Advisory
AI Operating Model Design
The structures, decision rights, and routines that make AI durable.
The Business Problem
Why this matters now
AI pilots routinely succeed in isolation and fail to scale because no operating model exists to absorb them, ownership is unclear, governance is improvised, and the people who built the pilot are the only ones who can run it.
Our Approach
How we deliver it
- 1
Design the target operating model: structure, decision rights, and forums
- 2
Define how AI Centers of Excellence, business units, and IT collaborate
- 3
Establish governance routines that scale with the AI portfolio
- 4
Pilot the model on one workforce or business domain before enterprise rollout
Deliverables
- Target operating model design and RACI
- AI governance forum structure and cadence
- Center of Excellence charter and staffing model
- Rollout and adoption plan
Expected Outcomes
- AI initiatives that scale beyond their original pilot team
- Clear accountability for AI outcomes at the executive level
- Faster decision cycles for new AI use cases
- An operating model that flexes as the AI portfolio grows
Industries
Where this service applies
Financial Services
Financial institutions have the data and the capital to lead on AI, but legacy governance, model risk requirements, and talent scarcity slow even well-funded programs to a crawl.
Telecommunications
Telecommunications providers are embedding AI into network operations and customer experience while managing some of the most complex legacy technology estates in any industry.
Retail
Retailers are racing to deploy AI across merchandising, customer service, and supply chain, often faster than their workforce and governance can keep pace.
Manufacturing
Manufacturers are combining AI, robotics, and automation on the factory floor while managing a workforce transition that touches union agreements, safety standards, and decades of institutional knowledge.
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