Most PoCs
never graduate.
Industry estimates put the share of enterprise AI proofs-of-concept that reach production somewhere between a quarter and a half. The technical PoC succeeds; the production deployment never lands. This page is an honest read on why - and on what has to be true for a PoC to graduate. If you are at the PoC stage, the answer is probably not buying hardware yet.
Four things a PoC is well-suited to validate.
A PoC is a fast, narrow experiment. It is best at answering specific technical questions whose answers can change the project's direction. Run with discipline, four PoCs answer four questions worth knowing.
Validates · 01
Technical feasibility
Can a current model, given representative data, produce output of the required quality at the required latency? This is the question PoCs are best designed for.
Answers
Yes (with this model, this prompt, this dataset, this scale)
Validates · 02
Quality envelope
On the workload as scoped, what accuracy / F1 / quality score does the model produce? What is the failure mode when it fails? What does the error distribution look like?
Answers
A measured number with confidence intervals
Validates · 03
Latency and throughput floor
Under representative load, what response time and throughput does the deployment achieve? Where is the bottleneck - model, retrieval, network, queue?
Answers
Measured numbers under representative conditions
Validates · 04
Integration shape
What data sources does the deployment need to read from? What systems does it need to write to? What identity / access boundaries are crossed? Sketched, not solved.
Answers
A diagram and an honest complexity estimate
Five things a PoC cannot tell you.
Every one of these has been the rock that a successful technical PoC has wrecked itself on at some organisation in the last twelve months. Knowing what the PoC does not answer is at least as valuable as knowing what it does.
Cannot tell you · 01
Whether the business case stands at production scale
A PoC running on a hand-curated dataset with a small user group does not tell you whether the deployment delivers ROI on production data with the full user base. The business case is a separate analysis.
Cannot tell you · 02
What the production cost actually looks like
PoC compute costs are not production compute costs. Real users hit the system irregularly, with data that is messier than the PoC dataset, in volumes that are higher. Cost extrapolation from a PoC is a known source of large surprises.
Cannot tell you · 03
Whether the people will actually use it
A PoC tested by the project team is not a production deployment with the actual end-users. Adoption, change management, and workflow integration are the dominant predictors of whether the value lands - and the PoC tests none of them.
Cannot tell you · 04
How the model behaves over time
A PoC is a snapshot. Models drift. Data drifts. User behaviour drifts. The reliability profile of a model in week one is rarely its reliability profile in month six. PoCs cannot test this.
Cannot tell you · 05
Whether the ops burden is sustainable
A PoC has the team's full attention. A production system has whatever ops capacity the organisation can spare. A PoC that runs because three engineers are watching it is not a deployment that runs without them.
Five reasons enterprise AI PoCs don't graduate.
In order of how often we hear them named. Each reason gets a fix that, if applied at PoC kickoff rather than at PoC end, materially changes the graduation rate.
Reason
01
The PoC validated tech that nobody had business-cased
The most common failure. Engineering ran a successful technical PoC for an AI feature whose business value was sketched on a slide rather than scoped properly. When the question moves from "does this work?" to "is this worth deploying?" - nobody has the answer ready.
Fix at kickoff
Run the business case in parallel with the PoC, not after it. Decide before you start what production economics make this worth deploying.
Reason
02
The data was clean for the PoC and isn't for production
The PoC used a hand-prepared sample. The production deployment has to handle the messy long-tail - bad encodings, broken metadata, missing fields, mixed-language content, formatting drift over time. The model that hit 92% accuracy on the PoC sample hits 70% on the production firehose.
Fix at kickoff
Test the PoC on a representative sample of production data, not the easy 10%. If your data is too messy for that, the data work is the project - not the model.
Reason
03
Integration complexity exceeded the implicit budget
The PoC was a notebook. The production deployment needs identity, access control, audit logging, monitoring, alerting, error handling, retry logic, and integration with three internal systems. The implicit assumption that "now we wrap it in an API and ship it" is rarely cheap.
Fix at kickoff
Sketch the production integration architecture during the PoC, not after. Get realistic estimates from the team that owns the systems being integrated.
Reason
04
Nobody owned the ops handover
The PoC was run by an AI consultancy. Production needs an operator - someone who runs the system, monitors it, retrains, escalates incidents. If the ops handover wasn't scoped at the start, it gets scoped under deadline pressure and doesn't go well.
Fix at kickoff
Identify the production ops owner before the PoC starts. The ops owner participates in the PoC enough to know what they're inheriting.
Reason
05
The business unit changed direction
AI projects often run six to nine months from PoC start to production. In that time, the sponsoring business unit reorganises, refocuses, or just loses the executive who championed the project. The technically-successful PoC has nowhere to land.
Fix at kickoff
Shorter PoCs (six to eight weeks). Tighter executive accountability. A go/no-go decision at the PoC end with a named decision-maker.
Six things that have to be true.
A PoC graduates when these six are in place. Not five - six. The temptation to wave through a PoC that has four of six is real, and the result is the late-stage failure modes named above.
Criterion · 01
Measured quality on representative production data
Not the curated PoC dataset - a sample drawn from real production traffic, including the messy long-tail. Quality target is met, with documented failure modes.
Criterion · 02
Production cost model with sensitivity analysis
What does the deployment cost at 1×, 5×, and 20× the PoC scale? Cost dominates which way as scale grows? What happens to the business case at each level?
Criterion · 03
Named production owner
A specific person, in a specific team, with a budget line for ongoing ops. Not "the AI team" - a person.
Criterion · 04
Integration architecture scoped
A real diagram of the production system, with each integration owned by the team responsible for it, with realistic time / cost estimates.
Criterion · 05
Evaluation harness in place
A repeatable evaluation that can be run against any model version on the standard dataset, producing a comparable score. Without this, future model swaps are hope.
Criterion · 06
Go/no-go made by a named decision-maker
A specific executive, with the authority to commit the production budget, has read the PoC report and decided. Either decision is acceptable; "we'll think about it" is not.
When buying hardware makes sense.
During the PoC, the answer is almost always no. PoCs run quickly on whatever compute is at hand - a workstation, a cloud GPU, an existing rented cluster. Buying production-grade hardware to run a PoC adds capex risk to a project whose own validation is incomplete.
The hardware conversation makes sense once the PoC has graduated - when the workload, the scale, the data sensitivity, and the production owner are all settled. At that point, the buying guide takes over: which stage of investment fits the workload, what footprint the deployment needs, what hardware specification matches.
One exception: if the PoC has to run on data that cannot leave your perimeter - sovereign data, regulated material, IP that a cloud-API would absorb - then the hardware question shows up earlier. A starter-scale node bought for the PoC keeps the data inside the building and becomes the development environment after the production node lands.
See the buying guideWhere this fits.
The journey
Deploying AI in your business
Six phases from discovery through ops. The PoC is Phase 3; this page lives inside that phase.
After graduation
AI infrastructure buying guide
Once the PoC has graduated, the hardware conversation makes sense. Five stages of investment, with realistic spend ranges.
Primer 02
AI workload patterns
Five shapes most enterprise AI deployments take. The pattern is what the PoC actually validates.
PoC graduated, or planning the next one?
Tell us where you are in the cycle. If the PoC graduated, we'll route you to a partner who handles production hand-off well. If you're scoping the next PoC, we'll route you to an AI consultancy who runs disciplined ones.