The Real Reason Employees Aren't Using Your AI Tools
- rmclements10
- Mar 30
- 10 min read
Updated: Mar 30
For a period of time I managed Accounts Payable for a large company. Aside from having zero accounting experience and it being a terrible fit for my skills, I mastered it pretty quickly. Despite mastering it and finding ways to make our process more effective, I had the same thought every single day.
"A literal monkey could do this job."
There was nothing uniquely human about me or what I was doing. I knew, long before AI would come along, that I added little value to the company.
I want to start with a question I've started asking every leadership team before we build a single slide deck or draft a single piece of communication about an AI rollout.
"What did you announce last quarter that involved headcount reductions? And how did you explain it?"
The room gets quiet. Sometimes defensive. Most of the time, it was NOT addressed. In any way. I usually have to make the connection - that the workforce they're now asking to enthusiastically adopt AI tools watched colleagues lose jobs in a press release that cited AI as the reason.Sometimes a few months ago. Sometimes a few weeks.
Then they wonder why adoption is low.
This is the context that nobody in the vendor pitch deck accounts for. This is the context your employees are swimming in when you send out the AI rollout announcement, when you schedule the training, when you post the internal blog about how excited leadership is about this transformative technology.
You are asking people to embrace the thing they've just been told cost their colleagues their jobs. And you're wondering why the message isn't landing.
The Layoff Context You Cannot Ignore
Analysts estimate that 23% of Q1 2026 layoffs now explicitly cite AI automation or AI-driven restructuring in SEC filings or press releases - up from 14% in Q4 2025. Tech Insider
Over 100,000 employees were affected by AI-attributed layoffs in 2025 alone. The announcements are everywhere, and they span industries well beyond tech: financial services, logistics, consulting, media, retail, manufacturing.
The specific examples employees are reading about aren't abstract. Block cut 4,000 jobs - nearly 40% of its workforce - with CEO Jack Dorsey directly attributing the move to AI, stating that "intelligence tools have changed what it means to build and run a company." Amazon eliminated approximately 30,000 corporate jobs across two rounds. Accenture cut 11,000 roles tied to restructuring around changing work. Baker McKenzie laid off up to 10% of its global workforce to shift toward AI efficiency. Programs
OpenAI CEO Sam Altman acknowledged at the India AI Impact Summit in February 2026 that companies are engaging in what he called "AI washing" - using AI as a scapegoat for workforce reductions they would have made regardless. "I don't know what the exact percentage is," he said, "but there's some AI washing where people are blaming AI for layoffs that they would otherwise do." Gizmodo
Nearly 60% of hiring managers surveyed admitted they emphasize AI's role in cuts because it's viewed more favorably than financial constraints.
Peter Cohan, a professor, told Built In that companies cite AI in layoff announcements because it is "the least bad reason companies can use." Built In
So this is the actual communication environment your employees are living in: companies are using AI as cover for financially driven cuts, the CEO of OpenAI is confirming it publicly, and your employees (many of whom are smart, experienced professionals) have figured this out. They don't live under a rock. They follow company stock prices and LinkedIn trends. They know what's happening. They read the same news you do.
And then you send them an email about how AI is going to empower them in their roles.
When executives "AI wash" their layoff announcements, they may be revealing that they view AI as a means for eliminating jobs and that causes workers not to trust, or even sabotage, future AI adoption plans.
Do you get it yet?
Has it clicked?
What It Has Done to Engagement
The engagement data coming out of 2025 and into 2026 isn't just bad. It's historically bad and the connection to AI anxiety is direct.
Gallup confirmed at the start of 2026 that U.S. worker engagement has fallen to its lowest level in a decade, with just 31% of employees reporting feeling engaged.
For the first time in Gallup's history of tracking the life evaluation of the U.S. workforce, more workers report struggling than thriving - 49% to 46%. Gallup
ADP Research's Global Workforce Survey of more than 39,000 workers across 36 markets, released in March 2026, found that only 22% of global workers strongly agreed their job was safe from elimination. Only 19% reported full engagement on the job. ADP Media Center
One in five employees feels secure.
One in five is fully engaged.
That is the workforce you are trying to build an AI adoption program inside of.
