We have multiple solutions running, but they're fragmented
Consolidating through governance, standards, and shared ownership
Fragmentation is normal – and fixable
If your AI initiatives have grown organically and now feel disconnected, you're not alone. This is a natural consequence of distributed experimentation. The good news? Consolidation creates enormous value. McKinsey found that high performers are far more likely to have centralised data, security and compliance, and they reuse code to accelerate deployment. There's a clear path forward.
of high performers adopt component-based models
McKinsey, 2024
have fully operationalised responsible AI
WEF/Accenture, 2022
more likely to succeed with developed change capabilities
Accenture, 2024
"It's easy to see how solutions get fragmented when change is happening so fast. The problem is that duplication and gaps waste resources and create confusion. Now's the time to step back, consolidate and standardise."
Version 1 AI Playbook Guidance
Understanding the Challenge
Fragmentation is often a people problem, not a technology problem. Different teams pursued AI independently, each solving their own challenges without coordination.
McKinsey warns against "tool proliferation" and emphasises that integration matters more than components. The solution isn't just technical architecture, it's governance, shared ownership, and collective decision-making across organisational boundaries.
REACH
Holding onto habits and reinforcing change
Recognise
Why AI matters now
Engage
Building the will to change
Acquire
How to change
Capability
Skills to implement
Hold
Sustaining change
Integration requires reinforcement mechanisms—governance structures, shared standards, and coordination committees that make fragmentation harder than consolidation. Build the infrastructure that sustains good practice.
What Success Looks Like
Digital Core
Consolidated data sources in a governed, secure core with modular architecture that enables interoperability across solutions.
Strategic Governance
Coordination committees reviewing roadmaps, ensuring cross-functional alignment, and making collective prioritisation decisions.
Reusable Components
McKinsey's component-based design; code reuse, shared services, and standardised approaches that accelerate deployment across teams.
Integrated Risk Management
Consistent responsible AI standards across all solutions, with transparency, systematic risk management, and clear escalation paths.
Practical Actions for Your Teams
- Establish coordination committees: Cross-functional groups that review AI roadmaps and ensure alignment.
- Adopt interoperable standards: Design modular infrastructure that allows solutions to connect, share data, and scale together.
- Create centres of excellence: KPMG recommends CoEs to bring people along, share best practices, and prevent reinventing the wheel.
- Implement responsible AI frameworks: The WEF playbook's nine plays include systematic risk management and transparency across all deployments.
- Prune non-performing pilots: McKinsey's first "hard truth" - discard what isn't working and focus resources on real signal. It's not failure, it's learning.
