Production-Grade AI: Boardroom to Bottom Line

If your organisation has a dozen AI demos and not a single line on the P&L to show for it, you’re not alone. The past two years have produced a glut of proofs‑of‑concept, assistants in side tabs, and model experiments. What separates leaders now is not model access but operating discipline: turning experiments into production overlays on systems of record, with stage gates, deprecation, and KPIs that speak the language of the balance sheet.

This post offers a practical playbook to move from demos to durable advantage—distilling strategy themes, operating patterns, and engineering non‑negotiables from the book “Production‑Grade AI” .

Why pilots stall (and what works instead)

• Pilots chase features, not outcomes. Define the economic objective up front (e.g., cost per successful task, time‑to‑decision, error rate) and design backwards from it. Success is not “the model works”; success is “the business metric moved and stayed moved.” A quick sanity check of anti‑patterns and reframing advice appears in the book’s early “reality check” and executive chapters.

• Sidecar assistants don’t change behaviour. If AI sits in a separate tab, people default back to the old way under pressure. Put AI in‑path—inside the system of record—so it intercepts and improves key decisions. Start in suggest mode, earn trust, then progressively automate. For practical overlay patterns and workflow redesign principles, see Beyond Chatbots and Rethinking Workflows .

• Governance is bolted on too late. Risk, approvals, and evidence need to be part of the delivery path from day one. Capture what the system saw, decided, and did—automatically—so you can expand with confidence. The playbook for operationalising this is outlined in Governing Autonomy .

A portfolio with stage gates

Treat AI like capital allocation. Move initiatives through clear gates and kill quickly when the evidence isn’t there.

• Gate 0: Value hypothesis and constraints. Name the workflow, owner, target metric, and risk tier. If you can’t name the system of record you’ll overlay, don’t start. See leadership expectations in the CEO chapter on mandate and cadence.

• Gate 1: In‑path prototype. Ship a real overlay on real data with logging, approvals, and rollbacks. “Assistant on the side” doesn’t count. Patterns to make this real are in Beyond Chatbots and Rethinking Workflows .

• Gate 2: Evidence of reliability. Establish golden datasets, behaviour tests, and outcome tracking. No expansion without passing reliability bars—see the evaluation approach in Doing AI for Real .

• Gate 3: Progressive autonomy. Move slices of the flow from suggest → approve → auto, with explicit confidence thresholds and kill switches. Guardrails and control patterns are covered in Controlling AI .

• Gate 4: Scale and sustain. Optimise cost per successful task with caching, routing, and retrieval tuning. Formalise cadence and ownership—platform strategies are detailed in Architecting for Scale .

Where the money is: overlays on the system of record

You realise value when AI sits at the decision point—CRM activities that improve conversion, finance reviews that shorten cycle time, claims adjudication that reduces leakage. Overlays make a specific step faster, better, or cheaper, and they’re easy to instrument.

• Start narrow: one high‑friction step, one measurable outcome, one system of record.
• Stay in‑path: intercept the step and propose or take action in context (see overlay patterns in Beyond Chatbots ).
• Measure behavioural change: “retained on new path” tells you whether people prefer the new way when the novelty wears off (measurement patterns in Doing AI for Real ).

Govern speed with evidence‑by‑design

Speed and safety are not enemies if you instrument them.

• Risk tiers set the default controls: the higher the risk, the stronger the logging, approvals, and monitoring (operationalised in Governing Autonomy ).
• Evidence is automatic: store inputs, retrieved context, prompts, tool calls, decisions, confidence, and outcomes as a trace tied to the business record (implementation patterns in Controlling AI ).
• Expand by blast radius: begin with a small cohort, widen only when reliability and outcomes hold.

Platform non‑negotiables

Behind every valuable use case sits a platform that turns experiments into services.

• A clear runtime: inference gateway, capability/agent registry, retrieval layer, policy/guardrails, and evaluation/observability—see pragmatic blueprints in Architecting for Scale .
• Portability and versioning: abstract model providers, version prompts/tools, and keep decision evidence for audits and rollbacks. Tactical guidance appears in LLMs, Prompts & Tooling .
• Task‑level economics: track cost, latency, and outcome per task. Optimise retrieval, caching, and routing before reaching for a bigger model.

Run a cadence the business can feel

What gets managed gets delivered.

• Executive rhythm: monthly review of portfolio gates and value; fortnightly product/platform sync on reliability, incidents, and cost; weekly demos showing in‑path improvements on real cases.
• KPIs that matter:
  • Cost per successful task (including human review time) — optimisation tactics in Architecting for Scale
  • Time‑to‑decision/resolution in the target step — redesign guidance in Rethinking Workflows
  • Conversion/quality lift in that step — overlay patterns in Beyond Chatbots
  • Retained on new path (behavioural adoption) — measurement in Doing AI for Real
  • Incident rate and mean time to rollback — control patterns in Controlling AI
• Deprecation as policy: every pilot starts with a sunset plan; every shipped overlay must replace something. Kill or scale—no indefinite limbo.

People, not just platforms

• Small, accountable pods: product, engineering, data, and policy/evaluation focused on one workflow at a time, supported by a platform team that provides gateway, guardrails, and evaluation. Role definitions and enablement are captured in the book’s skills and engineering chapters.
• CI/CD for non‑determinism: golden sets and behaviour tests in the pipeline; treat prompts and retrieval configs as versioned artefacts; automated regression gates to catch drift early. The path from prototype to production is outlined in From Prototype to Production .

A 90‑day board‑ready plan

• Weeks 0–2: Select 3–5 use cases, each with a system‑of‑record overlay, target metric, risk tier, and kill criteria (one‑page framing in the book’s Blueprint ).
• Weeks 3–6: Ship in‑path overlays in suggest mode with logging, approvals, and rollback. Start capturing traces.
• Weeks 7–10: Establish golden sets and behaviour tests; clear reliability bars; agree blast‑radius expansion with Risk/Legal.
• Weeks 11–13: Progress bounded flows to approve/auto where evidence supports; optimise cost per successful task; deprecate legacy steps.

The CEO mandate

Set the rules of the game: insist on in‑path overlays, demand stage gates and deprecation, and hold teams to P&L‑relevant metrics. With a visible cadence and a platform that embeds governance, AI moves from demos to durable advantage. If you want a compact reference architecture and operating checklist, see Architecting for Scale and the implementation guardrails in Controlling AI .