Hello, everybody, and the warm welcome to what I hope will be a different kind of session.For the next twenty minutes, we'll explore what it really means to leave change and some key aspects that often get overlooked in an era obsessed with speed, AI, and efficiency. Also, in our context, nowadays, we're not lacking information, but more humanity, more authenticity, more rawness, and perhaps a little bit of humor. So that's my goal with the presentation today, not to offer a playbook for change, but new insights, a great story that you can take back to your teams, a spark of new energy, and the space to create connections. So look around and feel free to to connect with each other. Lastly, in this sea of perfectly polished AI coated content, I've also come to appreciate imperfection much more lately in a slide, in an article on LinkedIn.

Anything that gives away that we're humans and bridges the distance to the author. So I do hope that in my presentation today, you'll find a little bit of imperfection as well. Okay. So here's what we'll explore today. Why most transformations fail? How speed without depth erodes strategy, why trust and culture are the real foundations of lasting change. To briefly introduce myself, I've spent over a decade leading digital products and technology programs, managing data and engineering teams, products and processes. And currently, besides my leadership role in the data space, I'm an executive and leadership coach and founder at Habit of Growth, where I also blog about change, AI, inclusion, career transitions in the AI era. So I welcome you to, join me. Now change is the only constant.

In my fourteen years of experience as leader in the corporate world, I've helped shape and led transformations, AI adoptions, digital, transformations, post merger integrations. And while each one of them looks different on the surface, the pattern is always the same. It starts with the trigger. So the moment the status quo starts, stops delivering, revenues are declining, opportunities are missed, or leadership simply loses confidence that the current trajectory is the right one, and they hope change will fix it. So leadership decides. We need new direction, new strategy, new structure. We'll pivot. We'll streamline. We'll relaunch. Then comes the announcement. The town hall, our favorite part, which is like a corporate version of the Super Bowl where organizations ask people to absorb the short term discomfort for the promise of a stronger, more sustainable future. Then change peaks everywhere, like team consolidations, new approval processes, cost inventories, new ways of working, AI adoption, sometimes, unfortunately, layoffs. But here's what research shows.

Fewer than one third of reworks actually succeed. And it is the same for post merger integrations. And even more telling, Bain and Company found that 88% of leaders are confident their rework will deliver, and only 36% of employees agree. This perception gap between what leadership believes and what people experience is not the communication problem. It's a prediction problem, and it becomes a self fulfilling prophecy. Because when most of your company doesn't believe the destination is real, they stop walking alongside you before you even notice. So the question I want to explore today is this, Where are the real points of disconnect between leadership and employees? What are the most underrated, least obvious signs and triggers of rework failure? Because they're not what you think. So let's dive right into it.

Here's a pattern I've seen repeatedly and it usually comes from good intentions. New leaders arrive under pressure from the board to move fast, to prove impact, to make visible changes, but then something happens. They put forward an idea and their team raises concerns. Right? So flag risk pushes back. And instead of treating this feedback as signal and context, they read it automatically as resistance to change, defensiveness, legacy thinking, people that who just don't get it. And sometimes that's true, and I could have another conference on that topic. But most often, that pushback is institutional wisdom trying to surface. It's the people closest to the work trying to tell you something important. They see risk you don't see.

And that's a good thing because the successor successful transformation is not imposed top down. It's co authored with the people closest to the work. Right? And I saw this firsthand many years back. New leadership came in, good intentions, and they wanted to introduce scrum. The problem well, we had already been using scrum in our department, and it was working for years. What we lacked wasn't a new delivery framework, but strategic alignment on priorities. So instead of asking what was working and what wasn't, what to keep and what to evolve, teams were handed a document. Here's our interpretation of scrum. Here are the ceremonies and how they should be called. Please comply. Questions from the teams were interpreted as resistance and met with the same response.

You need to adapt to the new mentality or you'll be left behind. The phrase we used to became unacceptable, unacceptable language. The message was clear. The past is no longer no longer matters. This is a new chapter. So in retrospective, that was a failed rework. But not because we changed the names of our meetings, but because this was a symptom of something deeper, misdiagnosing the real issue. Like treating a broken bone with vitamins. The strategic misalignment wasn't solved, but it became more obvious because the operational system that quietly compensated for it was destabilized. And here's what I took from that. Even when new leaders are good at identifying what isn't working, far fewer spend enough time to understand what already is. And by not doing that, you risk ripping out something you didn't know you have.

