Practical perspectives on enterprise AI implementation, custom agents, and digital transformation from the field.
Most enterprise AI pilots succeed. Most never make it to production. The gap isn't technical — it's a handful of unglamorous decisions nobody made at the start. Here's what actually gets pilots over the line.
The moment your AI agent reads content it didn't write — emails, tickets, web pages, documents — it can be given instructions by whoever wrote that content. Here's how to design agents that survive contact with hostile input.
GitHub Copilot, Claude Code, and Cursor can genuinely double a team's output — of both good code and bad. What separates the teams that get the upside from the ones drowning in review debt is everything around the tool.
Token prices keep falling but enterprise AI bills keep climbing. Here's where the money actually goes in production AI systems — and the patterns that bring it back under control.
Model Context Protocol has become the de facto way to plug AI agents into enterprise tools and data. Here's what MCP actually is, why it matters, and how to start using it in your own environment.
Million-token context windows have shifted what's possible in enterprise AI. Here's what genuinely changed with Claude 4, where long context wins over retrieval, and where it still doesn't.
A practical guide to creating enterprise-grade AI agents using Microsoft Copilot Studio — from design patterns to deployment strategies that deliver real business value.
Before investing in AI, every enterprise needs an honest readiness assessment. Here's the framework I use with clients to evaluate data maturity, infrastructure, team skills, and ROI potential.
A hands-on comparison of the leading AI platforms — Anthropic Claude, Google Gemini, and OpenAI GPT — with real-world enterprise use cases and recommendations.
How to incrementally introduce AI into legacy enterprise systems without a full rewrite — practical patterns for augmenting existing applications with intelligent capabilities.
