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Running an AI Proof of Concept: Lessons from the Field

August 20, 2025•5 min read•Team 400

"Let's do a PoC."

Famous last words. I've seen dozens of AI proofs of concept. Some proved concepts and led to successful deployments. Others proved nothing and wasted months of effort.

Here's what separates the two.

The Purpose of a PoC

A proof of concept should answer specific questions:

Can we? Is this technically feasible with our data, systems, and constraints?

Should we? Does this deliver enough value to justify the investment?

How? What approach, architecture, and resources are needed?

A PoC that doesn't answer these questions isn't proving anything—it's just experimenting.

What Goes Wrong

The Endless PoC

Signs you're in one:

  • PoC keeps extending ("just one more feature")
  • Scope creeps without clear rationale
  • No defined endpoint
  • Stakeholders lose interest

The PoC becomes a permanent state, never graduating to production or getting killed.

Fix: Define clear success criteria and timeline upfront. If you don't meet them, make a decision.

The Demo PoC

Signs:

  • Works great in controlled conditions
  • Falls apart with real data
  • Built to impress, not to prove
  • Nobody asks hard questions

A demo is marketing. A PoC is a test. They're different.

Fix: Use real data. Test edge cases. Try to break it. Involve skeptics.

The Orphan PoC

Signs:

  • Nobody owns the outcome
  • Built by a side team with no path to production
  • No business owner asking hard questions
  • Technical success but no business adoption

Fix: Assign clear ownership. The business owner should care about the result, not just watch from the sidelines.

The Premature Scale PoC

Signs:

  • Over-engineered architecture
  • Enterprise integration before proving value
  • Security review before basic feasibility
  • Months of work before any validation

Fix: Start scrappy. Prove the core concept first. Add enterprise requirements when you know it's worth it.

Running a Good PoC

Phase 1: Define Success (Week 0)

Before starting, answer:

What question are we answering? Not "can AI help us?" but "can AI process our invoices with 90%+ accuracy?"

What does success look like? Specific metrics. Numbers. Thresholds.

What does failure look like? Equally important. When do we stop?

What happens after success? If it works, what's the path to production?

What happens after failure? If it doesn't work, what did we learn?

Document this. Get stakeholder sign-off. Refer back to it when scope creeps.

Phase 2: Rapid Prototype (Weeks 1-3)

Build the minimum needed to test the hypothesis.

Do:

  • Use real data (anonymised if needed)
  • Focus on the hard part (the thing you're actually proving)
  • Cut corners on everything else
  • Involve end users early

Don't:

  • Build production-ready infrastructure
  • Perfect the UI
  • Handle every edge case
  • Worry about scale

At this stage, spreadsheets and manual processes are fine. You're testing the AI capability, not the software.

Phase 3: Validate (Weeks 3-4)

Test against your success criteria:

Quantitative validation:

  • Accuracy against labelled test set
  • Processing time
  • Resource costs
  • Error rates by category

Qualitative validation:

  • End user feedback
  • Stakeholder reactions
  • Identified gaps and issues

Be honest. If it's not working, that's valuable information.

Phase 4: Decision (Week 4)

Based on results:

Proceed to production: PoC proved the concept. Here's the implementation plan.

Pivot and try again: Core idea valid but approach needs change. Here's the revised hypothesis.

Kill it: Doesn't work, or doesn't deliver enough value. Here's what we learned.

Making a clear decision is part of the PoC. "Let's keep investigating" is usually not the right answer.

PoC Scope

Keep it narrow enough to execute quickly, broad enough to prove something meaningful.

Good PoC Scope

"Test whether AI can accurately extract invoice data (vendor, amount, date, line items) from our 50 most common invoice formats with 95%+ accuracy."

  • Specific task
  • Defined data set
  • Clear metric
  • Achievable in weeks

Bad PoC Scope

"Explore how AI could improve our finance operations."

  • Too broad
  • No success criteria
  • Could go anywhere
  • Will take forever

Data Reality

AI PoCs often fail because of data, not AI.

Data Questions to Answer Early

Do we have the data? Not theoretically—actually. Where is it? Can we access it?

Is it usable? Format, quality, completeness. Have you looked at it?

Is it representative? Does your PoC data reflect production reality?

Can we label it? If you need training data, who creates it?

Spend a week on data assessment before building anything. If the data isn't there, you can't PoC your way out of that.

Stakeholder Management

Who Needs to Be Involved

Executive Sponsor: Provides air cover, makes resource decisions Business Owner: Defines success criteria, validates results Technical Lead: Makes architecture decisions, assesses feasibility End Users: Provide real-world input, test usability IT/Security: Flag constraints early (better now than later)

What They Need from You

Regular updates: Brief, honest, focused on decisions needed Early warning: If it's not working, say so before the demo Clear recommendations: Not just findings, but "here's what we should do"

PoC Budget and Timeline

For most AI PoCs:

Timeline: 4-6 weeks is usually right. Shorter doesn't prove enough. Longer loses momentum.

Budget: $20,000-$60,000 for external help. More for complex scenarios. Less for internal experiments.

Team: 1-2 people focused, not 6 people part-time

If your PoC plan is 6 months and half a million dollars, you're planning a project, not a PoC.

From PoC to Production

The gap between PoC and production is larger than most people expect.

PoC-to-production typically requires:

  • Re-architecture for scale and reliability
  • Security hardening
  • Integration with production systems
  • Proper error handling and monitoring
  • User training and change management
  • Compliance review (if applicable)

Budget for this. PoC cost × 5-10 is a reasonable starting estimate for production.

Our Approach

We've run AI proofs of concept across industries. Our typical PoC engagement:

Week 0: Success criteria definition, data assessment Weeks 1-3: Rapid prototype with real data Week 4: Validation, results presentation, recommendation

We're direct about results. If it works, we'll tell you how to proceed. If it doesn't, we'll tell you why and what alternatives exist.

PoCs should clarify decisions, not defer them.

Talk to us about running an AI proof of concept.