Weekly AI insights: research, frameworks, and practices for purposeful, responsible use.
AI lets individuals think beyond their domain—but it also generates plausible fiction. Studies from HBS, the BBC, and European researchers show why experienced professionals are essential: they’re the ones who can tell when AI sounds right but is dangerously wrong.
AI fails two-thirds of real jobs—not because it’s useless, but because it can’t work alone and humans don’t know when to trust it. Drawing on UpBench and Nature studies, this essay shows why AI transformation requires apprenticeship, not installation.
AI doesn’t fail at scale because of weak adoption—it stalls because organizations can’t absorb the capacity it creates. This essay breaks down three hidden constraints—redeployment discipline, managerial activation, and governance design—that determine whether AI drives EBIT or just faster busywork.
AI budgets are rising fast—but value isn’t keeping up. This edition explores why teams stall at scale, from trust breakdowns and shadow AI to tool sprawl and misused time. Learn the key ratios and design fixes that turn saved hours into strategic outcomes.
Shadow AI use is rising while leaders claim productivity gains—without the proof to back it. This issue unpacks the disconnect, explores role-specific AI design, and shows how small businesses align adoption with outcomes. Speed is real—but only systems turn it into sustainable value.