From AI pilots to enterprise performance
What separates companies that scale AI from those that accumulate experiments�and how operating models, economics and governance determine whether adoption creates measurable value.
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AI and autonomous systems
Shape AI and autonomous systems around business priorities, operating models, governance and measurable outcomes.
Discover the macroEditorial overview
Articles
The rise of the agentic enterprise
How autonomous workflows could reshape decisions, coordination and productivity�and where human oversight remains essential as AI moves from assistance to execution.
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What should machines be allowed to do alone?
Physical autonomy should reflect environmental uncertainty, task complexity and the consequences when machine decisions are wrong.
How much autonomy does a workflow really need?
The real design question is where independent reasoning and action improve execution, and where deterministic logic remains superior.
Strategic challenges
AI investment is running ahead of its economics
Companies increasingly need to distinguish strategically important investment from expenditure supported mainly by technological enthusiasm.
AI opportunity is often easier to find than adoption capacity
The challenge is not generating use cases, but determining which ones the organisation can realistically implement and absorb.
POV
If everything is an AI priority, nothing is
Strategy requires deciding where AI deserves disproportionate attention, where experimentation is enough and what should be ignored.
A model in production is a dependency, not an achievement
The engineering challenge begins after deployment, when performance, cost and behaviour must remain manageable as everything changes.
Strategic impact
Operational discipline makes AI easier to evolve
Structured deployment, evaluation and monitoring allow teams to change models and configurations without losing visibility or control.
Readiness makes AI investment more selective
A clearer view of capabilities and constraints helps separate immediately viable opportunities from those requiring deeper preparation.
What we observe
RAG is often mistaken for a knowledge strategy
We frequently see document retrieval implemented before information quality, structure, permissions and relevance have been addressed.
Governance fails when every use case is treated the same
We often see low-risk and high-impact AI subjected to identical controls, creating friction without improving meaningful oversight.