Things Leaders Do

The AI Bridge: Why Gen X Leaders Are Perfectly Positioned for the Next Big Shift

• Colby Morris

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Gen X leaders have seen it all—floppy disks, dial-up, the rise of email, and the shift to remote work. So why does AI feel like such a curveball? In this episode of Things Leaders Do, Colby Morris explores how Gen X is uniquely positioned to lead through digital transformation—not by mastering every tool, but by mastering what really matters: people-first leadership.

From past transitions to today’s AI moment, Colby walks through five key steps Gen X leaders can use to bring clarity, confidence, and wisdom to an AI-enabled future. You’ll learn how to avoid the common traps of reactive tech adoption and instead guide your team with intentionality, purpose, and trust.

In This Episode, You'll Learn:

  • Why Gen X is the “bridge generation” and how that gives you a strategic advantage
  • What Harvard Business Review says about why most AI initiatives fail
  • A five-step framework for leading AI adoption the right way
  • How to create psychological safety around new technology
  • Real stories of Gen X leaders making AI work—without losing their people

Your Weekly Action Plan:

  1. Identify one real problem AI could help solve.
  2. Pair two team members to explore a use case collaboratively.
  3. Ask each person on your team what questions or concerns they have about AI.
  4. Draft your team’s principles for ethical, human-centered AI adoption.

Connect with Colby:

If you're a Gen X leader navigating uncertainty in the age of AI—this episode is your roadmap. Let’s not fall behind. Let’s lead forward.

Because those are the things that leaders do.


Speaker 1:

Imagine you're back in 1995, pulling a floppy disk out of a PC, getting ready to spend lunch watching a blockbuster tape. No swipe, no cloud, no worry about my Wi-Fi. Fast forward 30 years. Fast forward 30 years. Now you're leading a team that works on Slack, adapts to remote work and expects you to figure out AI before the fiscal quarter ends. Gen X isn't just stuck in the middle of generations. We're the ones who made the shift from analog to digital, and now we're being asked to do it again with artificial intelligence.

Speaker 1:

Hey, I'm Colby Morris and this is Things Leaders Do. The podcast that's all about real-world, people-first leadership. I help leaders design strategies that reshape culture, drive results and bring clarity in the chaos. Today's episode is for every Gen X leader who ever felt caught between fax machines and AI prompts and wondered if we're really ready for this next leap. Here's the truth about being Gen X in this AI moment. We're not catching up. We're leading from experience.

Speaker 1:

Think about your career arc for a second. You started taking handwritten phone messages and ended up managing remote teams across time zones. You learned research projects using actual encyclopedias, then built businesses using Google Analytics, then built businesses using Google Analytics. You remember when email was the disruptive technology that was going to change everything. Here's the contrast. Young leaders see AI as just another app to master. Older leaders sometimes see it as just this overwhelming mountain to climb. But you, You've lived through multiple technology waves. Older leaders sometimes see it as just this overwhelming mountain to climb. But you, you've lived through multiple technology waves and you know something both groups are missing. Technology adoption isn't about the technology, it's about the people. For Gen X leaders, our mental soundtrack should be confidence, not anxiety. We have the lived experience of helping teams navigate massive technological shifts while keeping the human element front and center. Here's what experience has taught us Great leaders focus on what doesn't change, even when everything else does. Hope you wrote that down. I'm going to say it again Great leaders focus on what doesn't change, even when everything else does. People still need clarity, they need trust, they still need to understand how their work matters. That's your advantage in this AI moment. You know how to lead, and you know how to lead people through change because you've done it so many times before.

Speaker 1:

Let me tell you about two different approaches I've seen recently. The reactive leader's response is something like this Everyone's talking about AI, so we need an AI strategy by next quarter. Let's bring in consultants. Let's buy enterprise licenses for every AI tool we can find and mandate that all departments start using AI for everything Right?

Speaker 1:

Six months later, it's chaos. Employees are overwhelmed, productivity is down, leadership is frustrated because they're not seeing the ROI they expected. Now the people first leader response is more like this AI is a tool that can help us solve some real problems, but first we need to understand what problems we're actually trying to solve and we need to bring our people along on the journey. In that same time frame, teams are experimenting thoughtfully. They're finding genuine inefficiencies. They're asking for more opportunities to explore AI applications. Here's what Harvard Business Review found 76% of organizations are experimenting with AI, but only 41% are seeing positive returns with AI, but only 41% are seeing positive returns. The difference isn't in the technology they chose. It's in how they led the change. When AI initiatives fail, it's usually because leaders focused on the tool instead of the team.

Speaker 1:

All right let's get practical. I'm going to give you a five-step approach that treats AI like what it actually is a powerful tool that needs thoughtful implementation. Step one start with problems, not possibilities. Okay, the wrong approach is something like we need to use AI because everyone else is using AI. That doesn't make any sense. The right approach is more like what are the recurring problems that eat up our team's time and energy? Whose operations team was spending six hours every Friday compiling status reports from different departments Six hours every week? Did you hear that? Six hours every week, that's a lot of hours every month. Right, that's a problem worth solving. So we started with Zapier's AI-powered automation to pull data from their project management system, their CRM and their financial dashboard, and then use Cloud to generate consistent first draft reports. The result they got their Fridays back and the reports were actually more consistent than before. Okay, start with one specific annoying problem and solve it well, then you can move on to the next one.

Speaker 1:

Step two build learning partnerships, not mandates. Here's where your bridge building skills really shine. Instead of trying to become the AI expert yourself, create partnerships across generations on your team. Take your most tech curious person often someone in their 20s or 30s and pair them with your most strategic thinker, who might be a little more in that Gen X range. Give them a problem to solve together, but not a tool to learn. You facilitate the partnership, you provide the business context, you remove the obstacles, but you don't have to be the person who knows every AI prompt and feature All right.

