Strategic perspectives on enterprise AI, technology architecture, and the decisions that shape how organizations adopt and operate intelligent systems.
Life sciences companies that invested in AI infrastructure early are now reporting real operating returns. The next bottleneck isn't model performance, it's governance for agents operating inside regulated workflows.
Read on LinkedInThe responsible AI controls most companies built — output review, prompt testing, human signoff — were designed for bounded systems that summarized and drafted. Once agents start taking action across live workflows, reviewing the output isn't enough. The control surface moves to action.
Read on SubstackAgentic AI is already delivering results in production, but the pattern is specific: tightly scoped problems, real system access, clear evaluation criteria, and engineering discipline.
Read on LinkedInNote-taking apps are becoming operational systems. Once a 'second brain' can execute work on schedules and triggers, it inherits the failure modes of any production system.
AI governance that lives in quarterly committee reviews can't keep pace with weekly deployments. Governance must be engineered into code, tests, and runtime controls.
With enterprise AI, the output works or it doesn't. Most organizations haven't built the evaluation environments needed to hold AI to that standard.
The average enterprise runs 12 AI agents, half in silos. That's often the right default — connect agents only when there's a measurable cross-team workflow benefit.