How to Run an AI Proof of Concept That Doesn't Waste Two Months

Most AI proofs of concept fail before the code starts. Not because the model won't work — modern models handle a shocking range of tasks — but because nobody agreed on what "works" would even look like. This post is about fixing that.

At Augere Labs we run a lot of two-week AI POCs. The pattern that keeps working is small, opinionated, and unromantic. Here it is.

The problem with most AI POCs

The typical POC goes: someone in leadership reads an article, someone in engineering builds a demo, everyone agrees it "looks amazing," and then the project stalls. Six months later there's no decision, no shipped feature, and no lesson learned that couldn't have been learned in two weeks.

Three things usually go wrong:

• The success criterion is a vibe, not a metric.
• The demo dataset is the happy path only.
• Nobody defined what "kill this idea" would look like.

A framework that fits in two weeks

Day 1–2: Define the question, then define the answer

Write down, before any code: what business question this POC answers, what number would be a "yes," what number would be a "no," and who has the authority to call it. If those four things aren't clear, don't start the POC — the discovery isn't done yet.

Bad question: "Can AI help with customer support?" Good question: "Can an AI agent handle at least 40% of tier-1 tickets end-to-end, with under 5% incorrect escalations, on our real ticket history?"

Day 3–4: Get the real data

The biggest single predictor of a POC that leads to a decision is whether it ran on real data instead of curated examples. Real data is messy, contradictory, incomplete. The whole point of a POC is to meet that messiness.

Anonymize, sample, but use the actual thing. If you can't get real data in four days, that itself is a finding — and probably a bigger problem than the AI question.

Day 5–8: Build the minimum honest system

Not the final architecture. The minimum honest system: retrieval if the task needs context, a prompt, a model call, output parsing, and a way to run it over the sample data. Skip caching, skip fancy UI, skip evaluations that aren't the success metric.

In projects like these, half a page of Python beats a full framework. The goal is to get honest numbers, not to build a foundation.

Day 9–10: Grade the outputs

Take 50 to 100 real inputs. Run them through the system. Grade the outputs against the success criterion — by hand if necessary. This day is where the POC actually earns its value.

The grading has to include the messy cases. Grade 20 clean examples and 20 hard ones. If the system passes the clean and fails the hard, you know what "shippable" would take.

Day 11–12: Write the decision doc

Not a slide deck. A two-page document: what we tested, what we found, what the number was, what would need to be true to ship this, and a recommendation. Include the examples that failed — those are usually more useful than the ones that worked.

Day 13–14: Present and decide

One meeting, one hour, one decision: build it, kill it, or do a second POC with clearer scope. If the decision can't be made in that meeting, the framing was wrong.

Where teams get this wrong

Building a UI before proving the model

A pretty UI hides model failures under the appearance of polish. Prove the model works first with the ugliest possible interface — a script, a CSV, a Jupyter notebook. UI comes after the "yes."

Testing on synthetic data

An AI POC that only worked on made-up examples has proven nothing. The messiness is the test.

No success metric

"It looked promising" is not a POC outcome. If you don't have a number, define one before starting.

Confusing feasibility with fit

Almost every AI POC is technically feasible. The real question is whether it's worth building — cost, integration, workflow fit. Include those in the decision doc, not just accuracy.

The three POC outcomes and what to do with each

Clear yes

Move to a scoped build with a hard deadline. Not "let's productize" — a real six- to eight-week engagement with a shipping goal. The POC is done, and if you don't move quickly, the momentum leaks.

Clear no

Kill it and document why. This is a good outcome. A killed POC that saved you a three-month build is worth every dollar. Publish the reasons internally so the same idea doesn't come back reshaped in six months.

Ambiguous

The most common outcome and the one that traps teams. If the metric is 60% and you need 80%, the question is: what would it take to get from 60 to 80, and does that path look like weeks or months? If weeks, do a targeted second POC. If months, treat it like a "no" and revisit in a year when models improve.

Real example

A B2B logistics company wanted an AI feature that would extract structured data from shipping documents. The internal team estimated six months of work. We ran a two-week POC: real documents, target of 85% field-level accuracy, hand-graded on 100 samples.

The POC hit 82%. Close, but with a clear pattern — accuracy was excellent on typed documents and poor on handwritten ones. The decision was to ship the feature for typed documents only (about 70% of their volume) and skip handwritten. That decision changed the ROI math and shortened the build to eight weeks.

Without the POC, they would have committed to full coverage and either overrun the timeline or shipped something that only worked half the time.

Common misconceptions

"A POC needs to look production-ready." No. It needs to answer a question. Production polish is the wrong currency for a POC.

"A demo is a POC." A demo shows what could happen. A POC shows what does happen on real data. Very different.

"If the POC works, we can just extend it into the product." Sometimes yes, often no. POC code is usually rough for good reason — it was optimized for answering a question, not for maintenance. Budget a rewrite for the parts that need it.

Frequently Asked Questions

How long should an AI POC take?
Two weeks is the right target for most cases. If it needs more than three, the scope is probably too broad.

Who should run the POC?
A senior engineer with judgment, not a junior engineer trying to prove capability. The value is in the questions asked, not the code written.

How much should an AI POC cost?
Rough range for an outside team: $8K to $25K for a well-scoped two-week engagement. Cheaper if the scope is narrow, more if data access takes work.

What if leadership won't commit to a metric upfront?
That's the signal to slow down. Without a metric, the POC has no way to end. Push back until there's an agreement on what "success" means.

Where to go next

Once the POC succeeds, our post on shipping your first AI feature covers the productionization. If you're deciding between hiring in-house or working with an agency for the POC itself, see how to hire an AI development agency.

Working with us

Two-week AI POCs are one of the most common engagements we run. Talk to us if you have an idea that needs a real answer, not another slide deck.