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
What is AI actually worth to the business?
Economic value depends on where AI changes revenue, cost, productivity or capital efficiency, not on the sophistication of the technology.
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
AI makes bad workflows easier to automate
New capabilities expand what machines can perform, but they do not resolve unnecessary steps, broken handoffs or poor process design.
AI expands the enterprise attack and failure surface
Models, prompts, tools and autonomous actions introduce pathways that conventional application security may not fully address.
POV
The model is rarely the source of differentiation
Durable domain AI comes from proprietary context, specialised knowledge and workflow integration, not access to the same model as everyone else.
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.
Better decisions begin before the recommendation
Structured AI support can broaden alternatives, expose assumptions and make the reasoning behind consequential choices more explicit.
What we observe
Too many AI systems are operated like demonstrations
We frequently see production applications without rigorous evaluation, version control, monitoring or defined lifecycle ownership.
Most AI strategies are portfolios disguised as strategy
We frequently see collections of use cases and technology initiatives without explicit choices about competitive or business priorities.