Organizations have spent the past several years racing to deploy AI. Understandably, much of this activity has focused on technology, governance, and new operating models. Yet a crucial ingredient of successful AI transformation has received far less attention: trust.
If leaders truly want to transform their organizations with tech and AI, they will need their people to make it happen. Unlike many previous technology transformations, AI can’t be deployed solely from the top down. Real value emerges as employees experiment with new ways of working, identify high-value use cases, redesign workflows, and continually refine how AI is applied in their daily work.
AI transformation is changing the relationship between employees and their organizations. Leaders are asking people to work in fundamentally different ways, and employees are deciding whether they trust them enough to make that journey. If people are unwilling to engage in this shift, even the most robust AI strategy will struggle to translate technological potential into lasting enterprise value.
Fortunately, organizations aren’t starting from zero. At a time when trust in many institutions has eroded, employers remain among the most trusted institutions in people’s lives. According to the 2026 Edelman Trust Barometer, 78 percent of employees globally trust their employer “to do what is right.”1 That gives leaders something invaluable to build on as they navigate one of the biggest workplace transitions in decades.
A recent McKinsey survey on AI preparedness finds that trust in the organization and its leaders is one of the strongest predictors of readiness to use AI.2 Organizations with higher levels of trust also report capturing greater enterprise value from their AI initiatives. Trust is therefore not simply an outcome of a successful transformation, but one of the conditions that makes transformation both possible and effective.
Trust also helps organizations learn faster. Employees are more likely to experiment with AI, share what’s working and what isn’t, and openly discuss mistakes when they believe leaders are genuinely interested in learning rather than judging or monitoring them. Those conversations create faster feedback loops, helping organizations adapt more quickly as AI capabilities and ways of working continue to evolve.
In this article, we discuss how senior leaders can earn trust by offering a clear plan, getting out into the organization, investing in employees, and equipping a broader range of leaders to help make the transformation successful.
Build trust—don’t just reduce anxiety
McKinsey has written extensively about building trustworthy AI, including responsible deployment, governance, transparency, and human oversight.3 Those technical foundations become even more important as organizations adopt increasingly autonomous AI agents.
But even the best technical safeguards don’t answer all the questions employees are asking themselves: What does this mean for me? Will I have a job? Will my boss support me? Can I trust my employer to navigate these changes responsibly? Employees are also thinking about issues such as privacy, fairness, bias, and how AI will be used across the organization.
Technical safeguards are essential, but leaders also must earn employees’ confidence that AI will be introduced thoughtfully, used responsibly, and that employees will be supported as their work evolves. When people trust that AI is being introduced to expand their capabilities rather than simply replace their work, they are more willing to engage with it and develop new ways of working.
However, as the McKinsey survey on AI readiness shows, roughly one in five respondents across every level of the organization report feeling anxious about AI-related changes at work. While that figure may sound lower than some might expect, anxiety is only one manifestation of uncertainty. Many employees who don’t describe themselves as anxious are still skeptical, hesitant, or unsure about how AI will affect their work.
For middle managers, who play a pivotal role in translating strategy into day-to-day execution, the percentage of individuals reporting anxiety rises to one in four (Exhibit 1).
That distinction matters because middle managers play a critical role in shaping how employees experience the transformation. If they don’t feel confident in the direction, don’t trust the organization’s approach, or don’t feel equipped to lead through the change themselves, they’re unlikely to build confidence in the teams they lead.
The survey also shows that respondents who report low trust in their organizations’ support during AI transformation are 1.5 times more likely than those reporting high levels of trust to feel anxious about workplace changes related to AI (Exhibit 2).
These findings suggest that leaders may be trying to solve the wrong problem. In reality, they can’t eliminate uncertainty during an AI transformation of this scale, nor should they try. The objective is not to remove every source of anxiety but to build enough trust that people are willing to move forward despite their concerns.
Faced with visible anxiety, leaders often respond with reassurance. They emphasize that AI is intended to improve productivity, not eliminate jobs, or they project confidence and certainty about how the future will unfold. While messages such as “this isn’t for cost-cutting purposes, and no jobs will be lost” may temporarily ease anxiety, they do not necessarily build trust.
Senior leaders who emphasize only reassuring messages run three significant risks: First, such reassurances will likely ring hollow. The world is changing too quickly for leaders to know exactly how everything will play out. Second, these messages risk destroying trust as soon as an action is taken that appears to contradict what was said. The first time AI displaces jobs, the leaders who implied that wouldn’t happen will lose employees’ trust.
