AI Proof of Concept: Scope & Budget

An AI proof of concept (PoC) is a 2–6 week project — typically $5,000–$25,000 — that answers one question with evidence: can AI hit a defined performance bar on our real data? Done right, it converts "we think AI could help here" into a pass/fail result you can take to a budget meeting. Done wrong, it's a demo that impresses everyone and predicts nothing.

The difference between the two is decided before any code is written: on your real data or not, against measurable criteria or not. Here's the framework we use to scope PoCs that actually settle the question.

Why Run a PoC at All?

Because AI projects fail for reasons that are cheap to discover early and expensive to discover late. A full production build runs $25,000–$300,000+ (the full breakdown is in our AI development cost guide), and the most common failure causes — messy data, an accuracy ceiling below the usable threshold, edge cases that dominate real volume — are all detectable in weeks for a fraction of that.

A PoC is worth it when the production build would exceed ~$40,000, the use case is novel for your data, or stakeholders disagree about feasibility. The output you're buying isn't software — it's a decision: measured accuracy on your data, a map of the failure modes, and a grounded estimate for the production build.

Equally important: a "fail" is a successful PoC outcome. Spending $15,000 to avoid a $150,000 build that would have died on data quality is the best ROI in this entire field.

The 2–6 Week PoC Framework

Every credible AI PoC has the same four phases. The timeline flexes with data messiness, not ambition.

Week 0 — Scoping (before the clock starts). Pick one use case, one dataset, one metric. "AI for our operations" is not a PoC; "extract line items from our supplier invoices at 95%+ field accuracy" is. Agree the pass/fail bar in writing and secure access to real data — not a sanitized sample someone hand-picked.

Weeks 1–2 — Data reality check. Pull a representative slice of production data and profile it: formats, gaps, contradictions, edge cases. This is where a third of PoCs reveal their verdict early — the data can't support the use case yet. Discovering that in week 2 is the system working, not failing. If the data passes, build the evaluation set: 100–500 real examples with known correct answers.

Weeks 2–4 — Build the thin slice. Implement the narrowest path that exercises the core risk: prompt + retrieval pipeline, extraction chain, or baseline model. No UI polish, no auth, no integrations beyond what the test requires. The engineering question is singular: can the AI do the hard part?

Weeks 4–6 — Measure and decide. Run the evaluation set, score against the agreed bar, and categorize every failure (bad input data vs model limitation vs fixable pipeline issue — they have very different implications). Deliver a written result: the number, the failure analysis, a production estimate, and a build / fix-data-first / stop recommendation.

Success Criteria: The Part Most PoCs Get Wrong

If you take one thing from this article: define pass/fail before work starts, and make it a number. Weak criteria ("the bot gives good answers," "the demo impresses the exec team") guarantee an ambiguous result and a stalled decision. Strong criteria look like:

"≥95% field-level accuracy extracting totals, dates, and vendor names from 200 held-out invoices"
"Correctly answers ≥85% of the top 100 real support questions, with zero fabricated policy statements"
"Forecast error (MAPE) under 12% on the last two quarters of held-out sales data"
"Drafts responses agents accept with minor edits ≥60% of the time"

Three rules make these work. Tie the bar to economics, not perfection — figure out the accuracy at which the workflow actually saves money given the cost of catching errors; that's your bar, and it's often 90%, not 99%. Test on held-out data the builders never saw during development, or you're grading the model on its own homework. Pre-agree what each outcome triggers — pass → production proposal, near-miss → one scoped iteration, fail → stop. Without that, even a clean fail becomes "let's try a bit longer" and the PoC never ends.

What a PoC Should Cost

Typical market ranges we see:

PoC type	Typical budget	Duration
LLM feasibility (chatbot, summarization, drafting)	$5,000–$12,000	2–3 weeks
Document extraction / RAG over your content	$8,000–$20,000	3–5 weeks
Predictive ML (forecasting, scoring, detection)	$15,000–$25,000+	4–6 weeks

Two warnings at the edges. A "free PoC" from a vendor is a sales demo — it will run on cherry-picked data and be scoped to succeed, which is precisely the evidence you don't need. And a PoC quoted above ~$30,000 or beyond 8 weeks is a production build wearing a PoC badge; cut the scope. Budget roughly 10–20% of the expected production cost, and treat anything outside that band with suspicion.

