Ideas and lessons learned on AI adoption, agentic development, RAG pipelines, and leading technology teams — collected in chronological order.
Loops promise to make planning optional, but a loop only terminates against a verifiable condition, and that condition is a specification. The durable engineering skills are moving upstream toward specification, verification and judgment.
Shadow AI costs enterprises $670K more per breach. What Canadian pension plans and financial institutions need to know and do now.
Most enterprise AI strategies start from one of two assumptions: get your data right first, or find high-value use cases. Both sound reasonable. Both are traps if you treat them as step one in a linear playbook.
The most productive workers in 2026 aren't the ones typing the fastest. They're the ones orchestrating the most AI agents.
The tools work. The gains don't show up. That's the uncomfortable reality of AI-assisted software development in the enterprise right now.
Most people think of AI as a chatbot. OpenClaw represents a fundamental departure: a persistent, proactive agent that operates continuously, acquires capabilities dynamically, and meets users where they already are.
Harness engineering makes AI-assisted development reliable for professional engineers. That doesn't mean it makes enterprise software accessible to everyone. Citizen development and enterprise engineering coexist only with clear boundaries.
How AI-assisted development evolved from vibe coding to spec-driven development to harness engineering in barely a year, and why the engineer's job is shifting from writing code to designing environments where agents write it well.
Why enterprises should optimize for flexibility and reversibility instead of trying to pick the perfect AI vendor in a market that shifts every few months.
The OpenClaw debate isn't really about one tool's security settings. It's exposing a fundamental tension: AI agents need freedom to be effective, but our security frameworks were built for predictability.
Anthropic brings the power of Claude Code to non-technical users with Cowork, and what this means for AI tool companies and enterprise AI strategy.
Ideas on enterprise AI strategy, the return of upfront requirements in an agile world, and why specs have taken the place of code.
Less than 6 months after my original post, not only has the technology improved dramatically, but the market has validated what many of us early adopters suspected: this represents a fundamental shift in how software gets built.
My journey building a RAG pipeline, highlighting lessons learned and best practices for enriching AI responses with relevant enterprise data.
Comparing the shift to AI-assisted coding to the transition from film to digital photography, and what I've learned putting agentic code editors through the paces.
How companies in regulated industries can take an incremental approach to AI adoption, from private chatbots to RAG systems, while managing security and privacy risks.
While automation has brought significant benefits to project management, it is important not to lose sight of the human dimension of this field.