July 29, 2025 • 16 mins
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:
- Identify one real problem AI could help solve.
- Pair two team members to explore a use case collaboratively.
- Ask each person on your team what questions or concerns they have about AI.
- 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.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Imagine you're back in 1995, pulling a floppy disk
out of a PC, getting ready tospend lunch watching a
blockbuster tape.
No swipe, no cloud, no worryabout my Wi-Fi.
Fast forward 30 years.
Fast forward 30 years.
(00:23):
Now you're leading a team thatworks on Slack, adapts to remote
work and expects you to figureout AI before the fiscal quarter
ends.
Gen X isn't just stuck in themiddle of generations.
We're the ones who made theshift from analog to digital,
and now we're being asked to doit again with artificial
intelligence.
(00:45):
Hey, I'm Colby Morris and thisis Things Leaders Do.
The podcast that's all aboutreal-world, people-first
leadership.
I help leaders designstrategies that reshape culture,
drive results and bring clarityin the chaos.
Today's episode is for everyGen X leader who ever felt
(01:06):
caught between fax machines andAI prompts and wondered if we're
really ready for this next leap.
Here's the truth about beingGen X in this AI moment.
We're not catching up.
We're leading from experience.
Think about your career arc fora second.
(01:27):
You started taking handwrittenphone messages and ended up
managing remote teams acrosstime zones.
You learned research projectsusing actual encyclopedias, then
built businesses using GoogleAnalytics, then built businesses
using Google Analytics.
(01:48):
You remember when email was thedisruptive technology that was
going to change everything.
Here's the contrast.
Young leaders see AI as justanother app to master.
Older leaders sometimes see itas just this overwhelming
mountain to climb.
But you, You've lived throughmultiple technology waves.
Older leaders sometimes see itas just this overwhelming
(02:09):
mountain to climb.
But you, you've lived throughmultiple technology waves and
you know something both groupsare missing.
Technology adoption isn't aboutthe technology, it's about the
people.
For Gen X leaders, our mentalsoundtrack should be confidence,
not anxiety.
We have the lived experience ofhelping teams navigate massive
(02:37):
technological shifts whilekeeping the human element front
and center.
Here's what experience hastaught us Great leaders focus on
what doesn't change, even wheneverything else does.
Hope you wrote that down.
I'm going to say it again Greatleaders focus on what doesn't
change, even when everythingelse does.
(02:59):
People still need clarity, theyneed trust, they still need to
understand how their workmatters.
That's your advantage in thisAI moment.
You know how to lead, and youknow how to lead people through
change because you've done it somany times before.
(03:22):
Let me tell you about twodifferent approaches I've seen
recently.
The reactive leader's responseis something like this
Everyone's talking about AI, sowe need an AI strategy by next
quarter.
Let's bring in consultants.
Let's buy enterprise licensesfor every AI tool we can find
and mandate that all departmentsstart using AI for everything
(03:48):
Right?
Six months later, it's chaos.
Employees are overwhelmed,productivity is down, leadership
is frustrated because they'renot seeing the ROI they expected
.
Now the people first leaderresponse is more like this AI is
a tool that can help us solvesome real problems, but first we
(04:11):
need to understand whatproblems we're actually trying
to solve and we need to bringour people along on the journey.
In that same time frame, teamsare experimenting thoughtfully.
They're finding genuineinefficiencies.
They're asking for moreopportunities to explore AI
(04:32):
applications.
Here's what Harvard BusinessReview found 76% of
organizations are experimentingwith AI, but only 41% are seeing
positive returns with AI, butonly 41% are seeing positive
returns.
The difference isn't in thetechnology they chose.
(04:53):
It's in how they led the change.
When AI initiatives fail, it'susually because leaders focused
on the tool instead of the team.
All right let's get practical.
I'm going to give you afive-step approach that treats
AI like what it actually is apowerful tool that needs
thoughtful implementation.
(05:14):
Step one start with problems,not possibilities.
Okay, the wrong approach issomething like we need to use AI
because everyone else is usingAI.
