AI is pushing tech companies to rethink how they organize teams, assign work and plan for the future. However, while an AI-driven restructuring plan may promise greater productivity and a stronger competitive position, those benefits don’t necessarily make the changes easy for employees to navigate.
For leaders, the challenge isn’t just deciding what needs to change; it’s managing how those changes affect the people expected to carry them out. Below, members of Forbes Technology Council discuss leadership mistakes that can damage employee morale during AI-driven restructuring, even when the business rationale is sound.
1. Asking Employees To Automate Away Their Own Jobs
One mistake is asking employees to embrace AI while making them audition for their own survival. If showing you how to automate their work puts their jobs at risk, you’ve made hiding knowledge a rational response. Be explicit about what is changing; “AI made us do it” is an abdication of command. You cannot demand adaptability from your people while outsourcing your own accountability. – Frederik Gregaard, Cardano Foundation
2. Leading With The Numbers Instead Of The Mission
The mistake is leading with the math, not the mission. Announce that “this role gets automated,” and people check out, even when the logic is airtight. At Agiloft, we never mandated AI. Instead, we showed the work: weekly demos, real wins and leaders doing it alongside everyone else. People don’t resist AI; they resist being told to adopt it without seeing why it’s worth it. – Eric Laughlin, Agiloft
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3. Neglecting Workforce-Wide AI Readiness
The biggest mistake a leader can make is to not bring employees along on the AI journey. Leaders who intentionally bring their entire workforce on this journey can pilot new technologies more broadly, implement rules and processes faster, and foster an organizational culture of innovation. At MetLife, we deploy a federated, CEO-led AI strategy where the ability to adapt is centered on our employees’ appetite to learn, unlearn and relearn skills at scale. – Bill Pappas, MetLife
4. Failing To Define Where Human Judgment Still Matters
Make a clear distinction between what AI will be utilized for and where human intervention and decision making will still be paramount. Do not use a “replace the process using AI and increase efficiency” narrative across the board. It can create job insecurity and bring down morale. Instead, get the workforce trained on the safe use of AI, and help them think about where they can bring efficiency into their own roles. – Komal Goyal, 6e Technologies
5. Succumbing To AI Hype And Fearmongering
Don’t perpetuate AI hype. Contrived fear-mongering is suffocating, and leaders who either knowingly or unwittingly succumb to it kill their credibility with their teams, their stakeholders and their customers. The hype- and fear-based mania takes various forms, including elimination of jobs, industries and companies. Leaders who cannot separate fact from hype are starting to stand out in most undesirable ways. – Ricardo Madan, TEKsystems Global Services
6. Leaving Employees Guessing About Their Future Roles
The biggest mistake is asking people to automate their work without clearly explaining what their roles will be in the future. It risks each “efficiency boost” via AI being seen by employees as a signal that they are needed less and less, which is naturally a big hit to morale. Leaders need to give their people a clear plan and opportunities to contribute to the decisions and changes being made. – Roman Eloshvili, XData Group
7. Dismissing Employee Concerns As Resistance
The critical mistake is treating employee hesitation as stubborn obstruction rather than recognizing the natural friction between established tradecraft and emergent tools. When leaders dismiss anxiety as mere drag, they alienate the domain experts whose judgment and institutional trust are essential to navigating the transition. – Sean Maday, Game Plan Tech
8. Failing To Update Career Development Paths
One mistake is restructuring work around AI without restructuring career paths. The routine tasks AI can automate are often the same tasks through which employees build experience and prepare for more complex roles. Remove that work without creating new ways to develop, and people may stop seeing a future for themselves in the organization. Leaders need to show not only how work will change but also how employees can continue to grow as it does. – Klaudia Zaika, Apriorit LLC
9. Announcing AI Changes Without A Reskilling Plan
The biggest mistake is announcing the “what” without a plan for the “who.” When AI is framed only as cost cutting, people see it as an enemy. Leaders must share a different mindset: AI is an ally that helps us improve in our roles and deliver more—and better—work. Pair every change with reskilling, and help teams bring AI into their day-to-day work. People commit when they see themselves in the future. – Maritza Diaz, ITJ USA, Inc.
