Key takeaways:
Continuous AI-driven modernization requires leaders to build organizations where people, expertise, and functions bridge differences, adapt together, and continuously evolve.
- Leadership must shift from “pathfinding” to “wayfinding,” embracing uncertainty, experimentation, and collective decision-making.
- Bridgers are essential: people who connect functions, translate perspectives, and integrate diverse expertise to turn complexity into collaboration.
- People and technology must evolve together, with organizations investing in T-shaped talent, employee involvement, and meaningful opportunities to learn and grow.
The other day, I was talking with the leaders of a successful global technology company known for its agility. Recently, they’d hit a wall. They were still capable of generating innovative solutions, but they had begun to struggle with scaling them.
I have seen this pattern start to emerge in many of the organizations I work with. In many ways, AI has muddied the waters of transformation – blurring the lines between what was previously thought of as ‘departmental’ change into something much more ubiquitous. Modernization, in this particular example, used to be confined to IT’s efforts to update outdated technology systems for cost or risk reasons. Now it’s as much about building the flexible foundations to power growth and enable rapid shifts.
The company in question realized that driving innovation without intentionally evolving the means to scale was now the limiting factor in their continued growth. Not just a technology problem; a whole business one. And the stakes here don’t stop at the financial.
The people factor in modernization
According to research, 30% of leaders say delaying modernization has led to challenges in retaining talent. Modernization, in other words, is as much a people issue as a technology one. Indeed, this statistic is unsurprising when you consider that as the role of technology evolves, modernization efforts are now a clear signal of how seriously enterprises are taking their AI ambition – and people are attuned to the implications for their own roles.
Talent is more likely to stay when they see meaning in the work and understand their role in it. Without a compelling story about how AI will create value – and why the disruption and risk are worth it – the question becomes not whether employees can move forward with an organization, but whether they want to.
Equally, system change requires redesign at all levels in order to keep people engaged and involved in that change. In fact, a failure to undertake this redesign is the loudest signal that the organization is not evolving to meet the demands of today’s operating environment. As with any change, if people do not see themselves in it, the result is disengagement. With continuous change as a business imperative, ‘disruption’ is now a constant operating condition.
And this has revealed something leaders may not have fully realized, or accepted, before: their senior team might be playing on a team, but they're not yet playing as a team. In a more predictable environment, a somewhat top-down model may have worked – for many organizations, even worked well – for years. However, knowing that remaining competitive in today’s environment now demands constant changes to update technology, processes and ways of working rather than ‘big bang’ transformation that moves from one fixed state to another, that model begins to break down.
When the pace of change is high and the answers are not obvious, senior leaders cannot simply divide up the work and operate independently within their own domains. They need one another’s diverse range of perspectives and expertise – as well as those of their employees – to make sense of what is happening, determine what to do next and to execute at the scale required. Advancements in AI have sharpened the imperative further – with buying committees expanding, and leaders recognizing that change programs can no longer be confined to or run by single departments.
Modernization calls for wayfinders, not just pathfinders
For years, many leadership teams approached ‘transformation’ as a pathfinding exercise: set a destination, determine the route and mobilize the organization to follow it.
Continuous modernization doesn’t work that way. With AI in particular, leaders are being asked to make consequential choices while the technology and its use cases are still evolving. They need to preserve optionality and learn quickly, while also being prepared to change course as new evidence emerges.
I think of this as wayfinding.
This changes what organizations need from their senior teams. Leadership is now less about supplying the answer and more about creating the conditions in which people across the organization can find the answer together.
That need is reflected in how decisions are now distributed across the enterprise. Research shows that modernization budgets and decisions are split across CIOs (58%), CTOs (51%), CFOs (39%) and boards (32%), so success increasingly demands cross-functional alignment as much as technical execution. The same is true of broad AI investment. When no one function has the whole picture, the organization needs to bring together different points of view to make an informed decision.
Inevitably, that creates conflict. And yet, however tempting it may be, leaders should not try to eliminate that conflict. In fact, innovative solutions rarely emerge without what I call creative abrasion: a marketplace of ideas in which people work through disagreement to create better answers.
The problem is that many organizations have not built that muscle. When disagreement arises, leaders may default to top-down decisions, avoid making a decision altogether or, far more commonly, go along to get along, sidestepping the kind of constructive debate that can sharpen their thinking. None of those approaches works particularly well in an environment defined by uncertainty. The goal, instead, is to learn how to make disagreement productive.
And that is where the ability to bridge across differences becomes essential.
Leadership is now less about supplying the answer and more about creating the conditions in which people across the organization can find the answer together.
The importance of ‘bridgers’
In any given project, an engineer may understand the IT architecture but not the customer. A product leader may understand the customer but not the technology. An external startup may bring a promising new capability but have little experience with the operating constraints of a large enterprise.
The people who are able to connect those worlds are what I call bridgers.
Bridgers do three kinds of work. They curate, by identifying the people and partners needed to solve a problem. They translate, by helping people from different contexts develop enough common language and understanding to work together. And they integrate, by aligning those parties around a shared intention and operating model.
What this requires is contextual intelligence. Effective bridgers understand that when people disagree or appear resistant, the problem is not necessarily interpersonal; often, people are responding rationally to the priorities and constraints of their own context. A bridger’s work is to understand those contexts before trying to reconcile them.
