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.
Read articleRelated macro
AI and autonomous systems
Shape AI and autonomous systems around business priorities, operating models, governance and measurable outcomes.
Discover the macroEditorial overview
Articles
AI moves into the physical world
Why robotics and autonomous systems are becoming a strategic operating-model choice rather than a standalone technology investment.
Read articleFocus
How much does your AI actually understand about the industry?
Generic capability becomes useful only when systems can work with the terminology, evidence and constraints that shape domain decisions.
Where should AI actually change the business?
The strategic question is not where AI can be used, but where it materially changes competitive position, economics or customer value.
Strategic challenges
Executives have more information, not more clarity
The strategic challenge is turning expanding volumes of internal and external signals into evidence that can inform consequential choices.
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
Autonomy should be earned, not assumed
A machine should gain decision authority only where its behaviour can be understood, tested and contained under real operating conditions.
An AI feature is not an AI product strategy
Adding intelligence to an existing experience is easy; deciding where AI fundamentally changes the proposition is the harder work.
Strategic impact
Reliability is built around failure, not perfection
Testing abnormal conditions and recovery paths makes system limits visible before failures propagate into operational processes.
Knowledge becomes more valuable when context travels with it
Semantic relationships, provenance and retrieval allow the same information to support different users, decisions and AI applications.
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.
Too many AI products begin with the model
We frequently see teams search for problems after choosing the technology, producing features with weak user relevance and unclear purpose.