AI StrategyIndustry Use Cases
The Secret to Healthcare AI? It's Not Artificial.
After years deploying AI across academic medical systems, one lesson stands out: healthcare AI succeeds through human partnership, not technology alone.
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Enterprise AI, written from inside regulated industries.
Notes on model independence, multimodel orchestration and the governance that decides whether an AI agent is safe to deploy, from the team running the platform inside academic medicine.
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- Governance and RiskModel Orchestration
Is AI Conscious? I Asked Three Models the Same Question.
Farrukh Khan asked GPT, Claude and Gemini whether they are conscious. Their different answers show why healthcare AI needs evaluation and human oversight.
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The Human Side of What It Really Takes to Make AI Work in a Health System
You can have the best model, agent and architecture and still fail at enterprise AI if you don't bring people along. Models provide intelligence; people make it work.
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What It Really Takes to Bring AI Into a Health System
Building an AI demo takes an afternoon. Getting AI to work inside a healthcare organization means token management, agents, SSO, HIPAA and connectors to the systems that hold your data.
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Healthcare AI: Can Moving Slower Reduce Risk?
Health systems vet AI through security, privacy and compliance reviews, so they rarely run the newest model first. That can reduce exposure to risks that aren't yet understood.
Read the post - Model OrchestrationAI Strategy
Are We Approaching AGI?
With GPT-6 Astra, some call this the start of an AGI era. No single model leads on everything; general intelligence may emerge from many models, tools and agents working together.
Read the post - Industry Use CasesAI Strategy
Healthcare Has a People Shortage. AI Could Help Solve It.
Healthcare already faces shortages of physicians, nurses and staff. The better question isn't which jobs AI will replace, but how it can give scarce healthcare workers more time for care.
Read the post - Model OrchestrationGovernance and Risk
Model Independence by Design: Why API Access Was Never the Hard Part of Healthcare AI
Calling a model through a cloud API is largely solved. The hard part is everything downstream: token allocation, adding agents, staying model-independent, security and HIPAA.
Read the post - Model OrchestrationAI Strategy
Open Source AI Is Cheaper. So Why Is It Losing?
Open source LLMs run 85-90% cheaper per token, yet closed source holds ~90% of the enterprise market. The real constraint is expertise, guardrails and infrastructure, not the model.
Read the post - AI AgentsIndustry Use Cases
Structured Data Meets the LLM Inflection Point
Analytics spends nearly $500B a year governing structured data, while LLMs grew up on language. MCP brings the two together; the real question is whether LLM answers can be trusted.
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Is SaaS Dying? The AI Prototype Boom in Regulated Industries and the Bridge to Production
Clinicians are building real-looking prototypes faster than product cycles can absorb them. Why that pressures SaaS, and what has to sit between a demo and production.
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The Counterintuitive Key to AI in Regulated Industries: What Deploying AI Inside Hospital Systems Taught Us About Human Partnership
Guardrails, validation and monitoring are all necessary. None of them is what decides whether AI takes root inside a hospital.
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Before the Hype: Building AI Where It Actually Matters
We were making probabilistic text generation trustworthy in hospitals in 2018, before anyone called it GenAI. That environment still shapes how we build.
Read the post - Model OrchestrationAI Agents
The Single Model Trap: Why Enterprise AI Needs Model Fusion
No single model can be trusted on its own. Running the same intent across several at once turns disagreement between them into a signal rather than a silent failure.
Read the post - AI AgentsGovernance and Risk
Beyond the Chatbot: The Three AI Agents Every Enterprise Needs — and Why Governance Makes or Breaks Them
A practical guide to the three enterprise AI agent types, where they fail, and how governance ensures safe deployment in regulated industries.
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The Single-Model Era Is Over: Why Multimodel AI Will Define Enterprise Strategy
Why enterprise AI strategy is shifting from choosing one model to orchestrating multimodel systems across modalities, LLMs, and hybrid architectures.
Read the post - AI StrategyGovernance and Risk
Foundation Model Independence: The Next Non-Negotiable Principle in Enterprise AI
Why single-model dependency creates long-term enterprise risk, and how model-independent architecture improves resilience, governance, and adaptability.
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