Somewhere along the way, the conversation about AI shifted into a conversation about fear. These concerns mostly center around career security, resistance to new technology, and a lack of clarity around what AI can do.
But our latest State of Workplace Empathy data points a much different opportunity for leadership: Communication and transparency.
In our latest State of Workplace Empathy data, 61% of employees said AI makes them optimistic about their career future. Another 65% say AI has helped them advance in their careers and 54% say that AI gives them more agency and control over their work.
Employees see what’s possible for them with AI. What worries them is being expected to push forward without clarity, training, or support.
AI workforce readiness means your employees understand how the technology fits into their work. They know what is expected of them. They have access to training. They can experiment and ask questions without fear.
A company can have high AI usage and low workforce readiness at the same time. Employees may be using the tools because they’ve been told to, while still feeling unprepared for what comes next.
Kyndryl’s 2026 People Readiness Report describes a widening gap between AI adoption and workforce readiness. Its research found that organizations generating stronger results from AI are redesigning roles, investing in workforce preparation, and helping employees understand new ways of working.
The question leaders should be asking is not simply, “Are our employees using AI?”
It’s “Have we prepared them to succeed with it?”
Employees with employer-sponsored AI training are much more likely to say AI accelerates their career progression than employees who received limited training or were left to figure it out. They were also more likely to:
Training does more than teach employees how to use a tool. It tells them their employer expects them to have a place in the organization’s future.
If an organization is investing in AI while leaving its workforce to learn through trial and error, employees will notice. They will also draw their own conclusions about what that investment means for them.
Companies don’t need to promise that every role will remain unchanged. They do need to be candid about what is changing and give people a reasonable path to adapt.
When we looked at employees who described their organizations as empathetic, one number stood out: 87% said they had received adequate AI training from their employer.
Employees in empathetic organizations were also more likely to say AI made them optimistic about their future, gave them greater control over their work, and helped them focus on higher-value contributions.
Empathy, in this context, isn’t about reassuring employees that everything will be fine. It’s about being honest enough to acknowledge uncertainty and thoughtful enough to prepare people for it.
It shows up in the decisions leaders make:
That is empathy in action. It’s also solid change management.
You don’t need a perfect roadmap to move forward, but you do need a clear communication plan.
A product demonstration is not a change communication plan.
Employees want to know why the organization is adopting AI, how it may affect their roles, and what support will be available. If leaders can’t answer everything yet, they should say that. Silence rarely creates confidence.
A broad introduction to generative AI can build awareness. It won’t prepare every employee to apply AI responsibly and effectively in a specific role.
Training should reflect the work, decisions, risks, and opportunities employees encounter every day. It must also reach the groups most likely to be overlooked, including frontline workers and employees without regular access to corporate learning platforms.
A usage dashboard may show whether employees opened a tool. It can’t tell leaders whether those employees understand it, trust it, or know when not to use it.
Ask employees whether they feel prepared. Find out where they are improvising, where they need guidance, and where the technology is creating extra checking or administrative work instead of reducing it.
AI requires experimentation, and experimentation includes mistakes.
Employees need appropriate guardrails, but they also need room to learn. If every misstep is treated as a performance problem, people will either avoid the tools or hide the problems they encounter.
Trust is central to that environment. McKinsey’s research on AI transformation identifies trust in an organization and its leaders as a strong predictor of employees’ readiness to use AI. It also notes that employees are more likely to experiment and share what is or isn’t working when leaders are interested in learning rather than judging.
There’s tremendous pressure to move quickly right now. And I understand why.
No leader wants to look back and realize their organization waited too long while competitors moved ahead. But speed without readiness creates a different risk: companies may invest heavily in AI without preparing the people expected to turn that investment into results.
The organizations that pull ahead won’t necessarily be the ones that introduced the most tools or pushed adoption the hardest.
They’ll be the ones that gave employees a credible path forward.
