Unknown
×

How would you like to connect to Sales?

Get a Call Send an Email Schedule a Meeting

The AI Reality Check: What Enterprise Leaders Need to Get Right in 2026

As companies continue to invest in AI in 2026, the real challenge is not access to tools. It is knowing how they are being used and whether they are creating real value. This episode explores why many AI efforts stall inside enterprises, not because the technology fails, but because leaders lack visibility, training, and clear performance standards. Long term AI success depends on measurement, accountability, and practical execution rather than hype or unchecked experimentation.

0:00 / --:--
YoutubeSoundcloudSpotify
Russell Fradin
Guest
Russell Fradin

Episode Summary

In this episode of PureLogics Pulse, host Amir Khan speaks with Russell Fradin, Co-Founder and CEO of Laradin, about what enterprise leaders must get right as AI adoption accelerates. Russell shares insights from building and scaling companies and explains why most AI challenges today are operational, not technical. They also discuss shadow AI, employee resistance, productivity measurement, and the growing expectations managers will have as AI becomes part of daily work. Russell emphasizes that leaders should not panic or block every unknown tool.

The discussion focuses on hidden AI usage across teams, the gap between power users and average users, and why training matters more than hiring new talent. Russell explains how organizations can track real productivity, cancel tools that do not deliver value, and scale the ones that clearly improve output. The key message is simple. Measure what is happening, guide employees properly, and raise expectations as AI becomes part of everyday work.

Show Notes

  • Many organizations underestimate how many AI tools employees are actually using.
  • Unmonitored AI usage creates governance risks and lost learning opportunities.
  • AI productivity gains are uneven across teams and individuals.
  • The performance gap is mostly a training issue, not a talent issue.
  • Leaders should measure outcomes such as output quality and business results.
  • Some AI tools should be cancelled if they do not show clear impact.
  • Successful tools should be scaled across departments with proper guidance.
  • Blocking shadow AI completely is less effective than tracking and evaluating it.
  • Manager expectations will rise as AI increases employee capacity.
  • AI success in 2026 will depend on visibility, accountability, and structured adoption.

Become a Guest