I recently came across research that stopped me in my tracks.
McKinsey’s 2025 AI workplace report found that high performers are three times more likely than their peers to have senior leaders who demonstrate ownership of and commitment to AI initiatives.
Three times.
Not marginally better. Not slightly ahead. Three times more successful.
Here’s the part that hit home with me - and should with you as well: The biggest barrier to AI implementation success isn’t technology, budget, or employee readiness.
It’s leadership.
If you’re reading this and you’re in your 40s, 50s, or 60s - if you’ve spent decades building expertise and earning leadership roles - this is your moment.
Your age isn’t the barrier to AI adoption. You’re the unlock.
The Numbers Tell a Story
Here’s what the research actually shows.
70 to 85% of AI initiatives fail. Most companies abandon these projects within months. The data shows abandoned AI projects jumped from 17% to 42% recently—implementation is getting harder, not easier.
Only 6% of companies are actually winning with AI—generating more than 5% EBIT impact.
So what separates that 6% from everyone else?
Not budget. BCG found that high performers prioritize depth over breadth, focusing on an average of 3.5 use cases compared with 6.1 for other companies—and generating 2.1 times greater ROI.
Not technology. AI future-built companies achieve five times the revenue increases and three times the cost reductions because of how they implement, not what tools they use.
The difference is leadership.
77% of ML implementation leaders had C-level leadership driving their projects. For 44% of leaders, digital projects were sponsored by the CEO or board.
Here’s the part that matters: High performers are much more likely than others to say that senior leaders are actively engaged in driving AI adoption—including role modeling the use of AI.
Role modeling. Not just approving budgets. Not just attending presentations. Actually using AI themselves.
The Leadership Gap (And Why It’s Good News for You)
Now here’s where it gets interesting.
McKinsey found something remarkable: C-suite executives are more than twice as likely to blame employee readiness for AI adoption challenges than to examine their own role.
Meaning, the biggest barrier to AI success is leadership. Yet leadership is twice as likely to point fingers at employees than to look in the mirror.
This is actually great news if you’re willing to lead differently.
If you’re someone who actually uses AI, who’s willing to experiment, who understands what it can and can’t do—you’re already ahead of most executives at most companies.
Why Experience is the Difference
Here’s something that cannot be said loudly enough: The skills you need to make AI work are exactly the ones you’ve spent decades building.
AI isn’t a technical problem. It’s a people problem. It’s about knowing which changes will stick and which ones will get quietly ignored after the kickoff meeting.
We all have watched organizations try to change before. We know how people actually behave when they’re told to do something new. We remember when everyone said email would never replace phone calls, when the internet would never replace in-person meetings, when social media was just a fad for teenagers.
We’ve lived through enough hype cycles to know the difference between real change and expensive distractions.
That’s not a liability. That’s exactly what makes us valuable right now.
What Actually Matters
The research is clear about what separates the winners from everyone else:
1. Pick a Few Things That Matter, Not Everything
High performers focus on 3-4 things where AI actually changes outcomes. Everyone else runs 15 pilot projects that quietly die in committee.
You’ve seen enough “strategic initiatives” fail to know the difference. You know what real transformation looks like versus what looks good in a PowerPoint.
2. Actually Change How Work Gets Done
Half of the companies winning with AI are redesigning how people work, not just adding AI on top of broken processes.
You know where the real problems are. You know which processes everyone complains about but nobody has fixed. You know what people will actually do versus what they’ll nod along with in meetings.
Junior leaders don’t have that knowledge yet.
3. Get Different Parts of the Company Actually Talking
The companies making this work get marketing, operations, IT, and finance in the same room actually collaborating.
You’ve spent years building relationships across departments. You know who the real decision-makers are. You know which battles are worth fighting and which ones to go around.
That’s not “soft skills.” That’s how things actually get done.
4. Use It Yourself
Here’s the most important finding: When senior leaders actually use AI, the number of employees who feel positive about it goes from 15% to 55%.
15% to 55%. Just from showing people you use it.
This isn’t about being the most technical person in the room. It’s about showing your team that AI is something you actually use to do your job, not something you’re forcing on them while you stick to email and spreadsheets.
You don’t need to be an AI engineer. You just need to use the tools and not be afraid to say “I’m still figuring this out” when you are.
What This Actually Looks Like
I’ve watched this play out at real companies. The difference between AI that works and expensive failure comes down to three things senior leaders do.
They Use AI Themselves
41% of CEOs are already testing AI—more than any other leader group. Not because they’re more technical. Because they recognize that credibility requires personal experience.
This doesn’t mean you need to become a prompt engineering expert. It means using ChatGPT or Claude for actual work:
- Research before strategy meetings
- First drafts of presentations or memos
- Analysis of complex decisions
- Learning about topics outside your expertise
The message this sends: “AI is a professional tool I use to do my job better, not a threat I’m defending against.”
They Make It Safe to Try and Fail
The executives who make this work let people experiment without worrying they’ll get in trouble for wasting time.
This requires something only experience gives you: the confidence to be wrong in front of your team.
Junior leaders are terrified of looking stupid. They need everyone to think they have all the answers. You’ve been around long enough to know that learning anything new means sucking at it first—and that’s fine.
When you say “I tried using AI for this analysis and honestly, it was a disaster the first time, but here’s what I figured out,” you give your team permission to try things and screw up.
That changes everything.
They Focus on What Actually Matters
The companies winning with AI use it to grow and innovate, not just cut costs.
This takes the kind of thinking you only get from experience. You know:
- Which problems are worth solving versus which ones just sound impressive
- What “better” actually looks like, not what consultants say it should look like
- How to know if something’s working beyond “the report says ROI is positive”
- Who needs to be on board for this to actually happen
You’re not just playing with technology. You’re connecting it to things that matter.
