40% of agentic AI projects will be abandoned by 2027 (not due to tech failure, but failed processes)
Future leadership requires less expertise, more judgment, discernment, and humanity
AI amplifies existing capabilities—weak organizations get weaker, strong ones get stronger
Technology is not the hard part anymore; organizational operating models are
Culture is a strategic asset that determines transformation success, not HR initiative
The ‘catcher’s position’—observation and seeing patterns—becomes MORE valuable in the AI era
The Reality Check: Why 40% of AI Projects Will Fail
For years, technology leaders have been told that digital transformation is about implementing the latest tools. But as organizations rush to embrace AI and agentic systems, a sobering reality is emerging. Gartner predicts that by 2027, 40% of agentic AI projects will be abandoned, not because the technology fails, but because organizations try to automate broken processes rather than reimagine how work gets done.
Few executives understand this challenge better than Julie Averill. As EVP and Global Chief Information Officer at Lululemon, she helped lead a technology transformation that supported the company’s growth from $2 billion to more than $10 billion in revenue. Along the way, she built global teams and operating models designed not just to scale technology, but to scale trust, culture, and business impact.
In her new book, Chief Impact Officer, Julie draws on lessons from some of the world’s most recognized brands. From Lululemon’s culture of radical honesty that fueled breakthrough growth, to Nordstrom’s evolution of technology from a back-office function to a strategic business driver, to REI’s use of technology failures as catalysts for trust and transformation, the book offers a fresh perspective on what it takes to lead meaningful change.
Today, we explore why successful transformation starts with people and processes, not technology alone, and what leaders need to do to ensure AI becomes a force multiplier rather than another failed initiative.
Leadership Evolution: Raising a Generation Born Into Technology
Your book makes a strong case that leadership capabilities are shaped long before formal roles and through lived experiences. We know that the current generation is living experience rather differently than all past generations. How do you see leaders and leadership changing in the next 15-20 years with the generations born into technology, automation, and AI?
I think leadership over the next 15–20 years will look very different because this generation is growing up in a completely different environment than any before them. Their lived experience is technology, automation, AI, constant connectivity, and also the pandemic.
One of the biggest shifts I see is that leadership will become less about having all the answers and more about judgment, discernment, and humanity. In a world where information is instantly available, and AI can analyze, summarize, and even recommend decisions, the value of a leader lies in the ability to ask better questions, navigate ambiguity, make ethical decisions, and help people make sense of complexity.
The human side of leadership becomes even more important as technology advances. Younger generations already expect authenticity, transparency, empathy, flexibility, and purpose from leaders. They are far less responsive to hierarchy for hierarchy's sake. And having lived through the pandemic during formative years, many of them experienced uncertainty, isolation, disruption, and a collapse of the assumption that institutions always know what they're doing. That experience shaped how they think about work, trust, mental health, relationships, and leadership itself.
The pandemic also fundamentally changed their relationship with work. Many watched their parents get laid off overnight, after decades of loyalty to an organization. As a result, I think younger generations are entering the workforce with a very different mindset: they do not assume companies will be loyal to them, so they do not define loyalty to a company the way previous generations often did. Work is important, but it is not their identity. They are much more likely to prioritize flexibility, well-being, relationships, meaning, and quality of life alongside, or even above, career advancement.
That shift will change leadership too. Future leaders may be less motivated by titles, status, or climbing traditional corporate ladders, and more motivated by impact, alignment with values, and creating environments where people can thrive as human beings, not just employees. I think we'll also see leadership become more distributed and collaborative because younger generations are used to networks, communities, and influence that comes from trust and credibility rather than position alone.
What I do wonder about is resilience and depth. Historically, leadership capability developed through long periods of challenge, patience, interpersonal struggle, and learning responsibility over time. We climbed each rung of the ladder, learning with each step. Technology increasingly removes friction and speeds everything up. AI may remove even more friction. The question is whether future generations will still get enough developmental experiences to build wisdom, endurance, and grounded judgment.
So I don't think the future belongs to leaders who are simply the most technologically sophisticated. I think it belongs to leaders who can combine technological fluency with deeply human capabilities, empathy, trust-building, ethical judgment, self-awareness, and the ability to help people navigate uncertainty together.
2. The Catcher’s Position: Observation in the Age of AI
You talk about strategy and the 'catcher's position'. The value of observation, taking in the whole picture, and having deep knowledge. In a world of instant gratification and AI doing magical heavy-lifting, do you think people can still find a 'catcher's position’. If yes, what would the new field look like?
The catcher's position came from my dad. He played seven years in Major League Baseball as a catcher, and he taught me how he saw the game from behind the plate. While everyone else faced the batter, he was the only one looking at the whole field. He noticed what the shortstop was doing, what the second baseman was setting up for, how momentum was shifting between innings. He observed human behaviors and predicted how they would act. He saw patterns nobody else saw because he was the only one positioned to see them. That's the position I've always tried to take into a transformation. Not rushing to the obvious problem. Stepping back first to see the whole field.