That doesn't make any sense.
The right approach is more likewhat are the recurring problems
(05:35):
that eat up our team's time andenergy?
Whose operations team wasspending six hours every Friday
compiling status reports fromdifferent departments Six hours
every week?
Did you hear that?
Six hours every week, that's alot of hours every month.
(05:59):
Right, that's a problem worthsolving.
So we started with Zapier'sAI-powered automation to pull
data from their projectmanagement system, their CRM and
their financial dashboard, andthen use Cloud to generate
consistent first draft reports.
The result they got theirFridays back and the reports
(06:21):
were actually more consistentthan before.
Okay, start with one specificannoying problem and solve it
well, then you can move on tothe next one.
Step two build learningpartnerships, not mandates.
Here's where your bridgebuilding skills really shine.
(06:44):
Instead of trying to become theAI expert yourself, create
partnerships across generationson your team.
Take your most tech curiousperson often someone in their
20s or 30s and pair them withyour most strategic thinker, who
might be a little more in thatGen X range.
(07:05):
Give them a problem to solvetogether, but not a tool to
learn.
You facilitate the partnership,you provide the business
context, you remove theobstacles, but you don't have to
be the person who knows everyAI prompt and feature All right.
Step three measure what matters,not what's easy.
(07:29):
Every AI experiment needs clearsuccess metrics tied to real
business outcomes.
Do you hear that Every AIexperiment needs clear success
metrics tied to real businessoutcomes?
I'm going to give you someexamples.
(07:49):
Bad metrics we're using AI inthree departments now.
Well, that doesn't really tellus anything.
Good metrics Our customerresponse time improved by 25%
since we started usingIntercom's AI chatbot for
initial inquiry sorting.
That's a good metric.
(08:11):
Another bad metric Everyone hascompleted AI training.
Well, yay, a good metric.
Our proposal team is nowdelivering first drafts 40%
faster using Jasper AI, whilemaintaining the same quality
standards.
Pick two or three meaningfulways to measure success and
(08:35):
track them consistently.
If everything is important,then nothing is All right.
Step four lead the ethicsconversation early.
This is where your experiencewith previous technology
rollouts becomes invaluable.
You remember what happened whencompanies didn't think through
(08:55):
the implications of social mediaor when they rushed into cloud
computing without that propersecurity protocol.
Yeah See, there's a reactiveapproach and a proactive
approach.
The reactive says wait untilthere's a problem, then scramble
to create policies.
The proactive approach says setclear principles before you
(09:19):
need them.
Start conversations now aboutdata privacy, about bias in AI
outputs, about transparency withcustomers and employees.
Create guidelines for what AIshould and shouldn't be used for
in your organization and behonest about what you don't know
.
Say something like we'refiguring this out together, but
(09:43):
here are the values that willguide our decisions.
All right, step five here arethe values that will guide our
decisions.
All right.
Step five create psychologicalsafety around learning.
Here's probably the mostimportant step and it's pure
leadership fundamentals.
Your team needs to know that itis safe to ask questions, admit
(10:05):
confusion and even expressconcerns about AI.
Some people are worried aboutjob security.
Others are overwhelmed by thepace of change.
Some are excited but don't knowwhere to start.
In your next one-on-one, I wantyou to ask hey, what's your
biggest question or concernabout AI right now?
And then listen, like reallylisten.
(10:28):
Don't immediately try to solveor dismiss their concern.
Create space for thatexperimentation without penalty
Make it clear that thoughtfulfailure is part of learning.
All right, let me share threeexamples of Gen X leaders who
got this right.
I'll start with Jennifer.
(10:48):
Jennifer runs operations for amidsize manufacturing company.
Instead of trying torevolutionize everything at once
, she focused on one chronicproblem equipment maintenance.
Her team was constantly playingcatch up with the pairs because
they couldn't predict failures.
She paired her most experiencedmaintenance supervisor with a
(11:09):
young analyst who understooddata patterns.