PoC-to-Production Pitfalls

More AI value dies between PoC and production than in either phase. The classic traps:

The 80/20 inversion. The PoC handled the common cases; production must handle the weird ones — plus integrations, auth, monitoring, and failure handling. Production typically costs 4–8x the PoC. If your partner quotes 1.5x, they're planning to productionize the prototype, which leads to trap #2.
Shipping the prototype. PoC code has no error handling, no security review, no scalable pipeline. Budget a proper build that reuses the PoC's learnings (prompts, chunking strategy, model choice), not its codebase — this is standard practice in our AI and machine learning engineering work.
The integration gap. The PoC ran on an exported CSV; production needs live, permissioned access to your CRM or ERP. Integration is routinely half the production budget — scope it during the PoC, not after. (This is where AI apps and integration expertise earns its keep.)
No owner on the business side. PoCs are run by enthusiasts; production needs an owner accountable for the metric, or the system decays within months.
Pilot purgatory. A "successful" PoC with no pre-agreed next step joins the graveyard of impressive demos. This is why the outcome-triggers rule above matters: industry surveys consistently find that most AI pilots never reach production, and the missing decision framework — not the technology — is the usual cause.

When NOT to Run a PoC

A PoC has real costs — money, weeks, and stakeholder attention — and it isn't always the right first step:

The pattern is proven and cheap. Grounded FAQ chatbots and standard document extraction are solved patterns in 2026. If the build is under ~$20,000, a phased build with an early checkpoint beats a separate PoC.
You already know the data isn't ready. If the source data is demonstrably absent or inaccessible, you don't need a PoC to tell you — you need a data project first.
No budget exists beyond the PoC. If a pass couldn't be funded anyway, you're buying a decision nobody can act on. Secure conditional budget first.
It's being used to stall. A third consecutive PoC on the same use case is organizational avoidance, not diligence.

Frequently Asked Questions

How much does an AI proof of concept cost?

Typically $5,000–$25,000: LLM feasibility tests run $5,000–$12,000, document extraction and RAG PoCs $8,000–$20,000, and predictive ML PoCs $15,000–$25,000+. A useful rule of thumb is 10–20% of the expected production build cost. Be wary of free PoCs (sales demos on cherry-picked data) and of $40,000 "PoCs" (production builds in disguise).

How long should an AI PoC take?

Two to six weeks: roughly one to two weeks of data profiling and evaluation-set construction, one to two weeks building the thin slice, and a final week measuring against the agreed bar and writing up the decision. If the plan exceeds eight weeks, the scope includes production concerns — UI, integrations, hardening — that belong in the next phase.

What should the success criteria for an AI PoC be?

A measurable target on held-out real data, agreed in writing before work starts — for example, "95% field accuracy on 200 unseen invoices" or "85% of top support questions answered correctly with zero fabricated policies." Set the bar at the accuracy where the workflow becomes economically viable, not at perfection, and pre-agree what happens on pass, near-miss, and fail.

Why do so many AI pilots never make it to production?

The main causes are non-technical: no pre-agreed decision path after the pilot, no business-side owner for the metric, and underestimating the jump — production typically costs 4–8x the PoC once edge cases, integrations, security, and monitoring are included. Teams that scope the production estimate and integration map as PoC deliverables convert at far higher rates.

Can we reuse the PoC code in production?

Reuse the learnings, not the codebase. The validated prompts, retrieval strategy, model selection, and evaluation set carry directly into production and are most of the PoC's value. The code itself usually lacks error handling, security, and scalable architecture — rebuilding on proper foundations is faster and cheaper than hardening a prototype.

AI Proof of Concept: Validate Before You Invest