Thinking & Perspectives
AI Strategy Insights
Practical perspectives on AI strategy, agentic systems architecture, and governance — from the advisory practice at Imagine Works.
AI Hallucination: What Enterprise Leaders Need to Understand in 2026
AI hallucination — when a language model produces confident, plausible output that is simply false — is now a measured enterprise risk, not a research curiosity. Vectara's leaderboard puts frontier model hallucination rates between 3% and 15% on a controlled summarisation task; Stanford's legal-AI study found purpose-built tools hallucinating on 17–33% of queries. Here is what leaders should know, and what to do about it.
Shadow AI: Why One-in-Five Enterprises Now Has a Governance Problem They Cannot See
Shadow AI — employees using unsanctioned generative tools at work — has moved from anecdote to material risk. IBM's 2025 breach data put a number on it: organisations with high shadow AI usage paid $670,000 more per breach, and only 37% of organisations have any policy to detect it. Here's what enterprise leaders should do about it.
Model Context Protocol: What Enterprise Leaders Need to Know Before Buying Into Agentic AI
Eighteen months after Anthropic released the Model Context Protocol, it has become the de facto standard for how AI agents connect to enterprise data and tools — adopted by OpenAI, Google DeepMind, Microsoft, and Cloudflare. For enterprise leaders evaluating agentic AI investment in 2026, MCP is no longer a technical curiosity; it is an architecture and procurement decision.
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.