In the AI era, the catcher's position becomes more valuable, not less. AI is very good at processing what's directly in front of it. It is not good at telling you what's missing. It can't tell you the question you should have asked. It can't tell you which data is distorted by organizational dynamics you don't yet understand. It can't tell you that the anomaly it flagged is actually a workaround a team created three years ago, because the system never fit how the business truly operates.
The field is bigger now. Faster. More interconnected. More players, even. But the position is still the same. You are still looking for what's happening underneath the surface. Where are the incentives misaligned? Who is being rewarded for the wrong behavior? What is the AI being asked to optimize that nobody stopped to question?
The leaders who keep that vantage point will use AI wisely. The ones who don't risk handing over judgment to a tool that doesn't actually possess any.
3. Defining Moments: From Crisis to Capability at Scale
You've led technology transformations at Nordstrom, REI, and Lululemon, scaling them from $2B to over $10B while building global teams. Looking back, what were the defining moments that shaped your leadership philosophy? Also, what is the most misunderstood aspect of scaling technology organizations at that level?
Three days before I officially started at Lululemon, the website went down for twenty hours.
There were no alerts. No drop detection. No clear ownership. The data center hosting lululemon.com had been sold to IBM, and nobody at our end knew who was responsible anymore. I made the calls to track it down myself, before I had a team, before I had a badge. Jim Cramer mentioned it on CNBC. He said "Julie Averill better get to work." He wasn't wrong.
That outage revealed more than a failed tech stack. It revealed a company without ownership or accountability. And it taught me what became the spine of how I lead.
My philosophy comes down to four beliefs I learned the hard way.
Technology is rarely the hardest part. The work that determines whether a transformation succeeds is human. Decision rights, incentives, trust, and whether people are telling each other the truth. I have watched the same technology succeed in one organization and fail in another, and the difference was never the code.
Innovation at scale happens when psychological safety is already in place. You cannot ask people to take risks, surface bad news, or move fast if the cost of being wrong is humiliation or punishment. Growing Lululemon from $2 billion to $10 billion required a team that could move faster than I could direct them, and that only works when people know they will not be punished for being honest.
Influence matters more than authority. Leaders who succeed at scale do not get there by issuing directives. They get there by being credible enough that people want to follow them. Authority is finite. Influence compounds.
The test of all of it is what endures after you leave. If the work depends on you, you didn't build a transformation. You built a dependency.
Those beliefs were sharpened by other moments, too. A website outage at REI early in my career, and a board member who asked me what I learned, not what went wrong. A $250 million, three-month omnichannel result at Nordstrom changed how the company viewed itself. Building the India Tech Hub at Lululemon with gender balance as a strategy, reaching 46 percent women by 2023, and watching that diversity produce better outcomes than a homogeneous team ever would have.
4. Crisis as Catalyst: Culture as Strategic Asset
When most organizations work hard at averting a crisis, you've said, "Don't waste a crisis, " as if it were an opportunity. In fact, you've written candidly about crises, from outages to the pandemic. How do those high-pressure moments reveal the true state of an organization's culture and leadership? While evaluating ROI, should a CFO or CEO reposition culture as a strategic asset?
When the website goes down, when the supply chain collapses, when a pandemic empties the stores, you find out very quickly what your organization actually is. The fog clears and you see what's really important and how decisions get made when there's not enough time to wait for data. You see who shows up, who hides, and who trusts each other enough to take a risk under pressure. None of that is visible when things are running smoothly. It only shows up under pressure.
I've watched the same crisis play out in two different cultures, producing two completely different outcomes. In one, people pull together, surface bad news fast, make decisions with incomplete information, and recover. In the other, the same problem becomes a finger-pointing exercise, decisions stall, and the company loses weeks. The technology was identical. The result was not.
5. From Hiding to Belonging: Diversity as Strategic Advantage
You've led in environments where you didn't always feel you belonged. How did that shape your approach to diversity, inclusion, and building high-performing teams? While the corporate world has come a long way, do you believe there's still more work to be done? If yes, what advice would you give to future leaders?
I spent years hiding parts of who I was at work. As a gay woman in technology, I didn't always look or sound like the people in the room. I spent enormous energy managing how I was perceived, and that energy came out of my actual job. I've felt that cost deeply and personally.
Then I saw the other side of it. Once I stopped editing myself, stopped making myself smaller, stopped apologizing for myself, I saw what happened when I showed up fully as myself. My ideas came faster. I showed my passion for ideas and opinions and no longer felt that I needed to blend. There's a freedom that comes from that, and at that point you bring more value to the company. I started showing up as a whole person, and the team got better, fast. Not because I had given a speech. Because I had made it safer for everyone else to stop hiding, too.
