POC to Production

Executive Summary

The proof-of-concept is a hypothesis test, not a mini-production system. Most enterprise AI POCs succeed technically and still fail to reach production — not because the technology didn't work, but because the POC was designed without a clear production path, success criteria were not agreed in writing before execution, or the gap between POC constraints and production constraints was not surfaced until it became a blocker. This chapter defines a rigorous POC design methodology — scoping, success criteria, gap analysis, production migration planning — that treats POC execution as the first step in a production journey, not an isolated experiment. Understanding where POCs go wrong is the most direct path to ensuring they go right.

Learning Objectives

  • Design a POC scope that is both feasible within the time constraint and production-representative
  • Define written success criteria before POC execution begins
  • Identify and plan for the production-POC gap: the constraints that differ between POC and production environments
  • Build a go/no-go decision framework that prevents both premature production recommendations and indefinite POC extension
  • Execute the POC-to-production migration as a structured engineering process

Business Problem

Enterprise AI POCs occupy an ambiguous organizational position. They are funded as experiments but expected to demonstrate production viability. They are staffed at POC intensity but must produce insights about production complexity. And they are evaluated against criteria that are often not defined until after the POC is complete — which means the evaluation is subjective and the path to production is unclear.

The failure mode is predictable: POC succeeds by every measure the team chose to measure; production deployment stalls because the measures chosen did not include the factors that actually determine production viability. A POC that demonstrated AI output quality but did not measure physician adoption rate, integration latency under concurrent load, or security architecture compatibility has not demonstrated production readiness — it has demonstrated that the AI works in isolation.

Conceptual Explanation

A well-designed POC has three properties that are often in tension:

Feasible: Can be executed with the available time, data, and people. A 6-week POC scope for a 4-week engagement is not feasible.

Representative: Produces evidence about the conditions that matter for production success. A POC against synthetic data is feasible but not representative of production data quality.

Decision-enabling: Produces a clear signal that allows a go/no-go decision at the end. A POC without defined success criteria cannot produce a clear go/no-go decision.

The POC design process is the discipline of finding the intersection of these three properties for a specific use case and client environment.

Core Architecture: The POC Design Process

Step 1 — Define the POC Hypothesis

Every POC tests a hypothesis. Making the hypothesis explicit is the first design step:

Implementation code omitted in the Playbook edition. For complete code examples, production patterns, and advanced implementation details, see the Enterprise AI Technical Reference.

A hypothesis that cannot be stated in this form is not POC-ready. If the capability claim is vague ("AI generates good discharge summaries"), the success criteria will be unresolvable. If the data source is undefined ("our patient data"), the integration scope is unknown. Make the hypothesis concrete before writing a line of code.

Step 2 — Define Success Criteria in Writing

Success criteria must be defined and signed off by the client before POC execution begins. Verbal success criteria are renegotiated after the POC produces results.

Implementation code omitted in the Playbook edition. For complete code examples, production patterns, and advanced implementation details, see the Enterprise AI Technical Reference.

Step 3 — POC-to-Production Gap Analysis

The POC operates in a simplified environment. Production operates in a constrained environment. The gap between these two environments is the primary source of POC-to-production failures.

Map the gap explicitly before POC execution:

Implementation code omitted in the Playbook edition. For complete code examples, production patterns, and advanced implementation details, see the Enterprise AI Technical Reference.

Step 4 — POC Execution Plan

markdown
# POC Execution Plan — Discharge Summary AI
# Duration: 4 weeks
# FDE + Client Engineering Team

