Perspectives on Life Sciences Transformation
Practical thinking on eClinical deployment, clinical data strategy, AI in drug development, operating model design, and what it takes to make technology change work.
New pieces appear here first, and on LinkedIn. Follow along for each one as it goes live.
Follow on LinkedInThe AI Bottleneck in Clinical Development Is Context, Not Reasoning
AI agents and tools do not fail only because the model is weak. They fail because the organization has not made the clinical context usable: systems, documents, workflow rules, decision rights, evidence trails, and governance. For regulated R&D, context readiness is now a strategic capability.
AI ROI in Life Sciences Starts With the Process, Not the Pilot
A company full of successful AI pilots can still be standing still. Real return appears when a process that matters to drug development changes — not when a model speeds up a single task. Where ROI actually shows up in regulated R&D, and how to choose the capability before you choose the pilot.
If You Want Better Clinical AI, Start With Better Data Architecture
AI is only as good as the data architecture underneath it. In most organizations, that architecture was never designed — it was assembled. This piece argues that the AI conversation in pharma is starting in the wrong place, and where it should start instead.
Why I'm Optimistic About the Future of Study Design
A rare optimistic take from someone usually pointing out where clinical technology promises do not match operational reality. Why the underlying conditions for genuinely better study design have shifted — and what leaders should be paying attention to.
Protocol Complexity Is Still the Hidden Tax on Trial Execution
Clinical teams rarely set out to design a slow trial. Yet protocol complexity continues to drive amendments, site burden, and timeline slippage in ways that most organizations underweight. This piece examines where the complexity actually accumulates and what to do about it.
Why Your AI Pilot Succeeded but Your AI Program Is Failing
Most pharma AI programs are an archipelago of successful pilots that never become a portfolio. The gap between pilot success and program success is operational, not technical — and naming it precisely is the first step to closing it.
90-Day AI Readiness Plan for GxP Organizations
A practical 90-day plan for GxP-regulated organizations preparing to deploy AI responsibly. What to assess, what to govern, and what to defer — sequenced so that downstream investment decisions rest on real evidence rather than vendor narrative.
What the FDA/EMA Joint AI Principles Actually Mean for Pharma
The FDA and EMA released joint AI principles. Most coverage summarized the document; few translated it into operational implications. This piece works through what the principles actually require of pharma IT, business, and governance functions.
What I Write About
AI & Clinical Development
Strategy, use cases, governance, and adoption for AI in drug development
eClinical Systems
CTMS, eTMF, Study Startup, EDC, clinical data, and platform modernization
Operating Model Design
Governance, ownership, process, and execution for clinical technology programs
Platform & Vendor Decisions
Selection, build-buy-partner choices, and deployment planning for R&D technology
Want to Discuss These Topics?
If any of these areas are relevant to challenges you are navigating, I am happy to have a direct conversation.