There's still enormous work to do. The numbers in technology are still wrong. The pipelines are still leaky. The C-suite still doesn't reflect the customers or the workforce in most companies. And the current political environment has given many companies an excuse to retreat from commitments they made five years ago, which will cost them in ways they don't yet see.
For future leaders, the advice is simple:
Don't lead the team you have. Lead the team you wish you had, and then build that team.
Hire for the perspectives that aren't in the room.
Promote people who challenge you, not people who echo you.
Be honest about the fact that homogeneity is more comfortable, and the comfort is exactly the problem.
6. Love as a Leadership Practice: Unconventional Rigor
You've coined a phrase "love as a leadership practice", which is unconventional in the global corporate setting. What does that look like in practice for senior executives, especially when in practice it could just as easily be misunderstood?
Love as a leadership practice has nothing to do with sentimentality. It is not about being soft, being everyone's friend, or avoiding hard decisions. If anything, it demands more courage, not less.
It is the discipline of seeing the actual human being in front of you and making decisions that reflect their dignity, even when the decision itself is difficult.
The experience that shaped this most for me happened during the Russia-Ukraine war in 2022. I had team members from both countries. The tension was enormous. Nobody knew how to sit on a call together, let alone work together.
So we held a listening session. Hundreds of people joined. I asked one question: How are you doing?
And then I waited.
The silence was long.
Eventually, a Ukrainian engineer spoke. Then another. And at one point, a Russian engineer spoke up and said, "You are our brothers and sisters. We grew up beside you." His comment opened the door for tears, sharing fears, family, loss of homes. It changed the entire room and we were all impacted.
People who had every reason not to trust each other extended trust anyway. They listened differently. They worked differently. They had trust and empathy for each other. Humans next to humans.
I think the biggest misunderstanding is the belief that love and rigor are opposites. They are not. In my experience, the leaders who practice this well actually hold a higher standard.
They give honest feedback because they genuinely care whether someone succeeds. They address underperformance early because protecting people from the truth is not kindness. They show up for their teams in moments of crisis because leadership matters most when conditions are hardest.
Love, in practice, often looks like accountability paired with humanity.
And in an AI era, I think this matters even more. As more routine work becomes automated, the uniquely human parts of leadership become more important: judgment, courage, trust, empathy, and connection.
Technology may optimize work. But people still decide whether they feel safe enough to contribute fully, challenge ideas honestly, and stay through difficult moments.
The leaders who can create that kind of environment will hold organizations together. The ones who cannot will eventually lose their people to someone who can.
7. Why AI Transformations Fail: Technology vs. Operating Models
A central theme in your book is that "technology is the easy part." But many transformation efforts do still fail despite massive investments in AI and digital. Why is that, and what distinguishes leaders who use AI to truly rethink how they create value?
Technology used to be the hard part. It really isn't anymore. The talent exists. The capital exists. The platforms exist. What gets in the way now is usually the organization itself.
You can have real-time data and AI-generated insights, but if decisions still take three committees and a month of meetings, nothing has actually changed. If your data is fragmented, AI just makes bad decisions faster. If incentives are misaligned across teams, AI will scale those misalignments too.
That's not a technology problem. That's an operating model problem.
That's why so many AI and digital transformations fail. Companies think the technology itself creates transformation. It doesn't. It exposes and magnifies whatever was already there.
The leaders who use AI well approach it very differently. They don't treat it as a technology project. They treat it as a forcing function to rethink the business itself.
They're not asking, "Where can we deploy AI?"
They're asking, "What does this business need to become, and where can AI help accelerate that?"
The first question creates pilots. The second creates transformation.
The other thing strong leaders do is measure AI in business terms, not technology terms. They don't get overly impressed by deployment counts or sophisticated models. They look at whether the business actually improved.
Did inventory move faster?
Did conversion improve?
Did cycle times shrink?
Did customer retention improve?
Did decision-making get better?
If those things are not changing, the AI is not creating value, no matter how impressive the demo looks.
And honestly, maybe the hardest part is having the discipline to stop initiatives that aren't working.
Most companies struggle with that. Once a major AI initiative has executive sponsorship, the political cost of admitting it isn't delivering value becomes very high. So organizations accumulate zombie programs, expensive efforts that continue because nobody wants to challenge the narrative.
The company looks innovative. But it's not actually becoming more effective.
The leaders who create real transformation are the ones willing to separate signal from noise early, make hard calls quickly, and keep redirecting resources toward measurable business value instead of technological theater.
8. Measuring True Transformation: What Endures After You Leave
You've said real transformation is measured by what endures after you leave. How should leaders evaluate whether their transformation efforts are truly sustainable?
The real test of a transformation is simple: after you leave, does it keep going?
