FREE RESOURCE: AI OPERATING MODEL
AI program problems are consistent. Surprisingly, so are the solutions.
Measure your current AI program process against our proven AI Operating Model to see where it holds up, where it could improve, and how to keep evolving it.
THE MODEL
A decade of expertise
delivered in just one afternoon
We codified our deep experience in deploying AI systems at large financial institutions into a framework to reduce the coordination friction that slows down your AI implementation.
Our workbooks walk you through each stage, with actionable steps to improve in each area.
1 Define roles & responsibilities
Clear roles & responsibilities
Defined handoffs
Built-in compliance controls
2 Set clear boundaries
Defined scope
Clear end goals
Focused constraints
3 Design your process
Stage definitions
Team handoffs
Definition of “done”
4 Manage risk
Policies & controls
Ongoing review & audit
Regulatory alignment
5 Operationalize results
Clear prioritization
Structured business cases
Success criteria alignment
FREE RESOURCES
AI Operating Model Workbooks
Our free workbooks offer actionable templates, checklists, and frameworks so you can build out or build upon your AI operating model.
AI OpModel Foundations
Define roles & responsibilities
Define the people, processes, and platform patterns that eliminate bottlenecks, accelerate deployment, and deliver measurable business value.
Clear roles & responsibilities
Defined handoffs
Built-in compliance controls
Scope & Structure
Set clear boundaries
Without clear boundaries, AI operating models become ambiguous and ineffective. Define exactly where your model applies—and where it doesn't.
Defined scope
Clear end goals
Focused constraints
AI Life Cycle
Design your process
Define how AI projects flow through your organization from idea to production deployment and ongoing maintenance with clear stage gates.
Stage definitions
Team handoffs
Definition of done
Use Cases & Value
Operationalize results
Create clear, consistent frameworks that assess and deliver tangible, measurable impacts on the business from AI initiatives.
Clear prioritization
Structured business cases
Success criteria alignment
AI Governance
Manage risk
Establish an AI governance program that creates controls for managing each individual use of AI within your organization.
Policies & controls
Ongoing review & audit
Regulatory alignment
PROVEN INSIGHT
We've seen this
over and over
This isn't a hypothesis. It's an observed pattern across dozens of enterprise AI implementations.
AI doesn't fail because models break.
It fails where ownership, governance, and value fall apart between idea and production.
Enterprise-tested
Implemented AI operating models across Fortune 500 companies and growth-stage enterprises.
Cross-industry
Financial services, healthcare, retail, technology — the pattern persists across domains.
Maturity agnostic
Whether teams are starting or scaling AI, the breakdown occurs in the same places.