Together, they implementedMicrosoft's Azure IoT analytics
to analyze equipment sensor dataand flag potential issues
before they became expensiveproblems.
Six months in, they'veprevented four major breakdowns
and saved over $200,000 inemergency repairs.
(11:33):
More importantly, her team wentfrom reactive to proactive and
morale improved dramatically.
Now there's Marcus.
Marcus leads a sales team thatwas genuinely worried AI would
replace them.
Instead of dismissing theirconcerns, he acknowledged them
(11:54):
directly.
Then he introduced HubSpot'sAI-powered lead scoring and
Salesforce Einstein forautomated data entry and initial
lead qualification.
Now his team spends more timebuilding relationships with
qualified prospects instead ofsorting through cold leads.
Sales numbers are up 18% and,as people feel like AI made
(12:16):
their jobs better, not threatenthem, lisa is another one.
Lisa's company hired a chief AIofficer with big promises and a
bigger budget, but nothingmeaningful happened until Lisa
and her fellow department headscreated cross-functional working
groups that were going toactually test tools and share
(12:39):
learning, vision and resources.
But the middle managers, that'swho made it real by focusing on
specific use cases and bringingtheir teams along step by step.
What's the common thread here?
All three leaders treated AIadoption like any other change
(13:01):
management challenge Peoplefirst, technology second.
Here's what I want you to dobefore next Friday.
First, identify one recurringproblem in your organization
that eats up time or createsfrustration.
Not necessarily the biggestproblem, not the sexiest problem
(13:22):
, just one problem that AI mightactually help solve.
Second, find two people on yourteam with different skill sets
and perspectives.
Set up a 45-minute workingsession where you explore one AI
tool together.
I want you to make itcollaborative, though not a
training session.
(13:42):
And third, in your nextone-on-one with each team member
, ask this question whatquestions or concerns do you
have about AI and how it mightaffect our work?
And then listen.
Listen without trying to fixeverything immediately.
And finally, draft a simpleone-page document with your
(14:03):
team's principles for AIadoption.
Keep it straightforward we testsmall before we scale.
We protect customer data.
We support each other'slearning, we focus on solving
real problems.
And that's it.
Four concrete steps that buildon what you already know about
(14:24):
leading people through change.
Look, we've spent our careershelping people navigate
transformation.
We took teams through the shiftfrom paper to digital, from
office-based to remote work,from hierarchical communication
to collaborative platforms.
Ai isn't fundamentallydifferent from those transitions
(14:47):
.
It's more powerful, yes, it'smoving faster, absolutely, but
the core challenge remains thesame helping people embrace new
tools while maintaining focus onwhat really matters serving
customers, buildingrelationships and creating value
.
We're not too old for this.
We're not behind the curve.
(15:09):
We're exactly where we need tobe with exactly the experience
this moment requires.
The younger leaders havetechnical fluency, but sometimes
lack the wisdom to know when toslow down and bring people
along.
The older leaders have businesswisdom, but sometimes get
overwhelmed by the pace ofchange.
(15:30):
You have both you understandtechnology, adoption and you
know how to lead people throughuncertainty.
That combination is exactlywhat organizations need right
now.
Believe it, hey.
If this resonated with you,share it with another Gen X
(15:51):
leader or just another leader ingeneral who's working through
their AI strategy.
And if you want to dig deeperinto people-first leadership,
whether that's executivecoaching, team development or
speaking at your next leadershipevent, visit
nextstepadvisorscom.
There's no E, just NXTnextstepadvisorscom.
I want to thank you forlistening to Things Leaders Do
(16:15):
Keep leading people throughtechnological change, building
trust in times of uncertainty,helping teams embrace new tools
while staying focused on humanconnection, navigating AI
transformation with wisdom andclarity.
And you know why?
Because those are the thingsthat leaders do.
Speaker 2 (16:39):
Thank you for listening to Things Leaders Do.
If you're looking for more tipson how to be a better leader,
be sure to subscribe to thepodcast and listen to next
week's episode.
Until next time, keep workingon being a better leader by
doing the things that leaders do.