Two things should endure: the value to the business compounds, and the impact on people continues.
That's hard for leaders to hear because we often believe our involvement is what made the work successful. But if everything depends on you staying in the middle of it, the organization can only grow as far as you can carry it.
As I've left different companies throughout my career, what mattered most wasn't whether the systems or roadmaps remained intact. It was whether the people had become more capable without me there.
Could teams make decisions that once required escalation? Could they recover from problems they were never specifically trained for? Could they tell each other the truth when something wasn't working?
That's what lasts.
Not the architecture. Not the systems. Not even the strategy.
What lasts is whether the organization became stronger because the people inside it became stronger.
Because if the capability continues after you leave, the transformation was real. If it doesn't, then what you built may have looked impressive, but it was never designed to endure.
9. AI as an Amplifier: The Strategic Role of Organizational Foundation
You argue that AI amplifies existing leadership capability rather than fixing it. What does this mean for CEOs currently investing heavily in AI-first strategies? And, if there is one idea you want every CEO or board member to take away from Chief Impact Officer, what would it be?
If your organization is strong, AI will make it stronger. If your organization has gaps, AI will make those gaps bigger and faster. There is no middle outcome. AI is not neutral. It is an amplifier.
That means the most important AI investment a CEO can make is in the capabilities that will not be amplified well by AI. Clear decision rights. Honest performance management. A culture where bad news travels up quickly. A leadership bench that can think under pressure. These are the things that determine whether your AI investments compound or whether they create a faster version of the same problems you already had.
Most CEOs I talk to are skipping this step. They are buying the technology, hiring a Chief AI Officer, announcing the initiative, and then wondering why six months later they have impressive pilots and no business impact. The technology was never the issue. The organization was.
The one idea I would want every CEO or board member to take away is this. AI success is not measured in AI. It is measured in what changes outside of it. If your business metrics aren't moving, your AI isn't working, regardless of how sophisticated the technology looks on a slide. Hold the organization accountable to outcomes, not activity. That single discipline will separate the companies that win the next decade from the ones that get left behind with a very expensive collection of pilots.
10. Why Now? Lessons for Leaders at Every Stage
This book came at a point when you have already achieved what many would consider the pinnacle of a career. What drove you to tell this story now, and what would be your "before-you-read" advice to those who are still at the beginning, or middle of their careers?
I've been very fortunate to have been a part of several significant transformations. Through this, I have some battle scars, but also a lot of lessons. And I've learned that what really matters to me is creating impact – to the companies I work for, the people I work with, and increasingly to the broader world. The book is my journey, both personally and professionally, and it illustrates the principles that I have come to believe are critical for both transformation and impact.
And, as I left, I started getting calls - CIOs, CTOs, board members, founders. They were facing AI transformations, and they were stuck in patterns I recognized because I had been stuck in them myself, years earlier.
After enough of those conversations, I realized the lessons I'd learned weren't just mine. They were patterns. And in an era where every leader is being asked to navigate AI without a map, those patterns matter more, not less.
That's why now. AI is going to magnify whatever leadership is already in your organization. If we don't talk about what good leadership actually looks like, the technology will sprint ahead of the human capacity to use it well, and the cost of that gap is going to be enormous.
The before-you-read advice for someone earlier in their career is this. The capabilities that will matter most in the next twenty years are not the ones being taught in business school.
They are:
Seeing systems
Owning outcomes
Telling the truth under pressure
Developing other people
Staying authentic when there are easier paths
None of those come from a credential. They come from experience that forces you to develop them. So when you get a hard assignment, take it. When you get an honest piece of feedback, sit with it. When you make a mistake, don't manage the optics. Learn the lesson.
AI is not going to change that. If anything, it makes it more important.
The Bottom Line: Leadership in the AI Era
Julie Averill's insights cut through the noise that often surrounds digital transformation. The key takeaway is this: Technology has never been the hard part. Building the organizational culture, decision-making structures, and human capability to use technology wisely is the real work.
As AI continues to reshape how we work, the companies that will thrive are not the ones with the most sophisticated models. They'll be the ones led by leaders who understand that AI is an amplifier, and who have spent the time building strong organizational foundations that can be amplified.
For leaders ready to rethink transformation, Chief Impact Officer offers both practical frameworks and proven principles from a leader who has scaled organizations from billions to tens of billions in revenue while building teams that lasted long after she left.
About the author
Julie Averill is the EVP and Global Chief Information Officer at Lululemon, where she has led technology transformation that scaled the company from $2 billion to over $10 billion in revenue. Throughout her career at Nordstrom and REI, she built global teams and operating models designed to scale trust, culture, and business impact. Her new book, Chief Impact Officer (8080 Books, June 16, 2026), draws on lessons from some of the world's most recognized brands to offer a fresh perspective on what it takes to lead meaningful transformation.
