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 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
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 articleFocus
Who actually owns an AI decision?
Effective governance starts with accountability for the decisions, systems and outcomes that AI increasingly influences.
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
When software begins to exercise authority
Agentic systems force enterprises to redefine decision rights, accountability and intervention across automated workflows.
AI dependency is becoming a strategic architecture question
Model and compute concentration can expose enterprises to changing economics, availability, jurisdiction and provider decisions.
POV
Governance that slows everything is badly designed
AI governance should increase control where consequences matter and remove unnecessary friction where they do not.
AI should challenge executives, not flatter them
Decision systems have greater value when they expose weak assumptions and credible alternatives rather than reinforce the prevailing view.
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.
What changes when workflows can act?
Agentic architectures can reduce coordination layers by connecting reasoning, decisions and execution within the same operating flow.
What we observe
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.
RAG is often mistaken for a knowledge strategy
We frequently see document retrieval implemented before information quality, structure, permissions and relevance have been addressed.