Imagine you're back in 1995, pulling a floppy disk
out of a PC, getting ready tospend lunch watching a
blockbuster tape.
No swipe, no cloud, no worryabout my Wi-Fi.
Fast forward 30 years.
Fast forward 30 years.
(00:23):
Now you're leading a team thatworks on Slack, adapts to remote
work and expects you to figureout AI before the fiscal quarter
ends.
Gen X isn't just stuck in themiddle of generations.
We're the ones who made theshift from analog to digital,
and now we're being asked to doit again with artificial
intelligence.
(00:45):
Hey, I'm Colby Morris and thisis Things Leaders Do.
The podcast that's all aboutreal-world, people-first
leadership.
I help leaders designstrategies that reshape culture,
drive results and bring clarityin the chaos.
Today's episode is for everyGen X leader who ever felt
(01:06):
caught between fax machines andAI prompts and wondered if we're
really ready for this next leap.
Here's the truth about beingGen X in this AI moment.
We're not catching up.
We're leading from experience.
Think about your career arc fora second.
(01:27):
You started taking handwrittenphone messages and ended up
managing remote teams acrosstime zones.
You learned research projectsusing actual encyclopedias, then
built businesses using GoogleAnalytics, then built businesses
using Google Analytics.
(01:48):
You remember when email was thedisruptive technology that was
going to change everything.
Here's the contrast.
Young leaders see AI as justanother app to master.
Older leaders sometimes see itas just this overwhelming
mountain to climb.
But you, You've lived throughmultiple technology waves.
Older leaders sometimes see itas just this overwhelming
(02:09):
mountain to climb.
But you, you've lived throughmultiple technology waves and
you know something both groupsare missing.
Technology adoption isn't aboutthe technology, it's about the
people.
For Gen X leaders, our mentalsoundtrack should be confidence,
not anxiety.
We have the lived experience ofhelping teams navigate massive
(02:37):
technological shifts whilekeeping the human element front
and center.
Here's what experience hastaught us Great leaders focus on
what doesn't change, even wheneverything else does.
Hope you wrote that down.
I'm going to say it again Greatleaders focus on what doesn't
change, even when everythingelse does.
(02:59):
People still need clarity, theyneed trust, they still need to
understand how their workmatters.
That's your advantage in thisAI moment.
You know how to lead, and youknow how to lead people through
change because you've done it somany times before.
(03:22):
Let me tell you about twodifferent approaches I've seen
recently.
The reactive leader's responseis something like this
Everyone's talking about AI, sowe need an AI strategy by next
quarter.
Let's bring in consultants.
Let's buy enterprise licensesfor every AI tool we can find
and mandate that all departmentsstart using AI for everything
(03:48):
Right?
Six months later, it's chaos.
Employees are overwhelmed,productivity is down, leadership
is frustrated because they'renot seeing the ROI they expected
.
Now the people first leaderresponse is more like this AI is
a tool that can help us solvesome real problems, but first we
(04:11):
need to understand whatproblems we're actually trying
to solve and we need to bringour people along on the journey.
In that same time frame, teamsare experimenting thoughtfully.
They're finding genuineinefficiencies.
They're asking for moreopportunities to explore AI
(04:32):
applications.
Here's what Harvard BusinessReview found 76% of
organizations are experimentingwith AI, but only 41% are seeing
positive returns with AI, butonly 41% are seeing positive
returns.
The difference isn't in thetechnology they chose.
(04:53):
It's in how they led the change.
When AI initiatives fail, it'susually because leaders focused
on the tool instead of the team.
All right let's get practical.
I'm going to give you afive-step approach that treats
AI like what it actually is apowerful tool that needs
thoughtful implementation.
(05:14):
Step one start with problems,not possibilities.
Okay, the wrong approach issomething like we need to use AI
because everyone else is usingAI.
That doesn't make any sense.
The right approach is more likewhat are the recurring problems
(05:35):
that eat up our team's time andenergy?
