AI Operating Model Design
Strategy without an operating model is a slide deck.
McKinsey's Global AI Survey (2024) identifies operating model and organisational design as the #1 barrier to AI scaling, cited by 57% of executives. Enterprises have the technology. They have the use cases. They lack the organisational architecture to deliver and sustain value.
BCG research (2024) finds enterprises with mature AI operating models are 6× more likely to achieve enterprise-scale AI deployment. The operating model is where strategy meets execution.
57%
cite operating model as the #1 AI scaling barrier
McKinsey, 2024
6×
more likely to scale AI with a mature operating model
BCG, 2024
3×
faster use case deployment with AI translator capability
McKinsey, 2024
70%
of successful AI scalers use a hub-and-spoke model
McKinsey, 2024
Three operating model archetypes
Not every operating model is right for every enterprise at every stage. We design the model appropriate to your current maturity, regulatory environment, and scale ambition
Centralised (AI CoE)
Advantages
Consistent standards and governance
Efficient platform and tooling investment
Clear accountability
Considerations
Creates bottlenecks as demand scales
Business unit adoption is slower
Risk of being perceived as a cost centre
Enterprises building foundational capability. Stage 1–2 of the Gartner AI Maturity Model
Hub-and-Spoke (Hybrid)
Advantages
Governance and standards from the centre
Execution speed at business unit level
Scales efficiently as use case volume grows
Considerations
Requires careful governance design to avoid fragmentation
Demands strong AI translator capability in business units
The target-state model for most large enterprises. Used by 70% of AI-mature organisations (McKinsey, 2024)
Federated (Embedded)
Advantages
Maximum business unit autonomy and speed
AI closely aligned to specific domain needs
Considerations
Governance fragmentation
Duplication of platforms and tooling
Inconsistent standards across the enterprise
Enterprises at Stage 4–5 of the Gartner AI Maturity Model with strong existing governance infrastructure
How we design your AI operating model
Five stages from diagnostic to transition-ready operating model design. Every stage produces documented deliverables your organisation owns
Stage 01
Operating Model Diagnostic
We assess your current AI operating model against the Deloitte four-stage maturity curve (Ad Hoc → Structured → Integrated → Optimised). We map your existing roles, governance structures, data ownership, and decision rights — identifying where your model is creating friction and where it is leaving value on the table
Stage 02
Target State Architecture Design
We design your target-state AI operating model. For most large enterprises, this is a hub-and-spoke model: a central AI Centre of Excellence governs standards, platforms, and enterprise risk, while business units own use case execution. McKinsey data confirms 70% of enterprises that successfully scaled AI operate a hub-and-spoke model by the time they reach maturity
Stage 03
Talent Architecture
We define the roles your AI operating model requires — from data engineers and ML engineers to the 'AI translators' McKinsey identifies as the most critical and scarce capability in enterprise AI. Enterprises with strong translator capacity (people who bridge business problems and AI solutions) deploy use cases 3× faster. We design the hiring framework, capability development pathways, and partnership model your organisation needs
Stage 04
Process & Decision Rights Design
We design the processes, policies, and decision rights that allow AI to be deployed and governed at speed — without creating new compliance or operational risk. This includes the intake process for new AI use cases, the model risk management process, the escalation and incident management protocol, and the ongoing performance monitoring regime
Stage 05
Transition Planning & Enablement
We design the transition path from your current state to your target operating model — with realistic sequencing, change management integration, and the internal capability-building programme required to sustain the model without ongoing external dependency
Build the organisational architecture that makes strategy executable
Most enterprises design their AI operating model as an afterthought — after the technology is already deployed. We design it before deployment, so your AI programme scales rather than stalls.
See also: AI Strategy · Change Management · AI Governance