monday.com’s Human Approach to Solving AI Anxiety

For Mental Health Awareness Week, HR Chief Magazine sat down exclusively with Cat Paterson, Regional People Director at monday.com, to explore the psychological impact AI is having in the workplace.

While AI is often seen as a tool to increase productivity, Cat shares a more human view, explaining that 78% of UK directors don't expect AI to reduce headcount, yet "AI anxiety" and "AI guilt" remain significant stressors for employees. 

1. How can leaders identify and mitigate "AI anxiety," where employees fear that the technology is a threat to their job security rather than a support tool?

AI anxiety is understandable, especially when so much of the public narrative focuses on replacement instead of empowerment. But inside UK businesses, the reality is far more nuanced.

monday.com's World of Work research highlights a gap between leadership intent and employee concerns - more than three-quarters (78%) of UK directors do not expect AI to reduce headcount, and almost a third (32%) actually expect to hire more because of it.

That matters because people need to hear a more honest and balanced story about what AI means for work.

The fear is less about AI itself and more about losing visibility, value or control as roles evolve. That’s why leaders need to move beyond simply talking about productivity gains and focus on building confidence within the business. AI may increasingly handle repetitive tasks, but it doesn’t replace instinct, relationship management, and critical thinking.

Reducing AI anxiety comes down to openness, training and trust. People need space to ask questions, experiment and understand how AI can support their own growth.

2. What role does "AI guilt" - the feeling that using automation is "cheating" or lazy - play in increasing workplace stress and burnout?

One of the emerging challenges in UK workplaces isn't resistance to AI itself, but the stigma that can come with using it. The notion of ‘AI guilt’ is coming from the top down - in our World of Work report, UK leaders cited the fear of being judged for using AI as one of their biggest workplace concerns. 

There’s a definite tension between the desire to innovate and feeling undermined by the tools that help you do it. We can’t ignore the emotional side of AI use, where the fear of being judged actually impacts rates of adoption.

To tackle this at monday.com, we’re actively making it an open talking point. To build confidence and encourage experimentation, my team regularly talks about how we use our tech stack, sharing wins and, most critically, pain points along the way to build our skillset. 

3. How can businesses ensure that the time saved by AI is used to improve employee work-life balance instead of simply increasing the volume of tasks?

AI can unlock real efficiencies, but the most important question is what happens with that time once it’s been freed up.

If every saved hour is simply replaced with more work, businesses risk turning AI into another source of pressure rather than a tool for more sustainable ways of working.

The organisations that get this right will be the ones asking a better question: not just “how much faster can we move?”, but “what do we want our people to have more time for?”

That might mean more space for strategic thinking, stronger team connection, or simply creating some breathing room throughout the week.

If AI is only used to push teams harder, trust will erode fast. Businesses need to be deliberate here: reassess workloads, pressure-test whether expectations are rising sustainably, and make sure that increased efficiency creates tangible benefits for employees too, not just the bottom line. 

4. What psychological guardrails can managers put in place to help employees feel confident enough to challenge AI without fearing it will reflect poorly on their performance?

One of the biggest misconceptions about AI adoption is that using it effectively means trusting it completely. It doesn’t. As AI becomes more central to workplace decision-making, discernment and critical thinking will be even more important - AI is not a universal solution, but a valuable tool and support.

That can feel uncomfortable, particularly in cultures where speed and innovation are heavily rewarded.

As people leaders, we’ll need to intentionally create environments where thoughtful challenge is encouraged to tackle fears about questioning tools and outputs.

In practice, that means building regular review points into workflows, being clear about where human oversight and sign-off are non-negotiable, and consistently reinforcing that employees are trusted to apply judgement, not just accept outputs at face value.

Managers need to create spaces where teams can openly explore where AI may have introduced bias or got it wrong, without any fear of blame. 

5. In what ways can open conversations about AI tools reduce the cognitive load and "imposter syndrome" often felt by staff trying to keep up with rapid tech changes?

The pace of AI adoption creates a particular kind of pressure where it feels like everyone else is adapting faster than you. If employees are expected to upskill, experiment, and stay ahead, they need clear guidance on what “good” looks like.

What’s often missed is that this isn’t a capability problem; it’s a visibility one. When AI learning happens quietly or inconsistently across an organisation, people overestimate how far ahead their colleagues are, which can lead to similar feelings of fear we associate with imposter syndrome.

The fix is making progress more tangible. Clearer benchmarks, role-specific learning pathways, and more realistic expectations around adoption all stop AI capability from feeling like a moving target.

The businesses that get this right will be the ones that treat AI as something structured and developmental, not a constant race to keep up. 

6. How can leadership promote a sense of "digital wellbeing" so that AI remains a positive contributor to a healthy office environment?

Digital wellbeing starts with the same principle as any other form: people first. That only means something if AI adoption stays closely connected to individual experience. 

Ask for feedback on improvements, friction points, and support needs, then reflect it in implementation.

Be deliberate about where AI shouldn’t encroach, protecting focus time, resisting the pull to be always-on, and making sure efficiency gains don’t quietly become higher baseline expectations.
Wellbeing strategies can’t remain static when AI is evolving so fast.

We need responsive guardrails around how it’s introduced, monitored, and adapted. People should never feel like they’re expected to catch up alone.