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
AI moves into the physical world
Why robotics and autonomous systems are becoming a strategic operating-model choice rather than a standalone technology investment.
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Does your AI know where its answer came from?
Enterprise knowledge becomes useful to AI when evidence can be retrieved, contextualised and traced rather than merely placed inside a prompt.
What should actually be automated?
The right boundary depends on the work itself: its variability, judgement requirements, exceptions and consequences when execution goes wrong.
Strategic challenges
AI expands the idea space faster than product teams can validate it
The challenge is separating technically impressive concepts from propositions that solve meaningful customer and business problems.
Enterprise knowledge was never designed for LLMs
Documents, databases and repositories reflect human systems of record, creating fragmentation that models cannot resolve by themselves.
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.
More autonomy does not mean better systems
The strongest agentic architectures constrain authority deliberately rather than giving agents the widest possible freedom to act.
Strategic impact
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.
Operational discipline makes AI easier to evolve
Structured deployment, evaluation and monitoring allow teams to change models and configurations without losing visibility or control.
What we observe
Adding industry documents does not create industry AI
We often see generic models connected to sector content without encoding the workflows, decision logic and constraints behind expert work.
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.