Forrester Research identified a growing segment it calls "coasters" - disengaged workers who don't believe their employer deserves their discretionary energy. This group is projected to reach 28% of the workforce in 2026. Forrester's explanation for the trend is direct: employees watch colleagues laid off for AI that never materializes, see entry-level positions disappear, and observe what feels like offshore arbitrage being branded as innovation. When a quarter of your workforce is actively withholding effort, no AI tool compensates for that loss. HR Executive
ADP Research found that employees who expressed fear of AI were twice as likely to experience high stress at work and more likely to be actively job hunting. The fear isn't a communications problem to manage around. It is a direct, measurable drag on organizational performance - and it compounds over time. It impacts colleagues and workplace enthuasism. It means employees aren't going to invest in innovative approaches to your work.
The confidence collapse isn't hitting everyone equally. The gap is most pronounced among older workers - a 35% decrease in AI confidence among baby boomers and a 25% drop among Gen X workers.
These are not people resisting technology out of stubbornness. These are experienced professionals who built expertise over decades watching it become potentially irrelevant, with no clear organizational investment in helping them rebuild.
So now the professionals who know your industry the best, who are most likely to make connections and champion innovations at precisely the most essiental intersections - they are now the ones that are disengaged.
Fear of Becoming Obsolete.
It's different from fearing you'll be laid off next quarter. FOBO is the creeping sense that your skills are degrading in real time, that you're falling behind faster than you can catch up, and that the window to stay relevant is closing while you're still trying to figure out what relevant means. People Managing People
Employees at organizations undergoing comprehensive AI-driven redesign are significantly more worried about job security - 46% - than those at less-advanced companies - 34%. The closer people get to actual AI deployment, the more threatened they feel. BCG
The more real the AI integration becomes, the worse the anxiety gets - even when the communication is saying the right things. Because the communication isn't the issue.
Why Your Communication Strategy Is Probably Making It Worse
I've designed a lot of AI communication campaigns over the years. I've also watched a lot of them backfire. And when I look back honestly at the ones that made things worse, they all shared a common flaw: they were designed to overcome resistance rather than address the conditions producing it.
The standard playbook looks something like this. You lead with benefits and vision. You feature success stories from early adopters. You position AI as an augmentation tool that makes work more meaningful. You train managers to answer common questions. You track open rates and call it engagement.
None of that is wrong, exactly. It's just operating at the wrong level of the problem.
The intervention for AI adoption resistance isn't better messaging - employees have stopped believing promises that "AI won't replace you, it will augment you" because they've watched it not be true for thousands of their peers.
What might actually work is showing them the skills that matter for roles that aren't disappearing, with investment that proves the organization means it.
Harvard Business Review's research on the psychology of AI adoption put it this way: 76% of executives believe their employees feel enthusiastic about AI adoption. The actual employee number is 31%. Leaders are more than two times off the mark. Harvard Business Review
When the perception gap is that large, you don't have a messaging problem. You have an intelligence problem. Leadership does not know what employees actually think, feel, and believe about this program. And you cannot design effective communication from inside that gap.
"What executives see as reallocating skills, employees experience as a threat to their jobs and expertise." Management transparency can reduce fear and build trust, but communication by example matters more than communication by message.
The Upskilling Gap That's Fueling Everything
When a manager took an interest in me and encouraged me to app[y for a leadership training, I was honored and excited. He saw my potential and thought I would be a great fit for a leadership position within the company, so he encouraged me to get additional training in some of the areas where I didn't have experience. Ultimately, making me the perfect candidate for a different position.
I went through the training and stepped into the management role and he was right. I was a great fit for my new role.
The only reason this process was exciting instead of terrifying - is because I felt valued. I felt like my hard work and going above and beyond was being noticed and appreciated. I was excited to go through additional training, because someone saw how I could step into more responsibility and be even more valuable to the company - which I was totally cool with because it also came with more money and more recogonition.
McKinsey's research on AI upskilling found that evidence suggests training alone rarely drives sustained behavior change. In a study of Microsoft 365 Copilot adoption, nine in ten participants said formal training would be useful - yet seven in ten ignored onboarding videos entirely, relying instead on trial and error and peer discussion. McKinsey & Company
We keep building training programs and wondering why people aren't changing their behavior. Lasting adoption happens when employees know what to do differently, and also believe in why it matters, feel supported by leadership, and see reinforcement in the systems around them.
