In this episode of the AI Agent & Copilot Podcast, Shawn Dorward, COO Innovia Consulting, challenges leaders to consider what measuring success looks like with AI implemented across the organization and notes the need for a mindset shift in leaders to understand that transformation starts at the leadership level, not just the frontlines of employees.

The Podcast is hosted by Dynamic Communities, the home of Summit NA!

Click here for the full video or the audio file from Spotify.

Key Takeaways

• The real AI mindset shift starts with leadership. We spend a lot of time asking whether employees are changing because of AI, but I think we may be asking the wrong question. Maybe we should be asking whether leadership has changed because of AI. If we’re serious about using AI to make the organization better, leadership has to go through the same reset we’re asking everyone else to make. That’s part of the broader mindset shift we need around AI. Transformation can’t just be something happening on the front line while leadership continues operating exactly the same way.
• Usage isn’t the same thing as transformation. Leaders absolutely should know whether people are using AI. Adoption numbers matter, and IT should be able to measure engagement. The danger is that those numbers can create a false sense of progress. They tell us people are using the technology, but they don’t necessarily tell us whether the business is actually operating differently because of AI. Sometimes we’re not transforming anything. We’re just making old work faster. A bad meeting with a great AI-generated summary is still a bad meeting—it just has better notes. Faster doesn’t automatically mean better.
• The better question is: What can we do differently now? Instead of asking, “How much AI are we using?”, start asking, “What are we now able to do differently because AI exists?” Those questions sound similar, but they measure completely different things. One measures activity. The other measures how the business actually works. That’s where AI starts becoming an operational differentiator instead of just another technology deployment.
• Leadership has to decide what happens to the time AI gives back. If AI makes someone more efficient, what happens to that recovered capacity? If the answer is simply, “Great, here’s more work,” don’t be surprised when people stop talking about how much time they’re saving. Leaders have to decide what that capacity is for. Is it better customer service? More strategic work? Innovation? Learning? Higher quality? Growth? AI may create the capacity, but leadership determines whether that capacity turns into actual business value.
• Turn the question back on the leadership team. At your next leadership meeting, don’t start with the AI adoption dashboard. Start with: “What have you personally changed, as a leader, because AI is now available?” Then ask, “What changed about how you lead?” and “What part of our business still operates as though AI doesn’t exist?” Those questions are uncomfortable—and that’s exactly why they’re useful. If we can’t identify how we’re running the business differently, we probably haven’t fully captured the value yet. This is part of why leaders need a new data strategy and, more broadly, a new way of thinking about how decisions get made.
• Start small, but make the change real. There are a few practical things leaders can do immediately:
  • Identify one leadership behavior that should change because AI is available.
  • Find one process that still assumes information is scarce, slow, or difficult to understand, and ask whether that assumption is still true.
  • Add one outcome measure alongside your AI adoption measures so you’re tracking what changed—not just who logged in.

The big takeaway for me: Don’t ask whether AI is changing your employees until you’re willing to ask whether AI has changed the way you lead. If leadership stays the same, there’s a good chance the organization will too.

Click below to listen to the Spotify audio version

Thanks for reading!

Shawn Dorward

Microsoft MVP, Business Applications

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