My colleagues and I saw this clearly in research for our book, Genius at Scale. For example, when Delta was developing its biometric capabilities, Nicole Jones, who led the airline’s innovation lab, had to bring together Delta’s IT organization, an external startup and government stakeholders. Each came to the work with legitimate but very different – and often conflicting – priorities and concerns. Delta’s IT team was responsible for systemic reliability at enormous scale. Government stakeholders were concerned with safety and compliance. The startup, meanwhile, knew every additional step cost scarce capital and shortened its runway.
Jones’s job as a bridger was not to make those differences – and the conflict they created – disappear. It was to understand why they existed, help each party see the constraints facing the others and create enough shared understanding and problem-solving to address key barriers to cocreation.
This is the kind of work continuous modernization requires: not simply assembling the right expert, but building relationships grounded in mutual trust, commitment and influence – and translating those relationships into a shared intent and operating model that can accommodate different expectations, working styles and structures.
Yet many organizations I speak with have too few bridgers – or lack them almost entirely. So the challenge is not only to identify the bridgers already in the organization, but to develop and multiply the bridger leadership within the organization.
Beware of bridger burnout
In many organizations, leaders may already know who their bridgers are, even if they do not use that language. It’s the person who, when a complicated project gets underway, prompts someone to say, “You need this person on the project. They know how to get things done.”
But for bridgers themselves, effectiveness creates its own challenge: everyone wants them. They become overloaded precisely because they are good at what they do. And when a project succeeds, their contribution is often difficult to see – if not altogether overshadowed. The technology team might naturally take credit for delivery and the business team for the outcome. The person who spent months aligning the groups, working through differences and preventing problems is less visible.
This is an inherently unsustainable scenario for organizations committed to continuous modernization. It is not only an operating-model problem; it can become a retention problem. The people most capable of navigating complexity are often asked to carry more of it while receiving less visible credit for doing so. Bridging cannot remain the heroic work of a few unusually capable people. Organizations need to identify the bridgers already doing this work and, just as important, build broader organizational capacity to bridge.
Build – and seek – more T-shaped talent
One clue lies in the career paths of people we’ve seen who bridge well. Many have what we call zigzag careers. They may have worked across functions, moved between technical and commercial roles or taken assignments that forced them to operate in unfamiliar contexts. In other words, they have repeated practice seeing the world through someone else’s constraints. Bridging is, after all, learned by doing.
Organizations should therefore think deliberately about developing and recruiting more T-shaped talent: people with real depth in one area but also enough breadth across others to collaborate effectively.
This is particularly important for technology talent. Specialization remains, of course, essential. But as nearly every company becomes a technology company, technical leaders increasingly need to be able to work across product, customers, operations and business models. Rotational assignments, cross-functional projects and exposure to different parts of the enterprise are practical ways to build the contextual intelligence that modernization requires.
These opportunities can also give high-potential talent the chance to learn, grow and contribute beyond the boundaries of a single role. Critically, however, these opportunities also need to be a safe place to learn. Stretch assignments, by definition, ask people to operate beyond what they already do well. Organizations therefore need to make room for missteps, learning curves and the coaching required to help people grow through them.
Organizations can also make the work of bridging easier to understand and execute by giving teams a simple framework. Before starting an initiative, ask stakeholders to articulate the problem and why solving it matters. Then map the work around a few questions: Whose expertise is required? Who will be impacted? Where do the interdependencies sit? What unintended consequences could emerge? The ultimate goal is to create that shared story about why the work matters, who needs to be a part of it and how they will work together.
With AI, bridging starts at the top
Building bridging capacity across the organization matters, but leaders also have to model it themselves. That is especially true when modernization touches employees’ own roles and their sense of what comes next.
AI puts this leadership challenge in sharp relief. Leaders have to bridge two kinds of uncertainty at once: what the technology can do and what it will mean for employees, even as the organization itself is still finding its way.
The organizations making the most progress with AI are often those where the CEO and C-suite are experimenting and familiarizing themselves with the technology firsthand. Employees need to see that senior leaders are in the game: using the tools, encountering their limitations and being candid about what they do and do not know.
That visible experimentation – and vulnerability – builds credibility. It also helps leaders empathize with the uncertainty employees are experiencing.
Education around AI should reflect the same principle. Much AI education is still designed for individuals. But enterprise value will come from teams learning how to change work across functions.
The objective, after all, is not simply to teach people how to use a tool, but to develop the judgment to use it well. Train intact work groups together around real use cases so they can learn not only what the technology can do, but how they will use it together. That gives employees a role in shaping the change, rather than simply absorbing it.
Give employees a stake in what comes next
This is where the talent dimension of change becomes especially important. If employees experience this as something done to them – or if the organization is slow to equip them for new ways of working – they may reasonably question what place they have in its future. The fact that nearly one-third of leaders connect modernization delays with retention challenges should sharpen the point: the people strategy cannot trail the technology strategy.
If any change is justified first in terms of efficiency, without a broader account of what the organization is trying to achieve for customers or to fulfill the organization’s purpose, employees will understandably hear a conversation about cost and replacement. Efficiency matters, but it is not the only source of value.
No company can sustain continuous modernization as a technology-only initiative. The human system and the technology system have to evolve together. This has long been true of organizational change – AI just makes the consequences of forgetting this truth much sharper.
What leadership teams need to be asking themselves is then this: What can we do in the organization’s future that we could not do before? When employees can see meaning in change — see how they will learn, contribute and grow as the organization changes – continuous modernization can become not only a technology strategy but a talent strategy, and a reason to stay and help build what comes next.
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