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
Who actually owns an AI decision?
Effective governance starts with accountability for the decisions, systems and outcomes that AI increasingly influences.
How much of your AI stack should you control?
The answer depends on workload economics, data sensitivity, resilience and the strategic consequences of external dependency.
Strategic challenges
AI dependency is becoming a strategic architecture question
Model and compute concentration can expose enterprises to changing economics, availability, jurisdiction and provider decisions.
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.
POV
If AI value cannot be explained, it is not a business case
Claims about transformation mean little without identifiable economic drivers, credible baselines and measurable paths to realised value.
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.
Strategic impact
Strategy turns AI activity into direction
Clear choices about ambition, priorities and sequencing connect individual initiatives to a coherent enterprise agenda.
Control has value, but it also has a cost
Selective sovereignty can protect critical workloads without forcing organisations to own infrastructure that offers little strategic advantage.
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.
Testing only expected behaviour creates false confidence
We frequently see AI evaluated for quality while adversarial inputs, dependency failures and edge conditions remain largely unexplored.