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The Operating Model for AI: Runbooks, Ownership, and Weekly Cadence

Mon Sep 15 20252 min read

AI systems degrade without an operating model. Define owners, review loops, and incident playbooks so performance compounds.

The Operating Model for AI: Runbooks, Ownership, and Weekly Cadence cover

This market rewards velocity and proof. AI is no longer differentiated by “we use LLMs” — it’s differentiated by whether you can deliver reliable outcomes faster than competitors, with unit economics that hold up.

Below is a practical playbook you can apply to most SaaS and service businesses. It focuses on what wins: clear decision rules, operational control, and instrumentation that turns learning into a compounding advantage.

The core idea #

AI creates durable value when it does one (or more) of the following:

  • Expands capability: customers can do something they couldn’t do before.
  • Reduces time-to-value: onboarding, setup, or adoption friction collapses.
  • Reduces cost-to-serve: fewer manual steps, fewer escalations, lower rework.
  • Improves decision quality: better prioritization, fewer mistakes, faster iteration.

If you can’t map the work to one of these, you’re likely building a demo.

A simple framework (use this in planning) #

1) Pick the highest-leverage workflow #

Good candidates share four traits:

  • High frequency
  • Clear success criteria
  • Structured inputs/outputs
  • Measurable business impact

Start with workflows where failure is tolerable, then graduate to higher-stakes areas once reliability and controls are proven.

2) Define the “proof stack” #

To win in a crowded market, you need artifacts that stand up in a buyer’s head and a CFO’s spreadsheet:

  • A baseline (“before”) and a target (“after”)
  • An evaluation set (real cases, not toy prompts)
  • A monitoring plan (what you watch weekly)
  • A rollback plan (how you reduce risk)

3) Instrument the loop #

Instrumentation turns AI into a compounding system:

  • Capture inputs (intent, context, tool calls, retrieval hits)
  • Capture outputs (result, latency, user follow-up, escalation)
  • Capture outcomes (activation, retention, revenue, cost-to-serve)

Then create a learning backlog: the smallest set of fixes that move outcomes.

What to do this week (actionable checklist) #

  • Choose one workflow where automation pays back in 30–60 days.
  • Define 2–3 metrics that map to outcomes (not vanity).
  • Create a “golden set” of cases and a pass/fail bar.
  • Ship a narrow version, measure, iterate weekly.

If you want a second set of eyes on the workflow selection and metrics, start here: Book a Strategy Call.

Sources #