Whose operations team wasspending six hours every Friday
compiling status reports fromdifferent departments Six hours
every week?
Did you hear that?
Six hours every week, that's alot of hours every month.
(05:59):
Right, that's a problem worthsolving.
So we started with Zapier'sAI-powered automation to pull
data from their projectmanagement system, their CRM and
their financial dashboard, andthen use Cloud to generate
consistent first draft reports.
The result they got theirFridays back and the reports
(06:21):
were actually more consistentthan before.
Okay, start with one specificannoying problem and solve it
well, then you can move on tothe next one.
Step two build learningpartnerships, not mandates.
Here's where your bridgebuilding skills really shine.
(06:44):
Instead of trying to become theAI expert yourself, create
partnerships across generationson your team.
Take your most tech curiousperson often someone in their
20s or 30s and pair them withyour most strategic thinker, who
might be a little more in thatGen X range.
(07:05):
Give them a problem to solvetogether, but not a tool to
learn.
You facilitate the partnership,you provide the business
context, you remove theobstacles, but you don't have to
be the person who knows everyAI prompt and feature All right.
Step three measure what matters,not what's easy.
(07:29):
Every AI experiment needs clearsuccess metrics tied to real
business outcomes.
Do you hear that Every AIexperiment needs clear success
metrics tied to real businessoutcomes?
I'm going to give you someexamples.
(07:49):
Bad metrics we're using AI inthree departments now.
Well, that doesn't really tellus anything.
Good metrics Our customerresponse time improved by 25%
since we started usingIntercom's AI chatbot for
initial inquiry sorting.
That's a good metric.
(08:11):
Another bad metric Everyone hascompleted AI training.
Well, yay, a good metric.
Our proposal team is nowdelivering first drafts 40%
faster using Jasper AI, whilemaintaining the same quality
standards.
Pick two or three meaningfulways to measure success and
(08:35):
track them consistently.
If everything is important,then nothing is All right.
Step four lead the ethicsconversation early.
This is where your experiencewith previous technology
rollouts becomes invaluable.
You remember what happened whencompanies didn't think through
(08:55):
the implications of social mediaor when they rushed into cloud
computing without that propersecurity protocol.
Yeah See, there's a reactiveapproach and a proactive
approach.
The reactive says wait untilthere's a problem, then scramble
to create policies.
The proactive approach says setclear principles before you
(09:19):
need them.
Start conversations now aboutdata privacy, about bias in AI
outputs, about transparency withcustomers and employees.
Create guidelines for what AIshould and shouldn't be used for
in your organization and behonest about what you don't know
.
Say something like we'refiguring this out together, but
(09:43):
here are the values that willguide our decisions.
All right, step five here arethe values that will guide our
decisions.
All right.
Step five create psychologicalsafety around learning.
Here's probably the mostimportant step and it's pure
leadership fundamentals.
Your team needs to know that itis safe to ask questions, admit
(10:05):
confusion and even expressconcerns about AI.
Some people are worried aboutjob security.
Others are overwhelmed by thepace of change.
Some are excited but don't knowwhere to start.
In your next one-on-one, I wantyou to ask hey, what's your
biggest question or concernabout AI right now?
And then listen, like reallylisten.
(10:28):
Don't immediately try to solveor dismiss their concern.
Create space for thatexperimentation without penalty
Make it clear that thoughtfulfailure is part of learning.
All right, let me share threeexamples of Gen X leaders who
got this right.
I'll start with Jennifer.
(10:48):
Jennifer runs operations for amidsize manufacturing company.
Instead of trying torevolutionize everything at once
, she focused on one chronicproblem equipment maintenance.
Her team was constantly playingcatch up with the pairs because
they couldn't predict failures.
She paired her most experiencedmaintenance supervisor with a
(11:09):
young analyst who understooddata patterns.
Together, they implementedMicrosoft's Azure IoT analytics
to analyze equipment sensor dataand flag potential issues
before they became expensiveproblems.
Six months in, they'veprevented four major breakdowns
and saved over $200,000 inemergency repairs.
