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.
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
How much does your AI actually understand about the industry?
Generic capability becomes useful only when systems can work with the terminology, evidence and constraints that shape domain decisions.
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.
Strategic challenges
AI forces choices before certainty exists
Executives must make investment and positioning decisions while technologies, economics and competitive implications continue to move.
AI dependency is becoming a strategic architecture question
Model and compute concentration can expose enterprises to changing economics, availability, jurisdiction and provider decisions.
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.
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.
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
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.
Every AI pilot seems to build its own stack
We frequently see separate integrations, retrieval layers and model access patterns created for problems the enterprise already solved elsewhere.