Finally, when leaders try to minimize concerns, they miss an opportunity to connect authentically, acknowledge the legitimacy of people’s feelings, and show helpful compassion and support, which, over the long run, can be much more helpful and build trust.
At this point, it is a given that AI will change jobs, roles, organizational structures, and career paths. Some of those changes will create exciting new opportunities, while others will be difficult and may create a sense of real or perceived loss. Employees know this. Instead of offering false assurances to address the immediate anxiety, leaders can take four steps to build long-term trust with their employees.
Four actions that create trust
Trust isn’t built through a single communication, town hall, or announcement. It’s earned through a series of deliberate leadership actions that demonstrate clarity, commitment, and consistency over time. Together, these actions help employees feel confident that AI transformation is being led thoughtfully and with their long-term success in mind.
Offer a clear plan (it doesn’t have to be perfect)
Employees don’t expect leaders to have all the answers. They do expect leaders to have a clear plan and to communicate honestly about what they know, what they don’t know, and how decisions will be made. Trust starts with being clear about where AI will create value, how work will change, what success looks like, and what the guardrails are that will guide the transformation.
A clear plan builds trust only if leaders communicate it frequently and candidly. During periods of significant change, employees look for information that helps them understand what is happening, why decisions are being made, and what those decisions mean for them. When information is incomplete or inconsistent, people often fill the gaps with their own assumptions, which can amplify uncertainty and anxiety. Getting ahead of that means acknowledging setbacks, explaining how the organization is adapting, and being candid when answers are still emerging. Far from signaling weakness, this kind of candor builds confidence in the organization’s ability to learn and adapt as AI capabilities and business needs evolve.
Leaders often feel pressure to project certainty, particularly when employees are anxious. But reassuring messages and confident predictions can create unrealistic expectations, ultimately eroding credibility. Employees are generally willing to accept uncertainty when leaders are honest about what they know, forthright about what they don’t, and committed to keeping people informed as the organization learns.
Leading organizations make learning part of the story by sharing what early pilots have taught them, how employee feedback is shaping future decisions, and how their approach continues to evolve. In an environment where AI capabilities and business priorities are changing rapidly, the goal isn’t to produce a perfect plan. It’s to give employees confidence that the organization has a clear direction and will continue communicating as that direction evolves.
Get out into the organization
One lesson that consistently emerges from employee listening is that senior leaders often have an incomplete picture of how AI transformation is unfolding. Employees don’t experience AI the same way. Some are energized by new opportunities while others are uncertain about changing roles, evolving expectations, or what AI means for their future. By the time concerns, workarounds, and unanswered questions reach the executive team, they’ve often been filtered, softened, or lost altogether.
Leaders who want to understand how the transformation is really unfolding need to see it firsthand. That starts with talking to people about what they are experiencing. In addition to their feelings of uncertainty, many employees have specific hopes and frustrations that leaders need to understand and address.
Leaders can visit teams, sit in on meetings, and do ride-alongs. Spending time with middle managers is particularly valuable because they often have the clearest view of both the organization’s strategy and employees’ day-to-day experiences. Nuanced insights emerge from curious and thoughtful dialogue, more so than from dashboards and pulse surveys.
All change, positive or negative, can involve letting go of familiar routines, expertise, and ways of working, which inevitably affects how employees perceive and experience the transition. Leaders who understand those experiences firsthand are better equipped to adapt their approach, respond to concerns early, and build trust throughout the transformation.
Invest in people
Because employees understand that AI will change work, they don’t expect leaders to promise that every role will stay the same. They do, however, want leaders to demonstrate a genuine commitment to helping people succeed through the transition. For leaders, it’s critical to remember that trust is built less by what they say than by the investments employees see them making in their future.
Demonstrating that commitment begins with recognizing that AI transformation isn’t a one-time change event. As AI capabilities evolve, roles, workflows, and expectations will continue to change. Organizations can’t just announce a new operating model. People need agile change management that helps them navigate that transition over time. This approach acknowledges the human process of adapting to new roles, new ways of working, and new professional identities.
People also need sustained capability building. One-time AI courses aren’t enough; employees need opportunities to experiment, learn in the flow of work, receive coaching from managers and peers, and build role-specific skills as work evolves.
