adoption

Source-backed Pagish topic assembled from the current AI intelligence feed.

AI in PracticeAug 31, 2026watch

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.

Why it matters: The better implementation pattern is transparency: explain what the system will do, what humans will keep owning, and how expertise will be rewarded. Otherwise the agent rollout becomes a quiet labor negotiation disguised as a software deployment.

AI in PracticeAug 26, 2026watch

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.

Why it matters: The metric to watch is not how many companies mention AI, but how many can point to repeatable work that improved because of it. Pagish will keep separating pilot noise from operational adoption.