AI & machine learning strategy

A prompt-first mandate is the highest-ROI governance policy, avoiding costly fine-tuning for problems solvable more cheaply.

A tool cheap in a 50-user pilot can become a six-figure monthly cost at full scale.

200-500 curated cases over-sampling the risky tail is your most durable, compounding data asset; no eval, no production.

Enforcing governance policy once and auditing everywhere beats fragile per-app plumbing while risk-tiering expensive checks against latency.

Naming the decision, consumer system, feature SLAs, and pager owner kills doomed pilots early, your highest-ROI move.

The gate controls model sprawl at the source; no owner and value hypothesis means no entry.

Fewer well-governed models beat orphaned ones; aggressive decommissioning prevents unmonitored liabilities with silent monitoring bills.

If disabling a misbehaving model needs an engineering sprint, you have no real containment; rehearse shutdown timing.

HITL on high-volume systems is rubber-stamping; measure override and reversal rates to prove oversight is real, not decorative.

Requiring disaggregated performance, out-of-scope limits, and human-review triggers makes fairness auditable; a model without a card doesn't deploy.

Great models on weak foundations underperform; talent (40-50%) and infrastructure (30-40%) drive results, not model novelty.