We have consistently found that different employees need different kinds of support, which should be integrated into a multifaceted capability-building strategy. For example, someone returning from parental leave may need help catching up on evolving AI tools and workflow changes. Someone whose role is shifting may need coaching, reskilling, or the opportunity to try something new. Early-tenured digital-native employees may need to build their expertise to better catch AI-generated errors or inconsistencies, while deep experts may need more opportunities to experiment with AI to build their comfort levels.
Leading organizations increasingly view AI upskilling as part of the transformation itself rather than as a separate learning initiative. They establish a shared level of AI literacy across the workforce so employees understand both the opportunities and limitations of the technology. From there, capability building becomes increasingly role-specific, helping managers, frontline employees, and technical specialists understand how AI changes the decisions they make and the work they perform.
The question leaders should keep asking is straightforward: What support do our people need to succeed in the organization we’re trying to build?
Investment also means creating visible pathways to what’s next. As AI reshapes work, employees want to know there is a future for them inside the organization. That may include job rotations, apprenticeships, stretch assignments, targeted reskilling, or structured pathways into emerging roles. Organizations that treat workforce transitions as a continuing organizational capability, rather than as a one-time HR exercise, reinforce an important message: While jobs may change, the organization’s commitment to its people endures.
Even with thoughtful workforce planning and reskilling, some transitions will be difficult. Employees pay close attention to how colleagues are treated when roles are eliminated or significantly changed. Fair severance, career coaching, outplacement support, and time to navigate the transition don’t just affect those who leave. They shape the trust of everyone who stays.
Some organizations are creating dedicated talent transition programs or internal talent marketplaces. When AI significantly affects an employee’s role, the goal is to provide a structured bridge to the next opportunity. Employees may be assessed to identify transferable skills, assigned to temporary projects where they can continue contributing while building new capabilities, connected with targeted reskilling opportunities, and supported in finding a permanent role elsewhere in the organization.
For instance, a global consumer products company initiated a program that keeps employees on payroll and benefits between assignments rather than severing ties the moment a role changes. The program has grown to include employees who use it to reskill after a job loss, including in AI, while remaining connected to the company and its network.
Even with thoughtful workforce planning, reskilling, and redeployment, some roles may ultimately be eliminated. How organizations manage these moments can have a lasting impact on trust. Providing fair severance packages, career coaching, outplacement services, time to conduct a thoughtful job search, and other transition support demonstrates that the organization’s commitment to its people does not end when a role does. Organizations that approach workforce transitions with transparency, fairness, and respect reinforce a culture of trust that extends well beyond the individuals who are directly affected.
Equip a range of leaders to build trust
During an AI transformation, trust must become an enterprise-wide capability, reinforced consistently by leaders across the organization. Credibility is crucial, reflecting the widely held belief that leaders generally try to do the right thing, act in line with their words and values, and prioritize employees’ needs and concerns.
If trust building is concentrated in only a handful of leaders, it won’t scale. Employees experience change through interactions with executives articulating the vision, functional leaders setting priorities, managers guiding day-to-day work, and teammates with whom they collaborate. Relational trust is nurtured by individual leaders when they are open, authentic, courageous, candid, transparent, and vulnerable.
Organizations shouldn’t assume every senior leader already knows how to lead this way, since many may not yet know what “good” looks like. Leaders who want to spend more time listening to employees may be constrained by competing priorities or a lack of experience with this kind of transformation. These capabilities can be developed, but it takes intentionality.
That requires equipping leaders with more than communication decks. Some organizations create leader discussion guides, hold AI office hours, offer peer coaching sessions, and facilitate “real plays” so leaders can practice difficult conversations before having them with their teams. They build time for these activities into the transformation itself, rather than expecting leaders to fit them in around everything else. They also create regular feedback loops that help leaders understand how they’re doing and continue developing this capability over time.
Employees don’t experience AI transformation through the CEO. Their day-to-day interactions with leaders at all levels and across functions shape how they perceive it. Organizations that intentionally equip leaders to build trust create a multiplier effect that no enterprise-wide communication campaign can achieve.
Trust develops through hundreds of decisions that, over time, demonstrate that leaders mean what they say. In an AI transformation, where uncertainty is high and employees are looking for signals about the future, organizations that build a clear plan, stay connected to employees, invest in people, and equip leaders to build trust create a strong foundation for navigating continual change.
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