From AI Prototype to Production: A 90-Day Plan

Use 90 days to productionize one narrow AI workflow—not to scale every prototype idea. Organize the work around evidence gates: define the task and risks, build a controlled vertical slice, prove quality and failure behavior, canary with real users, then promote only if ownership and operating signals are ready.

The calendar is a planning template, not a promise. Stop when a gate fails.

Day 0: Name the outcome and owner

Before the clock starts, record:

User and task:
Successful terminal outcome:
Acceptable abstention/escalation:
Prohibited outcomes and actions:
Authoritative data sources:
Product, engineering, data, security, and operations owners:
Quality, latency, cost, and risk objectives:

If ownership, allowed data, or acceptable failure is unresolved, run discovery instead of promising a launch. The AI readiness assessment provides the evidence rubric.

Days 1–15: Bound and measure the prototype

Deliverables

• one task map with happy path, clarification, abstention, and escalation;
• inventory of model, prompt, retrieval, tools, state, and human decisions;
• initial threat/data-flow diagram;
• 30–50 reviewed evaluation cases covering critical slices;
• baseline prototype results including case-level failures;
• architecture decision: deterministic, single-agent, or multi-agent;
• estimate range and top uncertainty spikes.

Gate: proceed only if the task has a measurable useful outcome, data use is allowed, the risky integrations can be sandboxed, and the baseline exposes a plausible path to thresholds.

Do not improve the demo UI during this phase unless UI behavior is the hypothesis.

Days 16–35: Build the controlled vertical slice

Move the workflow into the real application boundary with test data:

• authenticated identity comes from runtime context;
• tool schemas are strict and authorization is enforced at execution;
• business writes use stable operation identities and receipts;
• prompts, tools, model routes, policies, and indexes are versioned;
• one task trace connects retrieval, model calls, tools, validation, and outcome;
• timeout, budget, fallback, approval, and kill-switch paths exist;
• deployment is repeatable in an isolated environment.

Use the first production LLM app checklist to review the slice.

Gate: every consequential action is bounded by policy and produces an auditable result; failures are diagnosable from one trace.

Days 36–55: Prove behavior under failure

Expand evaluation beyond happy paths:

Run deterministic rules for hard constraints and calibrated review for semantic behavior. Turn every repaired failure into a regression case.

Gate: critical invariants pass completely, quality thresholds pass by slice, and no red-line security/data condition remains.

Days 56–70: Controlled pilot

Release to named internal or design-partner users with explicit scope:

• canary route and rollback owner;
• visible limitations and feedback path;
• human review for consequential actions;
• support and incident channel;
• daily trace/failure review;
• measured reviewer workload and override patterns;
• data retention/deletion procedure exercised.

Do not treat user enthusiasm as quality evidence. Compare successful task rate, corrections, escalation, cost, tail latency, and risk events with the predeclared objectives.

Gate: the workflow creates measured value without unacceptable human load, incidents, or unit economics.

Days 71–85: Harden operations

• close repeated pilot failures at the owning contract;
• calibrate alerts to an action and owner;
• test capacity, quota, provider, and downstream failure;
• complete incident runbook and customer communication path;
• verify dashboard segmentation by version and traffic slice;
• document support, on-call, access review, and change approval;
• perform final security/privacy review against the deployed architecture;
• freeze the candidate configuration for release evaluation.

NIST’s AI RMF describes governance as cross-cutting and risk work as continuous through Map, Measure, and Manage—not a one-time prelaunch checklist. The production plan therefore keeps owners and evidence through every phase.

Days 86–90: Promote or stop

Use a signed release record:

Candidate configuration envelope:
Offline evaluation result and reviewed exceptions:
Pilot/canary outcome metrics:
Security, privacy, and data decision:
Cost and latency objectives:
Rollback and kill-switch test:
Known limitations and accepted risk:
Production owner and first review date:
Decision: promote / extend pilot / stop

Promotion is not the end. Schedule the first post-release review, online sampling, evaluator calibration, permission review, and dependency/version reassessment.

Critical path and ownership board

One person may fill several roles in a small team, but no row should be ownerless.

Use the AI agent cost and timeline estimator to adapt capacity and contingency. If platform choice is unresolved, run the build-versus-buy proof tests during days 1–15 rather than after integration code accumulates.

JoinAI can adapt this plan to your systems through a scoped prototype-to-production engagement.

If you are comparing delivery teams, use the AI consulting partner buyer scorecard to test evaluation, security, proof-of-capability, knowledge-transfer, and exit evidence before committing.

Sources and further reading

• NIST AI RMF Core
• NIST AI RMF Playbook
• AWS Well-Architected Agentic AI Lens: Reliability
• AWS Well-Architected Agentic AI Lens: Design principles