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
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.
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What should actually be automated?
The right boundary depends on the work itself: its variability, judgement requirements, exceptions and consequences when execution goes wrong.
What happens after the AI prototype works?
Production introduces lifecycle, reliability and observability requirements that experimental environments are rarely designed to handle.
Strategic challenges
Industry context is harder to encode than industry data
Much of the knowledge behind specialist work sits in judgement, operating practices and relationships that datasets alone do not capture.
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.
POV
Do not automate work that should disappear
The first automation decision should be whether an activity belongs in the future workflow at all, not which technology can perform it.
A model in production is a dependency, not an achievement
The engineering challenge begins after deployment, when performance, cost and behaviour must remain manageable as everything changes.
Strategic impact
Prototypes turn assumptions into evidence
Early experimentation can reveal how users, models and product interactions behave before architecture and investment become difficult to change.
Knowledge becomes more valuable when context travels with it
Semantic relationships, provenance and retrieval allow the same information to support different users, decisions and AI applications.
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.
Sovereignty is often reduced to data location
We frequently see residency treated as sufficient while model dependency, compute concentration and portability remain largely unexamined.