AI adoption is accelerating across companies, but productivity gains often don’t follow.
I recently recorded a conversation that challenged a common assumption: when AI initiatives fail, it’s rarely because the models or tools are weak. More often, AI exposes existing issues: unclear processes, poorly defined workflows, and teams that haven’t aligned on how work actually gets done.
We discussed research showing that a large majority of organizations see little to no ROI from AI, not due to technical limitations, but because learning, integration, and change management are treated as secondary concerns. In practice, AI tends to amplify whatever system it’s placed into, good or bad.
AI adoption is accelerating across companies, but productivity gains often don’t follow.
I recently recorded a conversation that challenged a common assumption: when AI initiatives fail, it’s rarely because the models or tools are weak. More often, AI exposes existing issues: unclear processes, poorly defined workflows, and teams that haven’t aligned on how work actually gets done.
We discussed research showing that a large majority of organizations see little to no ROI from AI, not due to technical limitations, but because learning, integration, and change management are treated as secondary concerns. In practice, AI tends to amplify whatever system it’s placed into, good or bad.
Full conversation: https://www.youtube.com/watch?v=Q3KgONTL_s4
Curious how others here are seeing this play out:
Has AI improved real workflows in your org, or mostly highlighted structural problems that were already there?