Speaker 1:

Step three measure what matters, not what's easy. Every AI experiment needs clear success metrics tied to real business outcomes. Do you hear that Every AI experiment needs clear success metrics tied to real business outcomes? I'm going to give you some examples. Bad metrics we're using AI in three departments now. Well, that doesn't really tell us anything. Good metrics Our customer response time improved by 25% since we started using Intercom's AI chatbot for initial inquiry sorting. That's a good metric. Another bad metric Everyone has completed AI training. Well, yay, a good metric. Our proposal team is now delivering first drafts 40% faster using Jasper AI, while maintaining the same quality standards. Pick two or three meaningful ways to measure success and track them consistently. If everything is important, then nothing is All right.

Speaker 1:

Step four lead the ethics conversation early. This is where your experience with previous technology rollouts becomes invaluable. You remember what happened when companies didn't think through the implications of social media or when they rushed into cloud computing without that proper security protocol. Yeah See, there's a reactive approach and a proactive approach. The reactive says wait until there's a problem, then scramble to create policies. The proactive approach says set clear principles before you need them. Start conversations now about data privacy, about bias in AI outputs, about transparency with customers and employees. Create guidelines for what AI should and shouldn't be used for in your organization and be honest about what you don't know. Say something like we're figuring this out together, but here are the values that will guide our decisions. All right, step five here are the values that will guide our decisions. All right.

Speaker 1:

Step five create psychological safety around learning. Here's probably the most important step and it's pure leadership fundamentals. Your team needs to know that it is safe to ask questions, admit confusion and even express concerns about AI. Some people are worried about job security. Others are overwhelmed by the pace of change. Some are excited but don't know where to start. In your next one-on-one, I want you to ask hey, what's your biggest question or concern about AI right now? And then listen, like really listen. Don't immediately try to solve or dismiss their concern. Create space for that experimentation without penalty Make it clear that thoughtful failure is part of learning. All right, let me share three examples of Gen X leaders who got this right.

Speaker 1:

I'll start with Jennifer. Jennifer runs operations for a midsize manufacturing company. Instead of trying to revolutionize everything at once, she focused on one chronic problem equipment maintenance. Her team was constantly playing catch up with the pairs because they couldn't predict failures. She paired her most experienced maintenance supervisor with a young analyst who understood data patterns. Together, they implemented Microsoft's Azure IoT analytics to analyze equipment sensor data and flag potential issues before they became expensive problems. Six months in, they've prevented four major breakdowns and saved over $200,000 in emergency repairs. More importantly, her team went from reactive to proactive and morale improved dramatically.

Speaker 1:

Now there's Marcus. Marcus leads a sales team that was genuinely worried AI would replace them. Instead of dismissing their concerns, he acknowledged them directly. Then he introduced HubSpot's AI-powered lead scoring and Salesforce Einstein for automated data entry and initial lead qualification. Now his team spends more time building relationships with qualified prospects instead of sorting through cold leads. Sales numbers are up 18% and, as people feel like AI made their jobs better, not threaten them, lisa is another one. Lisa's company hired a chief AI officer with big promises and a bigger budget, but nothing meaningful happened until Lisa and her fellow department heads created cross-functional working groups that were going to actually test tools and share learning, vision and resources. But the middle managers, that's who made it real by focusing on specific use cases and bringing their teams along step by step.

Speaker 1:

What's the common thread here? All three leaders treated AI adoption like any other change management challenge People first, technology second. Here's what I want you to do before next Friday. First, identify one recurring problem in your organization that eats up time or creates frustration. Not necessarily the biggest problem, not the sexiest problem, just one problem that AI might actually help solve. Second, find two people on your team with different skill sets and perspectives. Set up a 45-minute working session where you explore one AI tool together. I want you to make it collaborative, though not a training session.

Speaker 1:

And third, in your next one-on-one with each team member, ask this question what questions or concerns do you have about AI and how it might affect our work? And then listen. Listen without trying to fix everything immediately. And finally, draft a simple one-page document with your team's principles for AI adoption. Keep it straightforward we test small before we scale. We protect customer data. We support each other's learning, we focus on solving real problems. And that's it.

Speaker 1:

Four concrete steps that build on what you already know about leading people through change. Look, we've spent our careers helping people navigate transformation. We took teams through the shift from paper to digital, from office-based to remote work, from hierarchical communication to collaborative platforms. Ai isn't fundamentally different from those transitions. It's more powerful, yes, it's moving faster, absolutely, but the core challenge remains the same helping people embrace new tools while maintaining focus on what really matters serving customers, building relationships and creating value. We're not too old for this. We're not behind the curve. We're exactly where we need to be with exactly the experience this moment requires.

Speaker 1:

The younger leaders have technical fluency, but sometimes lack the wisdom to know when to slow down and bring people along. The older leaders have business wisdom, but sometimes get overwhelmed by the pace of change. You have both you understand technology, adoption and you know how to lead people through uncertainty. That combination is exactly what organizations need right now. Believe it, hey. If this resonated with you, share it with another Gen X leader or just another leader in general who's working through their AI strategy. And if you want to dig deeper into people-first leadership, whether that's executive coaching, team development or speaking at your next leadership event, visit nextstepadvisorscom. There's no E, just NXT nextstepadvisorscom. I want to thank you for listening to Things Leaders Do Keep leading people through technological change, building trust in times of uncertainty, helping teams embrace new tools while staying focused on human connection, navigating AI transformation with wisdom and clarity. And you know why? Because those are the things that leaders do.

Speaker 2:

Thank you for listening to Things Leaders Do. If you're looking for more tips on how to be a better leader, be sure to subscribe to the podcast and listen to next week's episode. Until next time, keep working on being a better leader by doing the things that leaders do.