That’s the difference between expensive pilots and real results.
The Age Advantage Nobody Talks About
Here’s the uncomfortable truth: Young executives often push AI for the wrong reasons.
They want to look innovative. They’re terrified of seeming behind the times. They read a headline about ChatGPT and think “we need to do something with AI” without knowing what problem they’re trying to solve.
So they launch pilots that everyone knows will quietly die in six months.
You approach this differently. You’ve watched enough technology fads come and go to ask better questions:
- What problem does this actually solve?
- How does this change what we deliver to customers?
- What do we need to change for this to work?
- What are we willing to stop doing to make space for this?
These aren’t technical questions. They’re judgment questions. And asking them early prevents most AI failures.
Leaders who understand AI are 45% more likely to hit their goals—not because of technical skill, but because they ask better questions and know what actually matters.
Your Moment
If you’re in any kind of leadership role—formal or not—you have a window right now that won’t stay open forever.
Most organizations are still figuring this out. The ones who get it right will pull ahead. The ones who don’t will spend years trying to catch up.
This moment rewards people who can:
- Learn AI tools fast enough to actually use them
- Tell the difference between real opportunities and hype
- Get people to change how they work (which is harder than it sounds)
- Get different departments working together instead of protecting their turf
- Be okay with not knowing everything and learning in public
Notice something about that list? Every single one comes from experience, not youth.
You spent decades building exactly these skills.
Now you just need to learn the tools.
How to Start (Monday Morning)
Leadership isn’t about permission or titles. It’s about what you actually do.
Here’s how to start leading AI adoption—whether or not it’s officially your job.
This Week: Personal Use
Pick one task you do regularly that takes time but doesn’t require your strategic judgment:
- Research for presentations
- First drafts of routine emails or documents
- Basic data analysis or reporting
- Meeting prep or follow-up
Use ChatGPT or Claude for it. Refine the output with your expertise. Measure the time saved.
This accomplishes two things:
- You build actual experience with AI tools
- You create your first proof point
This Month: Visible Experimentation
Share what you’re learning. Not as “I’m an AI expert now” but as “Here’s what I’m trying and what’s working.”
In meetings, say things like:
- “I used AI to research this topic and here’s what I found…”
- “I’m experimenting with AI for X, and I’d be happy to show anyone interested…”
- “AI helped me draft this, but I added the strategic context because…”
This does something crucial: It makes AI normal. It shows your team that learning new tools is professional behavior, not a threat.
This Quarter: Pick One Real Problem
Find one workflow in your area where AI could actually help. Not “let’s try AI on something” but “here’s a real problem AI might solve.”
What you need:
- A clear idea of what “better” looks like
- Willingness to change how work gets done, not just bolt AI onto a broken process
- A way to tell if it’s actually working
Start small. Prove it works. Then expand.
Write down what works and what doesn’t. This becomes your playbook.
This Year: Help Others Get Started
Once you’ve proven AI works for you, help it spread.
This doesn’t mean forcing it on people who aren’t ready. It means:
- Showing how AI helps with problems people actually care about
- Finding the 2-3 places where AI would make the biggest difference
- Finding other people who are curious and want to experiment
- Making it safe for people to try without fear of wasting time
- Showing what’s working and what isn’t
You don’t need to be the most technical person. You need to be the person who connects the dots between what AI can do and what actually needs doing.
That’s exactly what your experience taught you to do.
The Real Barrier Isn’t Age
I started by saying the biggest barrier to AI implementation success is leadership.
More specifically, it’s leadership that approves AI projects but doesn’t actually engage with the tools. Leadership that delegates AI adoption to junior staff. Leadership that treats AI as someone else’s responsibility.
The research shows clearly: Real genAI adoption starts and succeeds when the C-suite is aligned. When executives demonstrate personal use, model experimentation, and connect AI to what actually matters.
If you’re worried you’re “too old” to lead this, you’re wrong.
Your age means you have:
- The pattern recognition to know what’s real and what’s hype
- The relationships and credibility to actually get things done
- The confidence to try things and admit when you’re still learning
- The wisdom to know which battles matter
- The connections across the company to make collaboration happen
These are exactly what separates the 6% who win with AI from everyone else.
The question isn’t whether you can lead this.
It’s whether you will.
The Bottom Line
Senior management support isn’t just helpful for AI success. It’s the single biggest factor.
Which means your experience, your relationships, your credibility—these aren’t holding you back from AI. They’re exactly what’s needed.
You don’t need to be the most technical person at your company. You need to be the person who uses AI yourself, makes it safe for others to try, and connects what AI can do to what actually needs doing.
That’s not a technical job. It’s a leadership job.
And if there’s one thing your decades of experience prepared you for, it’s leadership.
The data is clear. The opportunity is real. The question is simple.
Will you lead?
Sources
- 200+ AI Statistics & Trends for 2025: The Ultimate Roundup
- The state of AI in 2025: Agents, innovation, and transformation - McKinsey
- AI in the workplace: A report for 2025 - McKinsey
- Are You Generating Value from AI? The Widening Gap - BCG
- AI at Work 2025: Momentum Builds, but Gaps Remain - BCG
- From Potential to Profit: Closing the AI Impact Gap - BCG
- The impact of AI in business: what the reports show
- Want AI Adoption to Be Successful? Train the C-Suite First - Inc.com
- Six pillars of AI success for the C-suite - EY
- C-Suite Playbook: 3 Simple Steps for AI Success - Workday
- GenAI Transformation Starts at the Top: Why C-Suite Alignment Is the Key - Integreon
- AI in the C-Suite: Transforming Business Strategy with Executive Insights - Nutanix
