AI models are becoming a commodity.

tgroenwals shared this post · 1h 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.

The model is the visible 10%.
The system is where the value lives.

LLMs generate language.
RAG grounds that language in enterprise knowledge.
Agents connect that intelligence to tools and workflows.
Agentic AI coordinates multiple agents toward goals.

Every layer changes the company.

LLM → RAG: now you need data governance.
RAG → Agents: now you need execution permissions.
Agents → Agentic AI: now you need decision rights.

That is why so many programs stall.
They buy the model.
They skip the trust architecture.

Gartner predicts more than 40% of agentic AI projects will be
canceled by the end of 2027.
They have a name for the hype: agent washing.

Calling something autonomous does not make it governed.

The next AI advantage will not come from who has the best model.
It will come from who can safely connect intelligence to work.

Language → Grounding → Execution → Orchestration.

Intelligence was never the bottleneck.
Trust architecture is.

Your CEO is promising autonomous agents.
Your teams are still trying to get RAG through compliance.
That gap is the real AI strategy.

From Pilots to Platforms.

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.