10. Failing To Match AI Promises With Actions
The mistake is letting actions contradict the message. If AI is about productivity and not headcount reduction, prove it: Invest in training, give people time to adapt, and redeploy talent into value-added work. When employees see leadership follow through on what they’ve promised, they lean in. When they don’t, trust breaks, and every AI initiative after that encounters resistance instead of adoption. – Samuel Martinez, SDG Group
11. Introducing AI Without Redesigning Work
The most damaging mistake is announcing the AI strategy without redesigning the work. Leaders explain why AI matters, then leave teams to figure out on their own which tasks change, who decides what and how performance will be measured. People rarely lose morale because of change. They lose it because of ambiguity. When the old process stays in place and AI is layered on top, employees end up doing both jobs, and the technology starts to feel like a threat instead of relief. – Alessio Alionco, Pipefy
12. Rolling Out AI Without Proper Safeguards
One of the most serious mistakes tech leaders can make is rushing to implement AI tools without the necessary safeguards and governance measures in place. If employees see their leaders creating unnecessary vulnerabilities by introducing poorly managed AI solutions without a plan in place to secure them, it sends a damaging message that harms morale and erodes faith in leadership. If those vulnerabilities lead to negative outcomes, that faith can be hard to win back. – Peter Garraghan, Mindgard
13. Projecting Certainty About An Uncertain Future
Don’t project certainty you don’t have. Leaders often don’t yet know how every role will change, so they either go quiet or promise too much; both erode trust once reality diverges. Say plainly what has been decided, what hasn’t, and when people will hear more. Then involve teams in redesigning their own work; the people doing it often see first where AI truly helps. – Kuldeep Chowhan, Walmart
14. Pitting AI Against Employees
A huge mistake is positioning AI as a competitor to human roles. Most human roles will be redesigned because of AI, not completely replaced. The relationship between people and AI is that of a tango: It requires rhythm, balance and trust. Positioning AI as a collaborator and focusing on getting the human side ready to work with AI is the best recipe for getting people engaged with the initiative to make AI work, even when the business case is not clear. – John Kostoulas, Dayforce
15. Letting Uncertainty Linger
One leadership mistake is treating AI restructuring as a business decision that only needs to be explained, rather than as a human transition that needs to be led. Employees can understand the need for change, but vague communication and prolonged uncertainty make people feel disposable. Clarity, transparency and empathy are critical to maintaining trust and morale. – Nandish Dave, BitGo Inc.
16. Eliminating Entry-Level Roles
One mistake is cutting the entry-level rung because AI now handles junior work. Every senior engineer built judgment on that kind of work. Remove the rung, and mid-level staff see a ladder with nobody climbing behind them, while seniors wonder who will ever succeed them. Redesign junior roles around reviewing and correcting AI output, and count that review work toward promotion so that the pipeline keeps producing the experts who will someday check what the AI gets wrong. – Kuan Archer, Archer Innovative Solutions Group, LLC
17. Overlooking Employees’ Value In AI Transformation
It’s a mistake to believe that AI exists in a vacuum. In fact, the applications of AI with the best business outcomes are those that have been implemented under the strategic guidance and management of talented human beings. Morale is murdered if employees believe that they’re being undervalued and are expendable, when in fact the opposite is true. – Scott Burgess, Continu
18. Excluding Employees From Restructuring Decisions
One of the biggest leadership mistakes is treating AI transformation as a technology journey rather than as a people journey. No matter how compelling the business case may be, leaders must engage employees early, communicate openly and empower them to participate in the change. For example, when automating a function, leaders could have employees participate in the change process, identifying where AI can augment their capabilities so they are not casualties of it. – Babajide Ojuola, International Energy Services Limited