A phased approach to identifying high-value AI use cases, establishing governance, and scaling beyond pilots.
Pilot with business outcomes, not model novelty
Enterprise AI programs stall when teams start with models instead of decisions. Define one high-value workflow—document triage, support deflection, forecasting, or code acceleration—and instrument the baseline cost, cycle time, and error rate before selecting a model provider.
Governance comes early: data classification, retention, prompt logging, human-in-the-loop rules, and vendor DPIAs. Without these, security and legal veto production at the worst possible moment—after a successful demo.
Build the evaluation harness before scale
Production AI needs eval sets, golden answers, toxicity/PII checks, and regression suites as rigorously as unit tests for software. OpenEO Labs recommends offline evals for every prompt or agent change, plus online shadow deployments before customer-facing cutovers.
Separate experimentation environments from systems of record. Retrieval-augmented generation (RAG) over unstable knowledge bases produces confident wrong answers—treat knowledge freshness as an operational SLA.
Scale the platform, not one-off chatbots
Once two or three use cases prove value, invest in a shared AI platform layer: identity, secrets, observability, cost metering, and model routing. That layer prevents every squad from reinventing auth and logging—and keeps budgets attributable.
A practical 12-month roadmap: quarter 1 discovery and guardrails, quarter 2 production pilot with evals, quarter 3 platformization, quarter 4 portfolio expansion. The companies that win are disciplined about stopping failed experiments fast.