The social capital, the gluers, the stabilizers that quietly hold performance together, the things that differentiate you as a culture, as a company, as a brand. And this leads me to a key point. Treating turnover as a natural byproduct of reworks or of AI adoption can backfire, especially when the underlying assumption is that anyone not aligned can simply live. Yes. Sometimes, attrition is inevitable. You will lose people who cannot adapt, and in some cases, that's expected, and it's a good thing. But you'll also leave lose something far more valuable. The people with deep context, with hard won judgment, with system understanding. And this is the so called regrettable attrition. These are often the people who challenge weak decisions, surface what others miss, because they've made many mistakes and have learned the hard way what works and what doesn't work.

And, yes, apparently, pushback might sound like friction, but when used well, that's what sharpens strategy. Because when descent leaves the room, differentiation leaves with it. And this is linked to one of the biggest blind spots I see today. We're living in an age of speed and short short term is, and organizations are moving fast, embracing a size the moment approach and standardizing execution. Fractional leadership is rising. Teams are formed and dismantled quickly, and speed is prioritized above almost everything else. We want faster go to market, faster adoption, faster execution, and so on. But in this process, when organizations are importing the same playbooks, they're also becoming more interchangeable, trading strategic fit for efficiency. Sure. They're no longer reinventing the wheel, but they push on adopting it even when the terrain clearly calls for a camel.

And organizations that move fast without taking time to understand what actually works without listening to the people on the ground and without strategic clarity, they lose edge, they lose people, and in the end they lose customers. And that's even more true today, in our open innovation era where speed alone is no longer a differentiator because everybody is using the same tools, has access to faster tools. And differentiation comes from all of those nuances that make an organization unique. The strong culture, the social fabric, the foundation clarity, the deep customer understanding, the discipline of learning through failure. That's where strategic edge is built, and usually that takes time. It requires incubation, context, letting ideas compound long enough to mature into something distinctive. And that's how, differentiation strategy is born, not from reacting faster but from seeing clearer. Right? So the goal is not just to move fast to catch up with the market, it's to move fast in the right direction.

And here I wrote a story, spontaneously in my agenda in one meeting, a few years back. It's an imaginary conversation between two leaders. So one is asking, how do you define strategy? And the other is saying, well, we follow the Hansel and Gretel method. Oh, okay. That's interesting. How's that? Well, we leave behind a breadcrumb trail while sprinting ahead with execution. And when we hit the dead end or when we're asked about strategy, we go back along the trail and try to piece it together. Okay. And how is that working out? Well, we're lost in the woods. So that's a funny reminder, but a serious one after all. Right? That speed and execution without strategic clarity won't scale. It will just get you lost, faster.

Now if there's one thing to preserve through any change, be it the transition, a rework, an AI adoption is trust. And Harvard professor, Francis Frey and Anne Morris, co trust the essential leadership capital. Because once trust erodes, no amount of strategy or structure can compensate. And here's the twist. If we were to have a movie called how to lose trust in ten days, most of the companies could write the script. So what erodes trust? And I'll stick to two main things. One is delaying hard decisions. Keeping ineffective leaders in place after a rework or a large scale transformation doesn't preserve stability. It accelerates talent loss. So hear me out. And to be clear, we are talking here about leaders well known by the system who were tolerated with no consequence for the negative impact on teams, on psychological safety, the ones who employ bullying, create a climate of fear, or take credit, micromanage, and and all of that.

And these are the people you lose first. Not the weakest, the strongest, the one who stay through this function because they believe change might finally improve things. They tolerated the chaos. They carried more than they should have and waited for the reorg to signal a reset. But then change happened at the top and nothing changed for them. These are the stabilizer, the gluers, the top performers. And this is what I call the strangler fig pattern. And this is going to be an interesting one. So the strangler fig pattern is a concept coined by Martin Fowler wrongly applied to people. It works in software architecture, right, where it's been originally coined, where it says that you build new systems around the old, gradually replacing legacy components until the old can be retired.

But with people, that logic fails because you cannot slowly build around toxic leadership and expect trust to survive. You need to take resolute action. But the biggest trust detractor nowadays is related to AI adoption and how AI is framed. Because until recently, building a strong culture meant things like belonging, empathy, employee well-being, stability, and so on. While those still matter, in periods of rapid change, trust is increasingly built through radical transparency about trade offs. Organizations are what? Value generating systems sustained by profit. Right? And, yes, the relationship between company and employee is transactional. And that's not cynical. Right? It's structural. Let's let's acknowledge that. So instead of vague narratives like we're freeing up time for more strategic work, companies need to be far more explicit about the what's in it for me factor.

