Enterprises have run GenAI pilots. Fewer pass security review, integrate with SSO and CRM, or define on-call when quality degrades. Production is a systems discipline — data, architecture, governance, adoption — executed in order.
This roadmap reflects what we deploy for clients moving from demo to governed production. Skipping phases saves calendar time briefly and costs multiples later in rework, audit findings, and abandoned pilots.
Phase 1 — Discovery and data readiness
- Inventory workflows by volume, error cost, and existing KPIs
- Assess data quality, lineage, retention, and access policies
- Define success as measurable outcomes — not 'AI deployed'
- Identify mandatory human review before any automation claim
- Assign named data and security owners — not a generic 'AI team'
- Document corpus sources, refresh cadence, and exclusion rules
Skip data readiness and pilots collapse when real documents, permissions, and edge cases appear. Discovery should produce a ranked backlog, architecture sketch, and investment bands leadership can fund by gate — start with an AI readiness audit before production spend.
Phase 2 — Architecture and security
- Choose retrieval vs fine-tuning based on sensitivity and update frequency
- Implement PII redaction, secrets management, and audit logging from day one
- Design APIs so models never bypass authorization in source systems
- Version prompts, models, and evaluation sets like application code
- Define allowed tool calls and network egress for agent workflows
- Map data residency and subprocessors for legal and procurement review
Security architecture is sprint-zero work — not a gate before go-live. Clients pass audits because logging, redaction, and access boundaries were built with the first vertical slice, not bolted on after users adopted the tool.
Phase 3 — Build, evaluate, iterate
Ship one workflow, one user cohort, one integration path. Maintain golden test cases representing real edge cases — refunds, exceptions, ambiguous policy language. Staged rollouts and shadow mode beat big-bang launches for high-stakes flows.
- Weekly evaluation runs against golden sets before each prompt or model change
- User feedback buttons linked to session logs for triage
- Latency and cost budgets per workflow — alert when exceeded
- Rollback procedure tested — not documented only
Phase 4 — Scale and operate
Production means runbooks: on-call rotation, cost dashboards, drift monitoring, retraining triggers, and executive reporting tied to business KPIs. Train operators; document escalation; rehearse rollback when hallucination or policy violation rates spike.
Scaling is not cloning the pilot — it is hardening integration, load testing retrieval pipelines, and expanding corpora with governance. Each new business unit adds access boundaries and evaluation cases; budget accordingly.
MLOps capabilities required
- Latency and token cost monitoring per workflow
- User feedback capture linked to prompt and model versions
- Regression tests on evaluation sets before each release
- Incident response when hallucination or policy violation rates spike
- Corpus version control with scheduled refresh and diff review
- Executive dashboard tying adoption to throughput and error metrics
Anti-patterns that kill programs
- Platform-first — buying a 'GenAI platform' before picking a workflow
- Autonomy-first — removing humans before evaluation exists
- Vendor-only ownership — no internal product owner accountable for outcomes
- Metric-free pilots — demos without baseline KPIs
- Frozen prompts — no versioning when models and policies change
Change management
Operators must trust the system. Run train-the-trainer sessions, publish when to override AI, and celebrate human corrections as training signal — not failure. Adoption metrics belong on the same dashboard as technical SLOs.
Incentives matter: if agents are measured only on handle time while AI drafts require extra review steps, adoption will stall. Align KPIs with the hybrid human-AI workflow you designed.
Typical timeline
Discovery and architecture: two to four weeks. First production workflow with evaluation and SSO: six to ten weeks. Hypercare and iteration: four to eight weeks. Timelines compress when data is clean and an internal owner is dedicated — they extend when compliance review is sequential instead of parallel.
FAQ
When is a pilot 'production-ready'?
When it has SSO, audit logging, evaluation harness, named on-call, documented rollback, and a business KPI trending positively for at least one full operating cycle — not when leadership liked the demo.
Spectrum Future Tech delivers end-to-end GenAI production — discovery, RAG architecture, integration, MLOps, and handover — with architect-led squads and weekly demos.
Integration patterns that survive audits
Production copilots rarely live in isolation. They read from document stores, CRM, ticketing, and data warehouses — each with its own authorization model. The anti-pattern is giving the model a service account with blanket read access; the durable pattern is per-user delegated access with query-time permission checks.
- User-context retrieval — answers respect the asker's existing system permissions
- Write actions through approved APIs only — no free-form database access
- Caching policies that respect document retention and takedown
- Separate staging corpora for UAT — never test against production PII without controls
Handover to internal teams
Vendor-built pilots fail at handover when runbooks, evaluation sets, and prompt libraries stay proprietary. Contract for knowledge transfer: paired ops weeks, documented architecture, and shared repos before final payment milestones.