As AI adoption accelerates, some workers will naturally feel apprehensive. Harvard Business Review found trust in employer-provided generative AI fell by 31% between May and July 2025, despite rising top-down pressure to leverage the technology and boost productivity. This shows a clear gap between leaders’ priorities, and those of their teams.

AI tools have the ability to lighten workloads across business units and drive measurable results: for instance, teams using AI as a core part of their sales functions were 65% more likely to increase win rates.

Yet narratives that frame AI squarely in terms of cost-cutting and headcount reduction are holding some employees back from embracing this technology despite the obvious upside.

EMEA General Manager & VP, Gong.

The conversation has shifted from speculation to execution, yet many organizations still struggle to move beyond isolated pilots. Closing that gap is a job leadership must prioritize to realize the technology’s potential.

AI initiatives do not fail because the tools themselves are weak or ineffective, but instead because employees do not trust how the technology is being introduced, what new tools mean for their role, or whether they will still have a place once they are embedded.

The real challenge is helping teams overcome their AI apprehension by building their fluency, introducing clear guardrails, carving out time for practical training, and spotlighting use cases that make the solution feel controllable and purposeful, rather than opaque and threatening.

Putting trust and fluency at the forefront

AI is not going to take an employee’s job, but another human who uses it efficiently, and to their own advantage, will.

It will give rise to entirely new roles (prompt engineers, AI ethics officers, AI maintenance specialists) just as digital transformation created functions that barely existed a decade ago. When I worked at a global social media company a decade ago, "social selling" felt abstract. Now it is mainstream, and we are on the same trajectory with AI, only faster.

When leaders talk about AI purely in terms of doing more with less, employees hear threat, not opportunity. People are far less likely to trust AI if they do not trust leadership's intentions behind it. Reassurance cannot come from policy alone, it has to come from behavior.

That starts with a considered view towards building fluency among teams. This is where leadership is responsible for showing employees how AI works in their own roles. Where employees don’t have a clear understanding of how and where they can integrate AI into their workflows, it’s only natural that they will fill this gap with doubt.

Adoption sticks when leaders provide clear guardrails, responsible training and practical examples so AI feels like a technology that amplifies their skills, rather than a threat.

Redesigning roles, not just adding tools

AI adoption cannot succeed if users' roles don’t evolve at the same pace. If people are operating differently to maximize the gains of AI, their individual job scopes can’t be static.

The most effective leaders are proactively redesigning responsibilities to remove low-value drudgery and focus their teams on interpretation, creativity and judgement.

This looks different depending on the business function. In a customer success team, the role must evolve beyond reactive post-sales support, because that wastes the additional business intelligence that AI can deliver. Leaders must recognize that CSMs can do so much more when aided by AI, and reshape their roles so they act as strategic revenue architects.

Using AI to build expertise on specific buyer personas, they can ask sharper, more consultative questions to anticipate their needs and solve problems before a customer realizes they exist. That is a genuine restructuring of the role, not a superficial rebrand.

On the sales side, AI is transforming how managers coach. Rather than sitting beside a rep on a call and writing notes (with all the unconscious bias that can bring), managers can now review an AI-generated brief, apply data-driven scorecards and identify precisely where individuals need support.

They can surface the sales calls where challenging situations were handled best and use real examples as training material. They can see which teams are winning and why, and where processes are breaking down. Coaching stops being as instinctual and subjective and becomes more rooted in data and real outcomes.

What true AI leadership looks like here

While employees might be apprehensive about the implications of AI on their roles, leaders are ultimately being judged on the results their teams drive and moving forward, AI will be a significant multiplier. It’s no longer a question of whether AI can deliver, so the challenge is taking workers on the journey towards understanding how AI will amplify their skills, not replace them.

Encouraging AI adoption in a way that drives real outcomes is one of leaders’ most urgent priorities. Employees do not move from apprehension to agency simply because they are told AI matters. They do it when leaders make the technology understandable, useful and clearly aligned to supporting their roles.

That means demonstrating trust and reshaping how people work, not just demanding that people urgently learn how new tools work.

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