Training addresses the first condition. Most organizations never build the other three.
People aren't afraid of AI. They're afraid of being left behind (or completely replaced) by it.
When AI adoption is not met with real investment in professional development, the technology looks less like an empowerment tool and more like a threat.
What Actually Works - And What Leadership Has to Own
You cannot fix an employee trust problem with communication alone.
You can only fix it with decisions. Decisions that demonstrate that leadership means what it says about AI being an augmentation tool and not a workforce reduction strategy.
If you have a workforce reduction strategy in mind - that's fine. Just own it. And for the love of pete don't blame it on AI.
And - can I shout this from the rooftops?
That means if your organization has used AI as a cited reason for layoffs while simultaneously running an AI adoption program, the first conversation that needs to happen is not with your employees. It's with your C-suite, about the credibility gap that now exists between those two messages and what they're willing to do to close it.
If not, you can just go bang your head against a wall to prepare yourself for the next 2 years of your life.
Acknowledge the environment directly.
Workers want certainty, but this could be a good opportunity for leaders to communicate that 'We are figuring this out together.' That approach builds trust. The instinct is to project confidence and certainty. The research says that kind of transparency, including honesty about what leadership doesn't know yet, is more effective. Not weakness. Not avoiding the questions.
Invest visibly in skills before you demand adoption.
McKinsey found that when reskilling is designed as a talent and change journey — not a standalone training module - it can unlock adoption and turn what feels like a threat into a source of loyalty. The sequence matters. If the organization invests in your ability to work with AI before it demands that you use it, the psychological meaning of the tool changes entirely.
Make leadership behavior the primary message.
Transparency from leaders about how they are personally learning to use AI, what they find difficult, and what they're getting wrong builds far more trust than polished internal communications about AI's transformative potential.
You don't need the CEO pretending she knows how to use this tool to do a job she's never done - find a manager who has DONE THE JOB. Who knows the impact it has the potential to make. Have them lead a walk through of the new tool, point out where it can save them time, acknowledge the weak areas, and praise the intersections where they think it could be a game changer.
The leaders who are most effective in AI integrations I've been part of share their learning publicly - including their failures. That kind of visible modeling is not comfortable for most leaders. It is, however, what the data says works.
Build real listening infrastructure before you build messaging.
The organizations that have successfully narrowed the executive-employee perception gap on AI aren't doing it by sending better surveys. They're doing it by creating ongoing, psychological-safety-enabled channels where the real sentiment can surface and where leadership actually responds to what they hear. Not with spin. With decisions.
Make the job security conversation explicit.
This is the one most organizations keep dancing around, and it is costing them more than they realize. BCG found that when workers are well-informed and genuinely familiar with AI, apprehension turns into enthusiasm. The fear is not about the technology. It's about the unknown implications of the technology for their specific role, their specific team, their specific future.
Address it directly. In town halls, in manager conversations, in one-on-ones. "Here is what we see AI doing in your role. Here is what it does not do. Here is what we are investing in to make sure you have what you need to work with it." That conversation, had honestly, lands differently than any amount of vision messaging.
Compliance driven by fear is not adoption.
It looks like adoption on your dashboard. It produces the green numbers in your quarterly review. And it completely fails to generate the behavior change, the workflow integration, the genuine human-AI collaboration that actually produces ROI.
The employees who are truly using AI, changing how they think, how they work, what they're capable of, are the ones who feel safe enough to experiment (to make mistakes), skilled enough to use the tools effectively, and trusted enough by their organization to believe the investment runs both ways.
Create sanctioned safe spaces — small forums or Slack channels where employees can surface concerns and share failures without consequence. The ones who voice concerns and mistakes early are your best allies, not your obstacles.
Building those conditions is harder than building a communications campaign. It requires decisions, not just messages. It requires leadership behavior, not just leadership messaging. And it requires an honest reckoning with what your organization has communicated - explicitly and implicitly - about what AI means for the people inside it.
That reckoning is where the real work starts.
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