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
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.
Read articleFocus
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.
Who actually owns an AI decision?
Effective governance starts with accountability for the decisions, systems and outcomes that AI increasingly influences.
Strategic challenges
AI expands the enterprise attack and failure surface
Models, prompts, tools and autonomous actions introduce pathways that conventional application security may not fully address.
AI fragmentation becomes expensive at scale
Independent models, platforms and integrations can create duplicated infrastructure and technical dependencies that compound over time.
POV
AI readiness is not a maturity score
A company can be highly capable overall and still be unready for the specific use cases it considers strategically important.
Scale does not mean putting everything on one platform
A strong AI architecture standardises what should be shared while preserving choice where technologies and requirements will continue to change.
Strategic impact
Good governance can make AI easier to scale
Clear roles, proportional controls and common decision standards reduce ambiguity as AI expands across functions and use cases.
Domain depth changes what AI can reliably support
Combining specialist knowledge with relevant data and workflows allows AI to address tasks that generic applications cannot contextualise.
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.
The ROI calculation usually starts too late
We often see AI economics assessed after technology choices are made, leaving benefits estimated around investment rather than the reverse.