(11:33):
More importantly, her team wentfrom reactive to proactive and
morale improved dramatically.
Now there's Marcus.
Marcus leads a sales team thatwas genuinely worried AI would
replace them.
Instead of dismissing theirconcerns, he acknowledged them
(11:54):
directly.
Then he introduced HubSpot'sAI-powered lead scoring and
Salesforce Einstein forautomated data entry and initial
lead qualification.
Now his team spends more timebuilding relationships with
qualified prospects instead ofsorting through cold leads.
Sales numbers are up 18% and,as people feel like AI made
(12:16):
their jobs better, not threatenthem, lisa is another one.
Lisa's company hired a chief AIofficer with big promises and a
bigger budget, but nothingmeaningful happened until Lisa
and her fellow department headscreated cross-functional working
groups that were going toactually test tools and share
(12:39):
learning, vision and resources.
But the middle managers, that'swho made it real by focusing on
specific use cases and bringingtheir teams along step by step.
What's the common thread here?
All three leaders treated AIadoption like any other change
(13:01):
management challenge Peoplefirst, technology second.
Here's what I want you to dobefore next Friday.
First, identify one recurringproblem in your organization
that eats up time or createsfrustration.
Not necessarily the biggestproblem, not the sexiest problem
(13:22):
, just one problem that AI mightactually help solve.
Second, find two people on yourteam with different skill sets
and perspectives.
Set up a 45-minute workingsession where you explore one AI
tool together.
I want you to make itcollaborative, though not a
training session.
(13:42):
And third, in your nextone-on-one with each team member
, ask this question whatquestions or concerns do you
have about AI and how it mightaffect our work?
And then listen.
Listen without trying to fixeverything immediately.
And finally, draft a simpleone-page document with your
(14:03):
team's principles for AIadoption.
Keep it straightforward we testsmall before we scale.
We protect customer data.
We support each other'slearning, we focus on solving
real problems.
And that's it.
Four concrete steps that buildon what you already know about
(14:24):
leading people through change.
Look, we've spent our careershelping people navigate
transformation.
We took teams through the shiftfrom paper to digital, from
office-based to remote work,from hierarchical communication
to collaborative platforms.
Ai isn't fundamentallydifferent from those transitions
(14:47):
.
It's more powerful, yes, it'smoving faster, absolutely, but
the core challenge remains thesame helping people embrace new
tools while maintaining focus onwhat really matters serving
customers, buildingrelationships and creating value
.
We're not too old for this.
We're not behind the curve.
(15:09):
We're exactly where we need tobe with exactly the experience
this moment requires.
The younger leaders havetechnical fluency, but sometimes
lack the wisdom to know when toslow down and bring people
along.
The older leaders have businesswisdom, but sometimes get
overwhelmed by the pace ofchange.
(15:30):
You have both you understandtechnology, adoption and you
know how to lead people throughuncertainty.
That combination is exactlywhat organizations need right
now.
Believe it, hey.
If this resonated with you,share it with another Gen X
(15:51):
leader or just another leader ingeneral who's working through
their AI strategy.
And if you want to dig deeperinto people-first leadership,
whether that's executivecoaching, team development or
speaking at your next leadershipevent, visit
nextstepadvisorscom.
There's no E, just NXTnextstepadvisorscom.
I want to thank you forlistening to Things Leaders Do
(16:15):
Keep leading people throughtechnological change, building
trust in times of uncertainty,helping teams embrace new tools
while staying focused on humanconnection, navigating AI
transformation with wisdom andclarity.
And you know why?
Because those are the thingsthat leaders do.
Speaker 2 (16:39):
Thank you for listening to Things Leaders Do.
If you're looking for more tipson how to be a better leader,
be sure to subscribe to thepodcast and listen to next
week's episode.
Until next time, keep workingon being a better leader by
doing the things that leaders do.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
The Herd with Colin Cowherd
The Herd with Colin Cowherd is a thought-provoking, opinionated, and topic-driven journey through the top sports stories of the day.