Situation
The firm had no shortage of AI: many tools, many pilots. What it lacked was governance and compounding. Every team experimented on its own, nothing was owned, and none of it tied to a business result, so the initiative stalled.
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Technology servicesDigital TransformationStrategic consulting engagement
A Services Firm Turned Stalled AI Pilots Into a Governed Operating Model
Enterprise technology services firm (anonymized) · named when customer permission clears
See the full projectQuality
Every dept
mapped to a scored roadmap
Quality
12
governed skills in the library
Time
Live
dashboards replacing manual reporting
The arc
Situation, work, outcome.
Work
We built a governed AI operating model from the bottom up: governance and policy first, then projects and artifacts, then skills, then agents, then apps. We interviewed every department, produced the standard artifacts (value stream maps, SOPs, a scored opportunity register, a roadmap), ranked by opportunity, and executed the smallest layer that created each outcome, a process change, a skill, an agent, or an app.
Outcome
Every department now maps to a scored opportunity register and a phased roadmap. Manual, multi-tool report pipelines were rebuilt as live, governed dashboards, and the firm has a safe foundation for agents instead of scattered, ungoverned pilots.
The operating-model arc
What discovery surfaced, what we built, what the QBR recalibrated.
Every engagement runs the same three-phase shape, foundation before automation, measured every cycle.
Phase 1, Foundation
Weeks 1-4- Set governance, the AI usage policy, and the AI champion role.
- Stood up the core skill library and the artifact standards.
Phase 2, Discovery
Weeks 5-12- Interviewed and mapped each department.
- Scored the company-wide opportunity register and ranked it.
Phase 3, Execution
Weeks 13-20- Deployed the top-ranked fixes and the first agents.
- Rebuilt manual report pipelines as live dashboards.
Case study, FAQ
Questions about this engagement.
Published as FAQPage schema for AI Overview + People Also Ask citation.
Why build a foundation before deploying agents?
Because AI accelerates whatever already exists. Point an agent at a broken, ungoverned process and you scale the mess. The foundation, policy, artifacts, validated skills, is what makes agents safe, auditable, and able to compound instead of stall.
What does a governed AI operating model actually include?
Five layers built bottom-up: governance and policy, then projects and artifacts, then reusable skills, then agents built on those skills, then the apps where it all surfaces. Skip a layer and the initiative stalls, which is exactly why most do.
Want yours on the list?
Start with a measurement.
The value-impact OKRs we set together at kickoff become the case study when the engagement closes. One vendor, one roadmap, measured every quarter.