By training employees on AI tools that enhance their day-to-day responsibilities and being clear on expectations, organizations will drive adoption rates and gain trust to boot.
By Maggie Mancini
It’s not clear if AI is truly being embraced by employees. In fact, new survey data from Workday finds that nearly 70% of employees say they feel anxious or overwhelmed by AI and nearly 40% do not think AI is saving them time at all. What can HR do to help? Here, CHRO Cheryl Yuran of Absorb Software shares how organizations can show employees how AI fits into their real tasks and how it impacts overall decision-making.
HRO Today: Executives often say AI is giving employees hours back each week, yet many employees say it’s barely saving them any time. What’s driving that disconnect between leadership expectations and employee reality?
Cheryl Yuran: Executives are often optimistic about AI, and they see it as a breakthrough productivity tool that should give employees hours back each week. But, due to the state of upskilling, employees experience it differently—often as just another tool they’re expected to use on top of their existing workload that adds complexity, rather than take it away. When the technology isn’t explained, the work itself doesn’t change and the time savings executives promised don’t always show up in real life.
There’s also a clear difference in perception. Leaders tend to see AI as an opportunity, while many employees see it as a threat to their roles. That gap in how each group feels about AI shapes how they judge its impact and helps explain why expectations and reality don’t always match.
In many cases, AI isn’t immediately giving time back but shifting how it’s spent. Employees might write faster or summarize information more quickly, but they may not fully trust the technology, so they’re also reviewing outputs, fixing inaccuracies, and learning new systems. That extra oversight doesn’t always feel like a time savings.
Until AI is seamlessly embedded and employees feel confident in how to use it and understand where it needs human oversight, the time saved won’t be noticeable.
HROT: When AI adoption stalls inside organizations, what do you believe is really getting in the way from an employee standpoint?
Yuran: When AI adoption stalls, it’s usually not because employees refuse to use it. It’s because they haven’t been clearly shown where it fits into their actual work. Being told to “use AI” is vague. Without specific examples tied to their role, it can feel optional, confusing, or even risky to try.
There’s also a fear of getting it wrong. Employees worry about sharing sensitive information, making mistakes, or looking like they’re cutting corners by relying on a tool too heavily. Without clear guidelines and visible examples from leaders on how to use AI responsibly, most people fall back on familiar routines.
Adoption doesn’t stall because people are unwilling. It stalls because the change hasn’t been built into everyday workflows in a clear, supported way. Many organizations talk about AI in terms of “productivity” and “efficiency.” Why do those messages resonate in the C-suite but fall flat with employees on the ground?
Executives are focused on numbers, growth, margins, and output per employee, so language about productivity and efficiency naturally resonates with them. Employees hear those same words differently. If you tell a team AI will “drive efficiency,” some will quietly wonder whether that means fewer roles, tighter expectations, or pressure to do more with less. Even when that’s not the intent, that’s how it can land.
What resonates more is specificity. Instead of saying, “AI will improve 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 on client strategy,” or, “We want you to use AI to draft first versions, so you can spend more time refining and building new skills.” That kind of narrative shift makes it personal. It connects AI to someone’s actual day and shows how it could reduce tedious work or help them grow, rather than simply improve a metric on a dashboard.
HROT: Employees feel more anxious than excited about AI. What are organizations misunderstanding about how that anxiety actually shows up in day-to-day work?
Yuran: AI anxiety rarely looks like someone openly refusing to use a tool. More often, it shows up as hesitation and overcompensation. Some employees stick to familiar workflows because they’re unsure what’s expected or worried about making mistakes in public. Others use AI cautiously but don’t talk about it, unsure whether it signals initiative or puts their role at risk. Some respond by working longer hours to prove their value, even if AI is supposed to reduce workload. The misunderstanding is that leaders expect either excitement or pushback, when in reality anxiety often shows up as quiet stress, avoidance, or burnout.
That uncertainty chips away at confidence in ways that aren’t always obvious. Performance can look steady, but you may notice people contributing less in meetings, avoiding experimentation, or sticking closely to old processes. You might also see employees putting in extra hours to prove their value.
If organizations only look for open resistance, they’ll miss the real signs that point to AI anxiety. Leaders need to watch for shifts in engagement and energy, and make space in regular check-ins for honest conversations about what feels unclear or stressful in day-to-day work.
HROT: Companies are investing heavily in AI training, yet behavior often doesn’t change. Why, despite ongoing investments in AI tools, is employee behavior unchanging?
Yuran: Companies are investing in AI training, but many aren’t doing the hard work required to make that training stick. They tell employees that AI will save time on admin tasks, yet “admin” looks different for a sales rep, a recruiter, and a support agent. If leaders don’t spell out exactly how a specific role should use AI in real situations, employees are left guessing. When people aren’t sure how a tool fits into their real responsibilities, they default to the habits that already feel safe and proven.
A bigger issue is that expectations often don’t change. If a customer support team is measured on average resolution time, and leadership introduces an AI tool that speeds up research and drafts replies, then performance metrics should reflect that new standard. Leaders need to clearly say, in team meetings and written updates, “We now expect faster resolution times, and here are the AI tools that will help you get there.” That turns AI from a vague suggestion into part of how success is defined. And if managers aren’t modeling AI use themselves, whether by sharing their own use cases or spotlighting strong examples from their teams, adoption will stall.
Too many companies call AI a top-down priority but fail to show what that looks like in practice, which makes it feel like a mandate instead of a shared shift in how work gets done.
HROT: What advice do you have for HR and L&D teams who are trying to bridge the AI adoption gap between executives and employees?
Yuran: HR and L&D teams need to move beyond vague directives like “everyone should be using AI” or “AI training is now required” and translate strategy into clear, role-specific impact. That means showing a sales manager how AI can help them prepare for calls, a marketer how it can speed up writing campaign drafts, or a finance lead how it can assist with forecasting, so employees can see exactly where it fits into their day.
This is where an AI-powered learning platform becomes powerful. Teams can use AI-driven skill assessments to understand where each employee is starting from, then generate variations of the same training tailored to particular roles and existing skill levels. From there, they can craft personalized upskilling paths tied directly to their team KPIs, so AI training is not abstract, it’s connected to measurable business outcomes.
Shifting the narrative from efficiency to growth is also critical. Employees are more likely to engage with AI training when they understand how it will help them develop lasting skills and stay marketable. By framing AI skill building as a benefit to employees, not a risk to their roles, leaders can also build trust with their workforce and create lasting loyalty.
Mentorship and peer learning also are key levers that HR teams should be pulling to boost adoption and minimize anxiety. Leaders should describe their AI use cases to help set expectations and show, not tell, the impact AI can have on daily work. Consider adding incentives, too. Something as simple as a “best AI use case” knowledge shares in team meetings can encourage trial and error and teach employees use cases they may have never thought of.
From a metrics standpoint, HR teams should analyze learning performance and use insights to optimize results, like seeing which departments have completed training and tracking skill growth over time. A lagging department is an opportunity to reach out to the department leader and discuss challenges to adoption and ways to remediate, such as dedicated AI practice time, manager expectations, or more support from peers.
Boosting AI adoption at all levels takes a people-first approach, where leadership enforces use and learning through data-informed personalization, mentorship, and recognition.
