Ecosystem Intelligence
Signal From the AI × Life Sciences & Healthcare Convergence Layer
From someone deploying these with enterprise healthcare clients globally — not from the analyst bench. Updated as significant signals emerge.
Enterprise Debt Is Life Sciences & Healthcare AI's Real Blocker
Most health systems are running AI pilots on top of 15-year-old data infrastructure. The bottleneck isn't model quality — it's that the underlying data pipelines can't support production-grade AI. The organizations winning treated data infrastructure as a prerequisite, not an afterthought. If your AI strategy doesn't include a data infrastructure track, you don't have an AI strategy.
AI Orchestration Is the Next Workflow Frontier
Ambient documentation was the first wave. Prior auth automation, referral management, and claims processing are next. The teams I'm watching are building orchestration layers connecting multiple AI models to existing EHR workflows — not point solutions. The difference: orchestration compounds. Point solutions plateau.
ONC's $2M Signal: Federal Alignment on AI-Ready Infrastructure
ONC is specifically targeting interoperability and AI-ready infrastructure with $2M in next-gen health tech funding. If you're building anything that touches health data exchange, this is the policy direction to align with. Federal funding signals rarely move fast, but they move consistently — and this one is pointing at the right problem.
The HIMSS26 Governance Gap Is Real
The pattern at HIMSS26 Europe is consistent with what I see internally: AI projects stall not because models are bad, but because governance frameworks don't exist. Responsible AI isn't a compliance checkbox — it's the difference between a pilot and a production deployment. Organizations that build governance alongside capability ship faster, not slower.
MGB's LLM Leaderboard for Patient Care
Mass General Brigham is publishing a live leaderboard tracking LLM performance on patient care tasks. This is the benchmarking infrastructure the industry needs — domain-specific, outcomes-focused, publicly accountable. If this model gets adopted broadly, it will change how enterprise health systems evaluate and procure AI tools.