Thinking & Perspectives
AI Strategy Insights
Practical perspectives on AI strategy, agentic systems architecture, and governance — from the advisory practice at Imagine Works.
AI Centre of Excellence: Should You Build One — and What Should It Do?
As enterprise AI programmes mature, a recurring question emerges: who owns AI across the organisation? The AI Centre of Excellence is the most common answer — and one of the most misunderstood and misimplemented structures in enterprise AI. Here is how to get it right.
Orchestration Patterns in Agentic AI: Choosing the Right Architecture
Choosing an orchestration pattern is one of the most consequential architectural decisions in agentic system design. It determines how information flows through the system, how errors propagate, how human oversight integrates, and how the system scales. Here is a practical guide to the three core patterns and when to use each.
How to Design an AI Incident Response Process
AI incidents are not IT incidents. When a system produces a wrong, discriminatory, or harmful output systematically, the incident may have been occurring for weeks before anyone notices, the harm distributed across thousands of individuals, and the cause difficult to isolate. AI incident response requires its own framework.
Workforce Planning for AI: Roles, Reskilling, and the Human–AI Team
The question enterprise leaders most commonly ask about AI and workforce is: which jobs will be automated? This is the wrong question. The right question is: how does AI change the work — and what does that mean for the people doing it? Here is how to plan your workforce transition correctly.
AI Procurement: What to Demand in a Vendor's Governance Documentation
When organisations procure traditional software, the governance due diligence checklist is mature. AI procurement is different — the systems are not deterministic, their outputs depend on training data and deployment context the buyer does not control, and the consequences of inadequate due diligence are higher. Here is what to ask.
Multi-Agent Systems: When One Agent Is Not Enough
Single-agent AI architectures have well-defined limits. As enterprise AI ambitions grow to include research synthesis, complex workflow automation, and multi-step operational processes, multi-agent architectures become necessary. Understanding when and how to use them is one of the most consequential architectural decisions in agentic AI today.