AI adoption in the workplace has become one of the most important leadership challenges organizations face. Many organizations have given employees access to AI tools. Access alone doesn’t drive AI adoption in the workplace. Employees still need to understand how to use the tools, when to trust them, and where they can create meaningful value.
The gap between availability and adoption remains significant. Gallup found that even in organizations where AI tools are available, 67% of leaders use AI frequently compared with 46% of individual contributors. Employees are more likely to adopt AI when the tools fit their work, demonstrate clear value, and have visible support from managers. To better understand what drives AI adoption in the workplace, I asked leaders to share their best advice for building an AI-ready workforce.
Leaders on AI Adoption in the Workplace
Jeff McMillan, former Chief Analytics and Data Officer and Head of Firmwide Artificial Intelligence at Morgan Stanley: AI transformation is fundamentally a human and cultural evolution, not just a software rollout. Most organizations treat it as a technology play, and that is exactly why they miss so much of what matters. To make the shift work, leaders should focus on four things. First, set clear goals for the AI program and communicate them across the organization. Second, invest in education that meets the specific needs of each group: the board, the C-suite, managers, the teams building AI, and the broader employee base. One curriculum does not serve all of them. Third, drive adoption by embedding these tools in the daily work of your people rather than sending them off to a separate platform. Fourth, hold people accountable for business impact. Most companies track tool and token usage, but that activity does not translate into outcomes. Measurement is what connects effort to results. Long-term capability is created when a workforce has both the data governance foundation and the cultural agility to keep evolving alongside the technology.
Marshall Heilman, CEO of DTEX: Stop treating AI readiness as a training problem and start treating it as a trust problem. The leaders I respect most aren’t the ones rolling out the flashiest copilots first. They’re the ones who can actually understand what normal behavior looks like for an AI agent, and who’s accountable if it goes sideways. Every AI tool you deploy is effectively a new employee with credentials and access, so give it the same onboarding rigor you’d give a new hire: least-privilege permissions, a clear owner, and real oversight from day one. Too many organizations are eager to show board members they’re ‘doing AI’ that they skip the boring part, which is governance, and that’s exactly where things break. My advice is simple: build the guardrails before you build the hype, because a workforce that’s fast but unaccountable isn’t actually ready for anything. Get the fundamentals right, and the innovation takes care of itself.
Aaron Mitchell, CEO at HeroDevs: Start by being clear about the business outcomes you’re trying to achieve, not by rolling out a list of prescribed AI use cases. If employees understand the outcome, they’re often better positioned to identify where AI can make the biggest difference in their own work. It’s also important to give people the space to experiment. AI hackathons or dedicated company-wide innovation time are great ways to build excitement and encourage buy-in because employees get to explore what’s possible and build solutions to the problems they encounter every day. The best AI ideas rarely come from the top down. Finally, put clear guardrails in place. AI should accelerate execution and reduce repetitive work, but it shouldn’t become a substitute for strategic thinking or judgment. The goal is to help people think bigger and move faster, not outsource the decisions that matter most.
Sukhinder Singh Cassidy, CEO of Xero: There are two pieces of advice that come to mind for this era. First, if you want to be building an AI-ready workforce, be on the tools yourself. This is a “builder’s era” – where low code/no code solutions can help individuals, teams, and process owners solve their own problems with automation and get access to intelligence faster by being hands-on. It’s hard to do that if you don’t bother to understand what is possible and role model that usage, reimagining your own work the way you’re asking your teams to do. The second piece of advice I have centers around “accepting” the messiness that comes with figuring out the new ways of work. You may try top-down mandates, bottom-up citizen builders, AI rubrics, and frameworks. Don’t be afraid to copy what others are doing and try several different methods to crack the code of what works for certain work processes or teams. At the same time, you need to enable your teams with common infrastructure, so they know what to build on. But even that may shift as you go. Flexibility is the name of the game in figuring out how to enable different parts of your workforce to start, and then refine/adapt as you go. What’s in use today may be old tomorrow, but the goal is to build fluency and adaptability into the workforce while finding efficiency in the way you enable tools and workflow building.
