New data from Workday reveals a startling disconnect: while leadership is cheering over newfound efficiency, nearly 40% of workers feel AI hasn’t saved them a single minute. Even worse, 70% of employees report feeling anxious or overwhelmed by the technology, compared to less than 30% of executives.
So, why is the adoption stalling? According to Cheryl Yuran, Chief Human Resources Officer at Absorb Software, the problem isn’t the tech—it’s the talk. While the C-suite loves words like “efficiency” and “productivity,” those terms often feel like empty jargon to the person on the front lines. To a busy employee, “productivity” doesn’t explain how AI helps them get through a mountain of emails or a complex project.
In this week’s HR Query, Yuran breaks down why high-level promises aren’t enough to close this perception gap. She offers a practical solution for HR leaders: shifting away from blanket mandates and toward personalized, role-based learning that shows every employee exactly how AI supports their specific, day-to-day tasks.
Here’s what she had to say.
Recent research shows a disconnect between executives and employees when it comes to AI’s impact on productivity. Why do you think leaders believe AI is saving time, while many employees say they aren’t seeing the benefit?
Executives are looking at AI from a big-picture view, tracking things like how quickly customer issues get resolved or how much work each team produces. On paper, the numbers look good, and if they’re investing money in time-saving tools, they expect to see that pay off. However, employees are focused on something much more straightforward, their actual workday.
While AI may speed up one part of a task, it also takes time to learn how to use it effectively and ethically, and most organizations aren’t supporting that learning in a meaningful way. Employees end up reviewing output, fixing inaccuracies, and figuring out how to prompt better, all on top of their existing workload. On paper, productivity improves, but in practice, the day still feels just as full. Employees might say they’re able to do more, but it’s unlikely they’ll say their day has gotten easier or that they’ve won real time back.
There’s also a clear difference in how each group perceives AI to begin with. Leaders tend to see it as a competitive advantage and a sign of organizational progress. Many employees see it as a threat, or at least a minimum, an added layer of complexity. That gap in mindset shapes how each group experiences AI. It’s like rolling out a new sales platform or switching from Microsoft Office to Google Suite. Employees need real resources and support to learn new tools before they can feel the benefits. Until executives invest the same level of focus on AI enablement through role-specific training and mentorship as they do on the tools themselves, the time savings will remain theoretical for the people doing the everyday work.
Nearly 70% of employees report feeling anxious or overwhelmed by AI. From an HR perspective, what’s driving that anxiety? Is it fear of job displacement, lack of clarity, or something else?
At its core, AI anxiety is rooted in a lack of communication. When leaders talk about efficiency, employees may hear “job cuts,” even if that’s never the intent. A leader might say, “AI is going to improve our team’s output,” and an employee walks away wondering whether that means their role is at risk. That’s the origin of what we’re now calling “AI scapegoating.” We’re seeing companies reorganize and eliminate roles for several reasons, whether it’s to correct for over hiring or simply a strategy reset, but instead of being very clear about why, they’re defaulting to broad statements, like AI boosting efficiency. Employees then view the technology as a threat and are scared that leaning into it will be the very reason they end up being replaced. The fix isn’t a one-time all-hands announcement. It’s consistent, specific communication that connects AI to how work is evolving, not shrinking. Leaders need to say clearly: “We’re using AI to free you from the administrative tasks that pull you away from creative, strategic work, not to reduce headcount.”
There’s also a confidence gap that doesn’t get enough attention. Many employees are unsure what “good” AI use even looks like in their specific role. Without guardrails or training, they’re nervous to use it, worried they’ll look lazy, violate an ethical standard, or get something wrong in a way that reflects poorly on them. This anxiety also runs deeper than job security. Employees are questioning whether their current skills will still matter a year from now, especially when their organizations aren’t actively supporting skill development alongside the AI rollout. If a marketing manager isn’t shown how AI can help them develop stronger campaign strategies and grow their career, they’re left wondering if they’re being phased out rather than leveled up. When companies don’t clearly connect AI adoption to individual growth, anxiety builds, and it often shows up not as open resistance, but as quiet disengagement, avoidance, or burnout.
How can companies shift from high-level AI promises to practical, day-to-day value for employees? What does that look like in action? Where are leaders failing to put this into action?
Companies need to get specific about how AI fits into each role, not just encourage adoption broadly. That means clearly defining where teams expect efficiency to show up and giving employees a concrete picture of what success looks like with AI in their workflow. Instead of saying, “AI will improve your productivity,” a leader might say, “Our goal is to use AI to cut the time you spend on weekly reporting in half, so you can focus more on client strategy,” or, “We want you to use AI to draft first versions of briefs so you can spend more time refining ideas and building new skills.” That kind of specificity makes it personal and shows employees how AI could reduce the tedious parts of their jobs, rather than simply improving a metric on an executive dashboard.
Where leaders most often fall short is assuming that giving employees access to a tool will automatically change behavior. That’s unrealistic – the expectations have to change. If AI changes how a task gets done, leaders also need to adjust goals, timelines, or output expectations so employees aren’t just asked to do more with the same constraints. For example, if a customer support team adopts an AI tool that speeds up research and drafts replies, performance expectations should reflect that shift. Managers should say explicitly, in team meetings and written updates, “We now expect customers to hear back faster, and here are the tools to help you get there.” That turns AI from a vague suggestion into part of how success is defined. Without that kind of clarity, employees absorb AI as an added layer on top of everything else, which creates exactly the kind of burnout and resistance companies are trying to avoid.
What role should HR play in closing the perception gap between leadership and employees when it comes to AI adoption?
HR has a unique position here because we see how AI adoption feels on the ground, not just how it looks in the data. We hear directly from managers and employees, so we’re often the first function to spot confusion or burnout tied to unclear expectations. That visibility is valuable, but it only closes the gap if HR actively translates what we’re hearing into action, not just reporting. Concretely, that means bringing employee sentiment into leadership conversations and pushing back when AI strategy is being communicated in ways that are too vague to drive impact.
HR’s most important structural role is defining how AI use connects to performance and career development so employees aren’t left guessing. That might look like updating job expectations to include AI skills, building AI training into existing learning paths with role-specific examples, or working with managers to ensure that AI comes up in career conversations, framed as a growth opportunity rather than just another requirement. HR can also use data strategically by tracking which departments have low training completion rates, then proactively work with department heads to identify what’s getting in the way, whether that’s unclear expectations, lack of time, or a need for more peer support. When those expectations are clear and leaders model responsible use themselves, employee trust tends to follow.
For organizations that want to accelerate AI adoption without increasing employee burnout or resistance, what is the first step you would recommend?
First, employees need to clearly understand the relevance of AI to their specific role and how using it connects to their performance and growth. Without that connection, adoption feels optional at best and risky at worst. This isn’t a general training email. It’s a manager sitting down with their team and saying, “Here’s how I want you to use AI on the proposals you’re building,” or HR partnering with department leaders to create role-specific training tailored to what each team does. When AI goes from abstract to “this is how it makes your day easier,” employees engage differently.