MCP One Year In — What's Working, What Isn't
Model Context Protocol is a year into broader adoption. The standardisation has paid off in specific ways and disappointed in others. A practitioner perspective from the trenches.
Enterprise AI in 2025 — Year in Review
A second year-end reflection from the field. What stabilised, what surprised, and what's heading into 2026.
Building the 2026 AI Roadmap — A Practitioner Framework
Annual AI planning has matured into its own discipline. A framework for building the 2026 roadmap that holds up through the year, not just through the planning cycle.
Migration Patterns — From Early AI Deployments to Mature Ones
Many enterprises have early AI deployments that worked enough to ship and now show their limitations. The migration from early to mature deployment is its own programme of work.
AI Platform Engineering — What Mature Platforms Look Like in 2025
The first wave of enterprise AI platforms is now mature enough to extract patterns. The platforms that compound value across line-of-business teams share recognisable shape.
Enterprise AI in 2024 — What We Learned
A year-end practitioner reflection on what changed in enterprise AI in 2024, what stayed the same, and what to take into 2025.
AI in Supply Chain — Where the Genuine Wins Are Landing
Supply chain AI has been a long-running marketing category. The genuinely useful applications in 2024 are narrower than the pitches but more durable.
AI Vendor Selection and Procurement for Enterprise
AI vendors are pitching every enterprise. The procurement process for AI tools needs to evaluate things conventional software procurement doesn't — model lineage, data handling, evaluation methodology, exit strategy.
Building an AI Centre of Excellence — What Actually Works
Every enterprise has an AI Centre of Excellence on the org chart or planned for one. The shape that compounds value differs from the consultancy-recommended default.
From AI Pilot to Production — The Playbook That Bridges the Gap
Every enterprise has AI pilots. Far fewer have AI in production. The bridge between the two is more about organisational discipline than technical capability. A practitioner playbook.
LLM Integration Patterns for Enterprise Applications
Most LLM proofs of concept work in a notebook and break in production. The patterns that survive deployment are not exotic — they're the ones built on enterprise integration discipline most teams already have.