Dr. Anthony Lee, President and CEO of Westcliff University: My best advice is to treat AI readiness as a people strategy, not simply a technology strategy. Buying the latest tools will not create an AI-ready workforce if employees are uncertain about how to use them, afraid to experiment, or unable to evaluate their output. Leaders should make AI literacy a shared organizational capability rather than confining it to technical departments. Employees at every level need opportunities to use AI in practical ways, understand its limitations, and learn the responsibilities that accompany it. An AI-ready workforce is not one in which everyone becomes a technologist. It is one in which everyone becomes more capable because of technology. At Westcliff University, we are integrating AI into teaching, learning, and professional development while continuing to emphasize judgment, ethics, communication, and critical thinking. The organizations that gain the greatest advantage from AI will not necessarily be those that adopt it first. They will be those that prepare their people to use it wisely.
Sam Kidd, founder and CEO of LawVu: I think one of the biggest mistakes organizations are making is starting with AI instead of starting with the outcome they’re trying to achieve. Too many conversations begin with, “Where can we use AI?” The better question is, “What problem are we trying to solve?” What process are we trying to improve? What outcome are we trying to deliver? Sometimes AI will be the answer. Sometimes a process change or a simple piece of automation will get you there faster and cheaper. Once you’ve identified the right opportunities, the focus shifts to your people. AI can generate ideas, analyse information, and complete tasks, but people still need to apply judgment, verify the output, and make the final decisions. Accountability doesn’t disappear because AI was involved. More broadly, I think leaders need to create a culture where people feel comfortable experimenting. Nobody has all the answers yet, and that’s okay. The organizations that will get the most value from AI won’t necessarily be the ones with the biggest budgets. They’ll be the ones that encourage curiosity, learn quickly, and stay focused on solving real business problems rather than adopting technology for its own sake.
Don Sarno, SVP Digital Enterprise Americas of Sonepar: Provide guardrails and governance, while making space for grassroots experimentation. Digital leaders can fall into the trap of focusing too much on the shiny new technology and not on the people. I get it. Our obsession with technology is why we do the job that we do. But leaders shouldn’t treat AI as a rollout of new tools when it’s really a workforce transformation. Employees may resist AI because they don’t know how to effectively use it and are unsure of how it’s going to affect their job. That uncertainty can cause hesitation when you need a spirit of exploration. Grassroots momentum is powerful because AI isn’t just a new tool that answers questions. It’s a whole new way of working. Real adoption happens when employees share how they’re redesigning workflows and incorporating AI agents into everyday processes. We found the greatest success by creating forums where associates could learn from one another’s experiences and by visibly recognizing the people leading that experimentation and innovation. An AI-ready workforce is built on a foundation of governance and a culture where employees trust the vision, understand how AI helps them solve specific challenges, and have access to both formal training and peer-to-peer exchange.
Jaime Newbery, VP of People & Culture at Pipedrive: Building an AI-ready workforce isn’t primarily a technology challenge. It’s a leadership challenge. Many organizations focus on giving employees access to AI tools, but access alone rarely changes behavior. People need confidence, clear expectations, and permission to experiment. Our own research highlights this challenge. Pipedrive’s The Hidden Cost of Selling report found that 25% of professionals struggle to know when to trust AI’s output, while 23% say their organization hasn’t provided clear guidance on when or how AI should be used. Without that clarity, adoption becomes inconsistent. Leaders need to define where AI creates value, where human judgement remains essential, and what great performance looks like when people and AI work together. Good governance matters, but so does creating psychological safety so people feel comfortable learning in public and sharing what’s working. Just as importantly, invest in your leaders. AI is changing how every team works, and managers need new skills to coach people through that transition. The best leaders won’t be the ones with all the technical answers. They’ll be the ones who help their teams ask better questions, embrace continuous learning, and adapt with confidence. AI should free people from repetitive work so they can spend more time solving problems, building relationships, and making thoughtful decisions. That’s where technology creates its greatest value and where leadership becomes even more important. The future won’t belong to organizations that successfully navigate one transformation. It will belong to organizations that build the capability to keep transforming. As leaders, our role isn’t to have every answer. It’s to create cultures where people have the confidence, curiosity, and courage to keep learning for whatever comes next.
