Jan. 16, 2026

AI Isn’t the Problem! Why AI Adoption Fails at Work (95% Get Zero ROI) | Jay Kiew

In this episode of An Hour of Innovation podcast, Vit Lyoshin speaks with Jay Kiew about a growing but often misunderstood challenge facing organizations today: why AI adoption so rarely leads to real productivity or innovation.

Jay explains why many companies invest heavily in AI tools yet see little to no return, pointing to research showing that the vast majority of organizations fail to achieve meaningful ROI from AI initiatives. Rather than blaming the technology, the conversation highlights how unclear processes, weak critical thinking, and low readiness for change quietly undermine AI efforts. AI, Jay argues, does not fix broken systems; it exposes them.

Vit and Jay explore how leaders misunderstand what AI is capable of, why undocumented workflows and poorly defined roles cause AI agents to fail, and how learning and integration matter more than deploying new tools. They also discuss how AI reshapes roles and responsibilities, why teaching AI inside organizations is harder than expected, and why design thinking and influence play a critical role in successful adoption.

This is a grounded, practical conversation for leaders, product teams, and professionals navigating the future of work, focused on the mindset shifts, organizational discipline, and change fluency required to unlock real value from AI.

Show Notes

Jay Kiew is a change strategist and transformation leader who works with organizations navigating complex change at scale. He is known for helping leaders move beyond tool-driven thinking toward building adaptive, change-ready cultures. In this episode, Jay’s perspective matters because it challenges the assumption that AI failures are technical problems and shows why leadership, process discipline, and learning capability are the real differentiators.

Takeaways

AI does not create productivity by itself; it only amplifies the quality of existing processes and decision-making.
Most AI initiatives fail not because of weak models, but because teams cannot clearly explain how their work actually gets done.
Research showing that 95% of companies see no AI ROI reflects organizational readiness gaps, not a lack of AI capability.
Poorly defined workflows become painfully visible the moment AI is introduced into a team.
Leaders often deploy AI as a solution before agreeing on what problem they are trying to solve.
Organizations that struggle with change management tend to struggle the most with AI adoption.
AI agents fail when humans cannot articulate rules, context, and success criteria for the work.
Critical thinking is becoming more valuable than technical AI skills as automation increases.
Change fluency, the ability to adapt continuously, is emerging as a core career skill for the next decade.
Teams that succeed with AI focus less on tools and more on learning, feedback loops, and behavior change.