When let's say rallying employees around an AI adoption program, stop using arguments such as loyalty, client satisfaction, or an uncertain promotion. But lead with arguments such as career leverage and the transferable skills that employees build in the process, which could become their next interview anchor. Because in a world of high job mobility, people optimize for optionality. And beyond this, sometimes the most valuable thing a company can do for you is help you connect with each other. As a parenthesis, do you know what's the most common regret I hear in coaching sessions from people who were laid off after a rework? That I was focused on my on doing my job, and I never took time to meet people outside my immediate circle from other departments and to build a network.

So what companies can offer is simple. A forum where people can connect, being mentorship, cross team. So they get to network before they actually need a network. Because nowadays, over 70% of new jobs are filled through networking. Now what about scaling AI? We cannot have a speech about, change without touching upon AI and, of course, about the AI adoption challenges, which are not technical, but they relate to all of the things that we've discussed before. Let me explain. So as McKinsey has pointed out, many organizations are reacting to the AI wave instead of leading it. So, basically, they're implementing AI in pockets gradually, tactically, but without rethinking the broader system required to scale it. So as we discussed speed without depth and direction. What does it mean?

That AI often lives in silos, marketing, product, data science, data analytics, each function running its own experiments, own tools, own priorities, own success metrics, often without a shared operating model, without clear governance, and most importantly, without a single c level owner accountable for enterprise wide adoption.

And the result is predictable. Fragmented infrastructure mirroring, fragmented ownership, of course, as per Conway's law, duplicated effort, inconsistent standards, and local optimizations instead of enterprise value. Now to oversimplify, let's say that marketing uses cloud, product uses Copilot, data science uses OpenAI. The tools are not the problem. The strategic fragmentation is. Also, scaling AI is not just about deploying more models, better accuracy. No. It's about creating the conditions for adoption, and adoption is human. It requires trust, which we discussed about, and building the organizational coherence to make this model these models together matter. Lastly, one of the most persistent misconceptions, at executive level is assuming culture will form on its own. That it will emerge naturally after a rework or after AI adoption, and culture will catch up. It won't. Culture doesn't self correct.

It does not quietly fall into place once the structure is redrawn or a new model is deployed. As Peter Drucker famously said, only three things happen naturally in an organization, friction, confusion, and underperformance. Everything else takes intentional design. And culture is not declared. It's it's built. It's built through repeated behavior. It's reinforced over time. Like, imagine you are trying to modernize a legacy app that is rigid, it's slow, has a lot of technical depth, a lot of patches that make it makes it hard to be maintained, and you eventually decide to replace it. But unless you change the development practices, you introduce observability, automated testing, tighter feedback feedback loop loops. So unless you change the culture around it, you will end up in the same place.

And, a great book that I recommend here about changing culture is Turn This Ship Around by David Market. So, look it up. Now here's what I wanna leave you with. Organizations keep restructuring. New org charts, new roles, new processes, most of them are solving the wrong problem because structure can enable change, but behavior determines if it actually matters. Sure. You can redraw the reporting lines. You can announce new priorities. You can implement the best systems in the world. But if you haven't built trust, if you're not listening to pushback, if you're moving fast without depth and direction, if you're losing your sharpest people, the structure won't save you. Because transformation doesn't happen in the org chart, it happens in the room where people decide whether to believe you, whether to stay, whether to bring their best self to work, or just show up.

And last but not least, the problem with reworks after reworks is that expectations don't increase. They collapse. Have you ever heard this argument? Well, yes. But leadership is new. You can't expect results overnight. Or, yes, but we just introduced the process. You cannot expect results overnight. And that's true. It takes months to deliver. But when you start another reorg a year later, nothing ever matures. So despite the ambitious targets, you never let them simmer long enough to become real. But at the same time, the paradox of transformation is that a cycle of failed reworks is often broken by another rework. And you will only know retrospectively which one actually paid off. So keep being optimistic. And if you're a leader, I wish you wisdom and strength to turn this ship around. Thank you, everybody, and have a wonderful day, and stay in touch on LinkedIn.