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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AI and autonomous systems
Shape AI and autonomous systems around business priorities, operating models, governance and measurable outcomes.
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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.
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How does your AI behave when someone tries to break it?
Normal performance says little about how a system responds to manipulation, hostile inputs, unexpected context or failing dependencies.
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.
Strategic challenges
When software begins to exercise authority
Agentic systems force enterprises to redefine decision rights, accountability and intervention across automated workflows.
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
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.
If an AI system cannot fail safely, it is not reliable
Accuracy under normal conditions matters less when one uncontrolled failure can trigger actions the organisation cannot contain.
Strategic impact
What changes when workflows can act?
Agentic architectures can reduce coordination layers by connecting reasoning, decisions and execution within the same operating flow.
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
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.
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.