A Practical Guide to Managing GenAI POCs: From Hypothesis to Handoff

Even after years of managing SaaS and AI projects, I’ll admit: GenAI POCs are a different beast.

Too often, I’ve seen the same pattern: someone builds a flashy demo, everyone nods in the meeting… and then nothing happens. No decision. No next steps. No product.

If you’re managing a GenAI initiative, especially in a technical PM role, this blog is for you. I’ll break down how to run a POC that actually answers real questions, moves the team forward, and avoids wasting time. Plus, I’ll share a checklist of what you should walk away with, and why it matters.

Let me be upfront: I’m still figuring things out, but here’s what’s working so far.

🎯 Step 1: Define the Point of the POC

Every GenAI POC should start with one clear, grounding question:

What uncertainty are we trying to reduce?

Too often, GenAI projects begin with curiosity, “Let’s see what the model can do!” That’s fine in early exploration, but if you’re not clear on what you’re trying to learn, you’ll likely end up with a slick prototype… and no clear decision.

I’ve found it useful to frame the POC around one (or more) of these core areas:

• Feasibility – Can the model handle this task reliably using our data?
• Fit – Does this actually work within our product, workflow, or technical stack?
• Constraints – Are there blockers around latency, cost, data privacy, or compliance?
• Value – Will this add meaningful impact for users or the business?

If your POC doesn’t aim to answer at least one of these, it’s worth pausing and rethinking the scope.

Because at the end of the day, a GenAI POC shouldn’t just generate excitement, it should reduce risk and help the team move forward with confidence.

🚫 What Usually Goes Wrong (Been There Myself)

I’ve run into these issues myself, and watched plenty of teams fall into the same traps:

• Unclear goals. The demo looks impressive, but no one knows what it was meant to prove or what decision it’s supposed to inform.
• Over-scoping. What starts as a focused experiment turns into a half-baked product. Suddenly, you're debugging edge cases instead of testing a hypothesis.
• No success criteria. Without defining what “good enough” looks like, you can’t objectively assess the results, and you’re stuck arguing opinions.
• Stakeholder confusion. People assume the demo is a finished, scalable solution. They don’t see the manual patchwork behind it. (More on that in the next section.)

Each of these can derail momentum, waste time, or worse - lead to decisions based on assumptions instead of insights.

⚠️ Common Pitfall: Clients Think the POC Is the Product

This one comes up a lot, especially when you’re working with external clients or internal stakeholders who aren’t deep in the technical details.

You run a GenAI demo where GPT summarizes data or answers questions. The output looks slick. The reactions are instant:

“Awesome! Can we roll this out next sprint?”

The problem? What they just saw was a carefully controlled, manually tuned, hardcoded experiment. It’s not scalable. It’s not secure. It’s not production-grade. But because the responses look fluent and intelligent, it creates a false sense of maturity.

Over time, I’ve learned to handle this more proactively:

• Set expectations early. Say clearly: “This is a proof of concept and not a finished product.” Repeat it if needed.
• Document the hacks. Call out what's hardcoded, manually reviewed, or stitched together just to get through the demo.
• Highlight the missing pieces. Be explicit about what's not there yet: authentication, error handling, logging, observability, safety rails, data governance.
• Don’t over-polish the UI. A slick front-end can unintentionally send the wrong signal — that it’s ready to ship. Sometimes, a rough prototype sets better boundaries.

GenAI demos are meant to impress, and that’s fine. Just make sure clarity isn’t sacrificed for showmanship. The more realistic you are about what the POC is (and isn’t), the smoother your path to productization will be.

📦 What a “Good” GenAI POC Should Deliver

If your GenAI POC doesn’t leave behind clear, usable documentation, you’re not just wasting time — you’re forcing future teams to relearn the same lessons.

To avoid that, I’ve started organizing POC outputs into two categories:

• 🛠️ Technical Artifacts → Help engineers validate, improve, or productionize the concept later.
• 📋 Non-Technical Artifacts → Help stakeholders understand outcomes and make confident, informed decisions.

🛠️ Technical Artifacts

These are critical for ensuring continuity across teams. They help engineers and data teams avoid reinventing the wheel — and surface risks early before they become blockers.

📌 Prompt Setup

Why it matters: Prompts are at the heart of most GenAI logic. Capturing what worked — and what didn’t — helps future teams iterate faster and avoid dead ends.

Include:

• Final versions of prompts
• System messages and prompt structure
• Few-shot examples (if used)
• Notes on failed or low-performing prompt variations

📌 Model + Infrastructure Details

Why it matters: Engineers need to know which model was tested, how it was accessed, and whether performance met acceptable thresholds for latency, cost, or availability.

Include:

• Model name and version (e.g., GPT-4, Claude 3, Mistral)
• Hosting method (API, managed service, self-hosted)
• Token usage, rate limits, and latency stats
• Cost breakdown for inference or integration

📌 Test Data + Outputs

Why it matters: Reproducibility matters — especially when productizing. Sample inputs and outputs help teams understand real behavior, edge cases, and inconsistencies.

Include:

• Representative test inputs (anonymized where needed)
• Sample outputs: strong, weak, and “weird” cases
• Edge-case testing notes or known failure modes

📌 Known Risks + Limitations

Why it matters: Helps prevent surprises during integration. Identifying gaps early protects engineering from avoidable rework and helps product teams set realistic expectations.

Include:

• Where the model struggled (hallucinations, ambiguity, fragility)
• Any hardcoded logic or manual workarounds used in the demo
• Assumptions that won’t hold in production (e.g., fixed input structure, pre-cleaned data)
• Red flags around security, bias, or compliance

📋 Non-Technical Artifacts

These artifacts are just as important as the technical ones. They ensure alignment across product, business, and leadership, and make sure the POC doesn’t die in ambiguity. Without them, even strong technical results can get lost in translation.

📌 POC Goal Statement

Why it matters: A clear goal keeps the team focused and prevents scope creep. It also provides a benchmark for evaluating whether the POC succeeded or not.

Include:

• What are we testing?
• Why are we testing it now?
• What decision will this POC help inform?

📌 Evaluation Criteria

Why it matters: Without defined success metrics, GenAI POCs can easily devolve into subjective opinions. Clear criteria ensure decisions are grounded in evidence, not gut feel.

Include:

• Accuracy or performance benchmarks
• Latency, cost, or usability thresholds
• Alignment with business value
• Qualitative signals (e.g., stakeholder or user feedback)

📌 Decision Summary + Recommendation

Why it matters: Don’t assume everyone saw the final demo or understands the outcome. This artifact documents what was learned, and what’s next.

Include:

• Go / no-go decision
• Key takeaways or lessons
• What’s required to move forward (e.g., data access, integration work, stakeholder buy-in)
• Suggested next step or roadmap action

📌 Stakeholder Communication Notes

Why it matters: Many POCs stall not because of technical flaws, but because of misaligned expectations. Capturing stakeholder feedback early prevents surprises later.

Include:

• What leadership or sponsors liked (or questioned)
• Misconceptions or unrealistic expectations to clear up
• Promises made, blockers raised, or risks flagged during reviews

🧠 Pro Tip: Package the POC Like a Mini Case Study

After wrapping up, bundle these artifacts into a short, structured summary, think of it as a lightweight internal case study.

You’ll thank yourself when someone asks three months later, “Didn’t we test that already?” or when a new PM joins the team and wants to pick up the thread. 😉