Strategy and adoption
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AI in PracticeSep 3, 2026
Meta’s Hatch rollout shows employee AI adoption needs trust, not token counts
Meta pushing its Hatch agent internally while easing away from token-count pressure is a useful correction in the enterprise AI race. Usage metrics can make AI adoption look active, but they do not prove that workers are doing better work or trusting the system.
InfrastructureSep 3, 2026
Altman’s compute warning captures the awkward economics of the AI buildout
Sam Altman warning about unsustainable silliness in compute buildout lands because the market is already asking whether AI infrastructure is ahead of demand. The industry is spending as if model usage, inference volume, and enterprise adoption will keep compounding rapidly.
AI in PracticeAug 31, 2026
Workers are starting to protect expertise from the agents they are asked to train
Enterprise AI adoption has a people problem hiding inside the workflow charts. If employees believe the agent they are training will later replace them, they have every incentive to withhold the messy expertise that makes automation useful in the first place.
AI in PracticeAug 31, 2026
The Pentagon’s chatbot portal shows defense AI is moving into everyday work
Military AI adoption is no longer limited to specialized battlefield systems. The Pentagon adding versions of major chatbots to a central AI tools portal shows that defense organizations are also trying to bring general-purpose assistants into ordinary knowledge work.
AI in PracticeAug 30, 2026
Worker sentiment is turning into a harder AI adoption metric
Enterprise AI adoption has been sold from the top down, but employee reviews are starting to reveal the bottom-up experience. The Decoder's report on souring AI sentiment shows that the real deployment test is not whether executives like the strategy; it is whether workers believe the tools make their jobs better.
AI in PracticeAug 30, 2026
Small businesses are getting a second-mover advantage in AI
Small businesses do not need to copy every AI experiment from large companies. Their advantage is that big companies have already made many of the expensive mistakes in public: over-automation, unclear disclosure, weak training, messy governance, and tools that sound useful but do not fit the work.
AI in PracticeAug 26, 2026
Enterprise AI is moving toward data-local deployment patterns
Enterprise AI adoption is increasingly constrained by where the data lives. Companies want the productivity gains, but they do not want sensitive records, customer data, or regulated workflows flowing into systems they cannot govern.
AI in PracticeAug 26, 2026
Corporate AI adoption is slower than the hype but faster than before
The enterprise AI story is more uneven than the launch cycle makes it look. Many companies are experimenting, but deep integration remains harder because workflows, data permissions, procurement, and employee trust all have to change together.
AgentsAug 23, 2026
Agentic AI adoption is moving faster than enterprise readiness
AI Business warns that agent deployments are accelerating while many organizations still lack the processes, controls, and operating models needed to use them safely.