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How employee feedback can help drive successful AI adoption in the workplace
TL;DR: Successful AI adoption strategies start by listening to employees. Here’s how to gather actionable feedback to tailor your adoption strategy.
When it comes to AI adoption in the workplace, most conversations are focused on the benefits companies will reap. What’s the bottom-line impact? How much more productive, faster and smarter can this technology make us? How can we do more with less?
But I think we all know that AI won’t transform work just because the technology exists. It succeeds (or fails) based on whether employees feel supported and motivated to use it. Rolling out a policy, appointing a Chief AI Officer, or running training sessions aren’t enough to drive the deep integration many organizations are after. It depends heavily on a company’s culture and employee sentiment:
- How are employees feeling about it? Are they excited? Fearful? Confused? Annoyed?
- How are leaders talking about it and modeling desired behavior?
- How are teams encouraged to integrate it into their day-to-day work? Is using AI stigmatized or incentivized?
- How is it being woven into a company’s ways of working versus being a shiny fad?
Obviously, there are huge technical components to this transformation, but there are also equally important psychological considerations. That’s why organizations should take a step back to understand how their employees are experiencing AI in their workplaces and explore the psychology behind the change. Let’s dive in.
The human side of AI adoption
First, let’s state the obvious and ground ourselves in some relatively current data: There’s plenty of momentum around AI adoption as well as gaps. (It’s also worth noting that you can probably track down data points to support any point you’re trying to make.) Owl Labs shared that nearly half of employees (46%) say they are already somewhat or heavily reliant on AI in their work, with another 36% using it occasionally for specific tasks.1 There’s a disconnect between leaders and employees. 82% of C-Suite leaders say their organizations are using AI solutions in workflows, yet only 34% say they’ve equipped employees with AI tools.2 Meanwhile, Gallup data shows that only 22% of employees say their leadership has provided a clear plan for how AI will be used.3
But beyond usage rates or training and communication gaps, there are some important social dynamics at play. Unsurprisingly, some uncertainty is in the mix – 41% of employees think their job will certainly or probably disappear entirely in the next 10 years because of AI.4
There’s interesting data around a potential adoption barrier that Harvard Business Review calls a “competency penalty” – where peers penalize their colleagues for using AI. Here’s a glimpse at the study they shared:
“When reviewers believed an engineer had used AI, they rated that engineer’s competence 9% lower on average, despite reviewing identical work. This wasn’t about code quality—ratings of the code itself remained similar whether AI was involved or not. The penalty targeted the perceived ability of the person who wrote it.
The competence penalty was more than twice as severe for female engineers, who faced a 13% reduction compared to 6% for male engineers.
Most revealing was who imposed these penalties. Engineers who hadn’t adopted AI themselves were the harshest critics…
Follow-up surveys with 919 engineers revealed a fuller picture. Many engineers actively anticipated this competence penalty and strategically avoided using AI to protect their professional reputations. Those who most feared competence penalties in the tech industry —disproportionately women and older engineers —were precisely those who adopted AI least.”
This underscores the importance of understanding psychological barriers to AI adoption that are just as real as technical ones.
Recommended: 5 AI Mistakes To Avoid In Internal Comms
Why feedback matters
This is where gathering employee feedback comes in to truly understand your people’s current mindset. Below are a few topic areas you can explore in an employee survey or focus groups to consider both technical and social awareness and comfort among employees. (Be sure to check out our blog post about how to avoid common mistakes in employee surveys.)
Awareness and application
- Do employees know what AI tools are available to them?
- How clear are they on the company’s policy on AI?
- How often are they using AI and for what?
- Can they name more than one or two use cases for their role?
(Rebecca Hinds, Head of the Work Innovation Lab at Asana is interviewed in an excellent podcast episode where she shares that in many cases, workers aren’t sure WHERE to apply AI to their work. Not many workers can name more than two use cases specific to their role.)
Perceptions
- How do they feel about using AI? Excited? Anxious? Fearful? Skeptical?
- Do they feel supported in experimenting with AI? Or do they worry colleagues will see them as lazy for using it?
- Do they feel encouraged by leaders? How do they see leaders using AI, if at all?
Training & Support
- Do they feel they’ve had enough training to use AI effectively?
- What formats of support (guides, peer learning, live demos) are most useful?
Impact on Work & Careers
- Do they feel AI will help them grow in their role? Or do they worry it will make their skills less relevant over time?
How to use the data: The goal isn’t just to measure sentiment, but to use it to guide tailored strategies for adoption, such as:
- Prioritizing use cases that make the biggest difference for employees
- Clarifying and shaping policies that encourage use without stigma
- Understanding fears or skepticism that need to be addressed
- Urging and enlisting leaders to show up beyond as an executive sponsor and be involved in AI experimentation, programs, hackathons, etc.
- Understanding where to focus training and tailoring it accordingly based on teams and roles
Using feedback to shape an effective path forward
Integrating AI is a fundamental shift in how work gets done. The question is whether that shift feels supportive and empowering, or confusing and threatening. ROI is far more likely when organizations invest in listening to employees, because the true return on AI starts with their people. The most successful adoption strategies aren’t copied from industry peers or crafted by executives behind closed doors. They’re tailored to the unique needs, concerns, and ambitions of employees. Employee feedback enables leaders to meet their people where they are – whether that means building greater awareness, providing more tailored training, or addressing cultural barriers. This is how AI adoption can move beyond compliance; it can become a real culture change with staying power.
Cited sources:
1 https://resources.owllabs.com/blog/pulse-survey-2025
2 https://www.thomsonreuters.com/en-us/posts/corporates/c-suite-survey-2025/
3 https://www.gallup.com/workplace/691643/work-nearly-doubled-two-years.aspx
4https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain
FAQs
What are common psychological barriers to using AI at work?
Employees may worry that AI could make their roles less relevant, or even replace them over time. Others hold back because of cultural stigma, such as the “competence penalty,” where colleagues may judge them as less capable if they rely on AI. These dynamics can create hesitation even when tools are available.
Why is employee feedback important for AI integration?
Feedback helps leaders understand where employees are on the adoption curve—whether they need greater awareness, training, or cultural support. Using this insight, organizations can tailor their strategies to address real employee needs instead of making top-down assumptions.
What makes an AI adoption strategy successful?
The most effective strategies aren’t copied from peers – they’re designed around employees’ unique needs and concerns. Tailoring policies, training, and use cases based on employee feedback ensures AI adoption becomes a genuine culture change rather than a compliance exercise.
Ready to gather actionable feedback?
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