tgroenwals shared this post · 4h ago

AI models are becoming a commodity.
AI systems are becoming the moat.

McKinsey: 88% of organizations now use AI.
BCG: only 5% are capturing AI value at scale.
MIT’s research was even sharper: 95% of enterprise GenAI pilots show no measurable P&L impact.

The gap is not intelligence.
It is architecture.

Most companies are buying Ferrari engines and installing them
in horse carriages.

Open almost any serious AI product.
You will not find a model working alone.
You will find a system:

Retrieval.
Memory.
Tools.
Permissions.
Workflows.
Monitoring.
Evaluation.
Governance.

Aiswarya Venkitesh The foundation layer deserves more attention. Security, governance, observability, evaluation, and human oversight become increasingly critical as autonomy increases.

Michael Lee Author Aiswarya Venkitesh - True. Autonomy increases capability, but it also increases the cost of being wrong. That is why security, evaluation and human oversight cannot be bolted on after the system is already acting.

Sandy Carter, Doctor of Science (hon) The model can be replaced quickly, but the data, workflows, permissions, and evaluation built around it are much harder to copy.

Michael Lee Author Sandy Carter, Doctor of Science (hon) - Right. Model portability is increasing, but the surrounding operating capability compounds over time. Data context, workflow design, permissions and evaluation become organizational memory that competitors cannot reproduce overnight.

tgroenwals shared this post · Jul 21

Michael Lee

Jul 20 · archived Jul 21

Your AI can pass every benchmark and still fail the only question that matters.

"𝗦𝗵𝗼𝘄 𝗺𝗲 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗶𝘀 𝗱𝗮𝘁𝗮 𝗰𝗮𝗺𝗲 𝗳𝗿𝗼𝗺."

Months in production.
A room full of smart people.
Nobody could answer.

The foundation was never built.
An auditor just happened to find it first.

The pattern inside regulated companies is predictable.

The board asks about the outer ring:
agents, high-risk decisions, regulated use cases.

The budget follows the board.
The failure lands in the inner ring:
source registry, lineage, ownership.

Where no one was watching.

I call this the 𝗔𝗜 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗚𝗮𝗽.

Joshua Ng Outer circle are often the flashy demos, sales pitches which stays in a intro deck or good feeling presentation that motivates you for one day and gets forgotten the next.

Michael Lee Author Joshua Ng - A good demo can create momentum. It cannot create ownership, lineage, or operating discipline. Those have to exist after the room stops applauding.

Arunkumar Sunderraj The execution gap often starts even earlier. Many organizations don't just lack data lineage, they lack a shared definition of the business concepts they're trying to govern. If every team defines "customer" or "policy" differently, traceability quickly breaks down.

Michael Lee Author Arunkumar Sunderraj - Right. Lineage can tell you where the data came from, but not whether everyone means the same thing by “customer.” Governance breaks when the organization can trace the number but cannot agree on what the number represents.

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Your AI can pass every benchmark and still fail the only question that matters.