The Enterprise AI Rollout Playbook
How to go from AI strategy to production deployment in 12 weeks — without breaking the business.
Most enterprise AI initiatives stall in "pilot purgatory" — endless POCs that never reach production, governance debates that never end, and shiny demos that no operator actually uses. This playbook is the field-tested SAZ approach to shipping production AI systems that compound business value from day one.
01 — Choose the right first build
The highest-ROI first AI build is usually NOT the most exciting one. We look for workflows with high volume, structured data, low regulatory surface, and clear success metrics. Common winners: claim processing, document intake, internal knowledge assistants, sales lead scoring. Avoid: customer-facing chat as the first deploy, anything that touches money movement without governance, anything where the failure cost > the time-savings benefit.
02 — Stand up the platform before the feature
The temptation is to ship the AI feature first and figure out infrastructure later. Don't. Spend the first 2-3 weeks standing up the AI platform: vector store, retrieval, evals harness, observability, governance log, and a "kill switch". Without this platform, every subsequent AI build will accrue technical debt and you'll spend Year 2 rewriting Year 1.
03 — Design the human handoff
Every production AI system needs a clear handoff to a human for consequential actions. Define the confidence thresholds, the queue for human review, the escalation path, and the audit trail. AI that always hands off is useless; AI that never hands off is dangerous. The middle path is the engineering work.
04 — Ship the eval harness first
Production AI without evals is hope, not engineering. Build the evaluation harness in week 1, not week 11. Combine: golden-set evals (manually curated), automated rubric scoring (model-graded), user feedback signals, and offline replay against the production log. Without continuous evals, model regressions are invisible until customers complain.
05 — Choose model-agnostic architecture
Lock in to one provider and you inherit their pricing, their latency, their downtime, and their roadmap. Use an abstraction layer (Claude / GPT / open-weight) so you can route by use case, hot-swap on incidents, and benchmark on cost per outcome. The 2-day investment saves quarters of pain.
06 — Roll out in waves
Phase 1: 10% of one team, with daily review. Phase 2: 100% of one team, with weekly review. Phase 3: rollout to adjacent teams with self-service. Phase 4: org-wide. Each phase is gated by quantitative success criteria — not "feels good" approval.
07 — Operate AI like critical infrastructure
Once in production, AI systems need the same operating rigor as any critical system: monitoring, alerting, on-call rotation, postmortems on failures, and a quarterly model + prompt + retrieval refresh cadence. Most teams under-invest here and pay for it in trust loss when something breaks.
- The right first build matters more than the right model
- Platform before feature — always
- Evals from day one — there is no "we'll add them later"
- Model-agnostic by default — never single-vendor in production
- Roll out in waves with quantitative gates
Services that deliver this playbook.
AI Consulting
A defensible AI strategy — built around your data, workflows, and risk profile.
AI Implementation
From strategy to production — shipped.
Internal AI Systems & Operations
Run AI like critical infrastructure.
AI Infrastructure
AI infrastructure built for production reality.
Run the AI Rollout with the SAZ team.
Email info@Sedighi.ca or call (604) 632-4959. Senior partner response within one business day.