## Architecture Diagram

```mermaid
graph TD
    HYPO["Define POC Hypothesis\n(Capability + Data + Criteria + Constraint)"]
    CRIT["Define Success Criteria\n(Written sign-off before execution)"]
    GAP["POC-to-Production Gap Analysis\n(8 dimensions)"]
    PLAN["POC Execution Plan\n(4-week schedule + roles)"]

    subgraph "POC Execution"
        W1["Week 1: Environment + Integration"]
        W2["Week 2: Quality Iteration"]
        W3["Week 3: Clinical Evaluation"]
        W4["Week 4: Analysis + Planning"]
    end

    subgraph "Go/No-Go Decision"
        GO["GO — Proceed to Production Planning"]
        CGO["CONDITIONAL GO — Mitigation Plan"]
        EXT["EXTEND — More Data Needed"]
        NGO["NO-GO — Root Cause Analysis"]
        RDS["REDESIGN — New Iteration Cycle"]
    end

    PP["Production Planning\nMigration + Architecture"]
    LAUNCH["Production Launch"]

    HYPO --> CRIT --> GAP --> PLAN
    PLAN --> W1 --> W2 --> W3 --> W4
    W4 --> GO & CGO & EXT & NGO & RDS
    GO --> PP
    CGO --> PP
    EXT --> W3
    NGO --> HYPO
    RDS --> HYPO
    PP --> LAUNCH

Enterprise Considerations

POC portfolio management: FDE organizations with multiple concurrent POCs need portfolio visibility — which POCs are at what stage, which are at risk of stalling, and which are ready for production planning. A POC tracking system (even a simple spreadsheet) that captures POC stage, success criteria status, blocking issues, and estimated production date is essential.

POC cost transparency: POCs consume significant resources: FDE time, client engineering time, API costs, and organizational attention. The cost of a POC must be weighed against the expected value of the production deployment. POCs for use cases with uncertain production ROI should be designed to be shorter and cheaper — enough to validate the hypothesis before committing full resources.

Avoiding the perpetual POC: Some organizations run perpetual POCs — endless iterations that never produce a production decision. This is usually a symptom of unclear success criteria or organizational risk aversion. The go/no-go framework with a defined timeline prevents this pattern.

Healthcare Example

⊕ Healthcare Example

Educational Example — Illustrative POC. Not intended as clinical guidance.

A Reference Healthcare Organization discharge summary AI POC produces the following results at week 4:

  • Physician edit rate: 24% (below the 30% threshold — primary criterion met)
  • Section completeness: 97% (secondary criterion met)
  • Medication accuracy: 88% (secondary criterion met)
  • P95 latency: 22 seconds (secondary criterion met)

Decision: GO

Production planning begins with a 12-week migration plan: App Orchard review (running in parallel, 8 weeks), AI gateway production deployment (week 1), BAA finalization (week 2–3), MRB approval (week 4), shadow mode pilot (weeks 5–6), canary to 10% of hospitalists (weeks 7–9), full deployment (week 12).

Common Mistakes

1. Starting POC execution before success criteria are signed off. When POC results are mixed, unsigned success criteria become negotiation fodder. Get written sign-off before the first line of code.

2. POC scope that does not represent production constraints. A POC against synthetic data in a local environment with a single user has told you almost nothing about production viability. At minimum, use production-representative data and test under concurrent load.

3. Not involving the client's engineering team in POC execution. An FDE who builds the entire POC alone creates a system the client cannot maintain. POC execution must include the client engineers who will own the system in production.

4. Missing the production gap for Epic App Orchard. App Orchard review is a 6–12 week process. POC designs that assume immediate Epic production access will create a production delay that was entirely foreseeable in week 1.

5. No-Go without root cause analysis. A No-Go POC is valuable information, not a failure. The output should include a root cause analysis: was the data quality insufficient? Was the use case mismatched to the AI capability? Was the prompt under-engineered? A structured No-Go enables the next iteration.

Best Practices

  • Define the POC hypothesis before defining the scope
  • Get written sign-off on success criteria before starting execution
  • Map the production gap in week 1; don't discover it in week 8
  • Involve client engineering in POC execution from day 1
  • Include clinical evaluation in every healthcare AI POC — not just technical metrics
  • Define a clear Go/No-Go decision process with a date on the calendar before execution begins
  • Begin App Orchard submission immediately on POC start — it runs in parallel, not after

Trade-offs

Speed vs. rigor: A 2-week POC is faster but produces less evidence about production viability. A 6-week POC produces stronger evidence but delays the production decision. The right balance depends on the risk of a production failure vs. the cost of a longer POC.

Representation vs. access: Using production data in a POC produces the most representative results but requires BAA, PHI controls, and access provisioning. Using synthetic data avoids these requirements but produces weaker evidence. For healthcare AI, production-representative de-identified data in an Epic sandbox is the practical middle ground.

Interview Questions

Q: A POC produces mixed results — the primary success criterion is met but one secondary criterion is not. How do you structure the go/no-go conversation with the client?

Category: Behavioral Difficulty: Principal Role: FDE

Answer Framework:

A Conditional Go is a legitimate POC outcome — it means the core capability is validated but a production risk exists that requires a mitigation plan before launch. The go/no-go conversation should be structured, not improvisational.

First, present the results factually against the success criteria that were signed off before the POC. The signed criteria are the agreed evaluation framework; departure from them requires explicit justification. If the primary criterion is met, that is the most important finding.

Second, analyze the failed secondary criterion: is it a fundamental limitation (the AI cannot consistently achieve this metric), a data quality issue (the metric failed because of specific data gaps, not the AI capability), or an engineering gap (latency is too high because the demo gateway was under-provisioned, not because the AI is inherently slow)? Each has a different mitigation.

Third, propose a concrete mitigation plan for the gap — with a specific owner, timeline, and re-measurement mechanism. The production launch is conditioned on the mitigation being completed and validated.

The go/no-go decision is the client's, not the FDE's. The FDE presents the evidence and the recommendation; the client decides.

Key Points to Hit:

  • Present results against the signed success criteria — no surprises
  • Categorize the failure (fundamental / data quality / engineering)
  • Propose a specific mitigation plan with owner and timeline
  • Decision is the client's; recommendation is the FDE's

Red Flags:

  • Redefining success criteria post-POC to match results
  • Recommending GO without a mitigation plan for the secondary gap

Key Takeaways

  • A POC tests a hypothesis — make the hypothesis explicit before designing the scope
  • Written, signed success criteria before execution are non-negotiable
  • The production-POC gap must be mapped before execution begins, not after
  • Eight production gap dimensions: data environment, concurrency, model governance, prompt management, error handling, observability, clinical workflow integration, and security
  • A Go/No-Go decision framework with defined categories prevents ambiguous outcomes
  • App Orchard review takes 6–12 weeks — submit in parallel with POC execution
  • POC execution must include the client engineering team who will own the system in production

Further Reading