Integrations to validate
• Performance levels to achieve

Example scope statement:

POC SCOPE: [VENDOR NAME] Document Processing

Objective:
Validate [Vendor]'s ability to accurately extract data from our 
vendor invoices and integrate with our accounting system.

In Scope:
- Invoice data extraction (vendor, amount, date, line items)
- Integration with [Accounting System] API
- Processing of 500 sample invoices representing format variation
- Accuracy measurement against manual baseline

Out of Scope:
- Approval workflow configuration
- User interface customization
- Historical invoice reprocessing
- Purchase order matching

Step 2: Define Success Criteria

Success criteria must be:

• Specific (clear what success looks like)
• Measurable (quantifiable metrics)
• Achievable (realistic given POC constraints)
• Relevant (aligned to business requirements)

Example success criteria:

Step 3: Prepare Data

Data requirements:

• Representative of production volume and variety
• Includes edge cases and exceptions
• Covers different formats/sources
• Sufficient volume for meaningful test

Data preparation checklist:

• Identified data sources
• Obtained necessary approvals
• Anonymized sensitive data (if required)
• Created ground truth for accuracy measurement
• Documented data characteristics

Ground truth creation:

For accuracy testing, you need verified correct answers:

• Manually process sample subset
• Have two people independently verify
• Document the "right" answer for each item
• Use this to measure AI accuracy

Step 4: Plan POC Execution

Timeline:

• Typical duration: 2-4 weeks
• Milestones: setup, configuration, testing, evaluation

Resources:

• Vendor resources (implementation support)
• Internal technical resources (integration, data)
• Business users (evaluation, feedback)
• Project manager (coordination)

Example POC timeline:

Step 5: Execute POC

Setup phase:

• Provision POC environment
• Load test data
• Configure integrations
• Train POC users

Testing phase:

• Run test scenarios
• Measure accuracy and performance
• Document issues and observations
• Gather user feedback

Daily standup:

• Review progress and blockers
• Adjust approach if needed
• Keep vendor accountable

Step 6: Evaluate Results

Quantitative evaluation:

• Accuracy vs. ground truth
• Performance vs. targets
• Volume handling
• Error rates

Qualitative evaluation:

• User experience
• Vendor responsiveness
• Integration quality
• Exception handling

Example evaluation template:

Step 7: Make POC Decision

Decision options:

1. Proceed: POC successful, move to contract/implementation
2. Conditional proceed: POC mostly successful, address specific gaps
3. Extend POC: Need more testing to reach decision
4. Fail: POC unsuccessful, do not proceed with this vendor

Decision framework:

Common Failure Modes

1. Undefined Success Criteria

Problem: No clear standard for evaluating POC results Prevention: Define specific, measurable criteria before POC starts

2. Vendor-Controlled Data

Problem: POC uses vendor's perfect sample data Prevention: Insist on your data; vendor sample data is supplementary at most

3. POC Scope Creep

Problem: POC expands to cover everything, never concludes Prevention: Time-box POC, enforce scope boundaries

4. No Ground Truth

Problem: Can't measure accuracy without known correct answers Prevention: Create ground truth data before POC starts

5. Limited User Involvement

Problem: Technical success but poor user experience Prevention: Include end users in evaluation from the start

6. Vendor-Optimized Configuration

Problem: Vendor spends POC hand-tuning for your specific test cases Prevention: Test on data vendor hasn't seen; include holdout set

7. POC Theater

Problem: POC designed to justify predetermined decision Prevention: Honest evaluation against objective criteria

POC Checklist

Planning:

• Defined POC scope and objectives
• Established specific success criteria
• Prepared representative data
• Created ground truth for accuracy testing
• Allocated resources (vendor and internal)
• Set timeline and milestones

Execution:

• Set up POC environment
• Configured solution and integrations
• Ran test scenarios
• Measured against success criteria
• Gathered user feedback
• Documented issues and observations

Evaluation:

• Analyzed quantitative results
• Evaluated qualitative factors
• Compared results to success criteria
• Identified gaps and risks
• Made go/no-go recommendation

Metrics to Track

Tooling Suggestions

POC environment: Vendor-provided sandbox, cloud environment, isolated test system Data management: Secure data sharing, anonymization tools Measurement: Accuracy calculation tools, performance monitoring Collaboration: Project management, documentation, communication

FAQ

Q: Who pays for the POC? A: Practices vary. Vendors often provide free or reduced-cost POC to win business. For significant POCs, cost sharing or paid POC may be appropriate.

Q: How much data do we need for meaningful results? A: Depends on variability. For document processing, 200-500 representative samples is often sufficient. For classification, need coverage of all categories.

Q: What if POC results are borderline? A: Dig into the details. Understand why criteria weren't met. Determine if gaps are fixable. Consider extended POC or conditional proceed.

Q: Can we run POCs with multiple vendors simultaneously? A: Yes, parallel POCs are common. They're more efficient but require more resources. Ensure fair and consistent evaluation.

Q: What if the vendor resists our data requirements? A: Red flag. Vendors confident in their solution should welcome testing with real data. Investigate why they're hesitant.

Q: How do we handle POC findings in contract negotiation? A: Strong POC results justify investment; weak results provide negotiating leverage. Document POC findings and reference in negotiations.

Next Steps

A rigorous POC separates vendors who can deliver from those who can only demo. Invest in POC design—clear criteria, representative data, honest evaluation—to make confident procurement decisions.

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References

• Gartner: "Best Practices for AI Proofs of Concept"
• McKinsey: "Getting AI Projects Off the Ground"
• MIT Sloan: "Winning With AI Pilots"
• Harvard Business Review: "Building the AI-Powered Organization"