Adam Wachtel, CTO of Click Boarding: An AI-ready team is first about strengthening judgment, while giving your team the room to develop with AI in the loop. Start by identifying a very narrow use case and a clear objective – answering “what do you want this to do?” In a single sentence. Roll that agent out, let the team work with it directly, gather feedback from those using it, ship improvements quickly, and expand from there. AI can’t be mastered through training decks; it’s something that’s adapting in real time, and you have to adapt along with it. One other critical consideration is to understand that AI is an expertise amplifier. Throwing someone who doesn’t understand what the AI is working on lends itself to bad outputs because the one accountable, the one driving the AI, can’t push back when an answer is wrong – or help to identify the root cause.
Neil Wheeldon, Chief Information Officer of PSA BDP: Building an AI-ready workforce is one of the most significant transformational challenges leaders face today. Leaders should start by creating a culture of continuous learning coupled with experimentation around key AI capabilities. As organizations mature in their ability to use AI tools, leaders should focus on integrating capabilities directly into processes and daily work routines where AI becomes indistinguishable from any other tool or function embedded within applications. Approaches to AI, however, do need to be balanced to ensure data, cybersecurity, and IP protections form the basis of any AI implementations within organizations, as well as the approaches to training and testing of use cases.
Stephen Straus, co-founder and CEO of KUNGFU.AI: My best advice: be ambitious with a strong bias toward action and lead with curiosity, transparency, and especially vulnerability. Leaders know AI is changing everything, but most appear to be operating as if it won’t impact their industry structure. Develop an AI strategy with the goal of being the disruptor. Focusing only on automation cedes competitive advantage, which could mean your AI initiatives turn out to be for nothing. But no one has a playbook for this. The leaders who come out ahead are the ones who experiment, learn, and iterate. How to build an AI-ready workforce to achieve all of this? Start with a culture of psychological safety to give your people the space to experiment, fail, and learn, and role model it through your own vulnerability. That’s harder than it sounds. Leaders are used to having the answer, or at least projecting that they do, but that is likely a recipe for failure.
Michael Zimmerman, President of Campus: The biggest mistake is treating AI as another box to check. The technology becomes valuable when it’s applied in context. Leaders should recognize that this isn’t a one-time update. AI is evolving too quickly for that.
Stefan Hirniak, President of Specialist Staffing Group US: Building an AI-ready workforce starts with recognizing that AI is as much a people transformation as it is a technology transformation. Across the engineering, technology, life science, and infrastructure organizations we support, the companies making the most progress with AI aren’t necessarily the ones investing the most in new tools. They’re the ones investing equally in helping their people understand, adopt, and apply those tools effectively. They engage employees early, identify high-impact use cases, and provide the training and confidence needed to turn AI into a productivity advantage. Another common mistake is assuming AI readiness begins and ends with hiring. Bringing in new skills will always be important, but many organizations already have talented engineers, analysts, and technical professionals who understand the business and can be upskilled to work alongside AI. In our experience, the bigger challenge isn’t AI replacing jobs. It’s helping employees understand how AI is changing workflows, decision-making, and productivity across their roles. We’re seeing growing demand for professionals who understand both the work itself and how AI can transform the way that work gets done. Technical expertise remains critical, but so do industry knowledge, adaptability and the ability to apply AI within real business environments. The organizations making the greatest progress are balancing strategic hiring with investments in their existing workforce while planning for the skills they’ll need three, five, and even ten years from now. Leaders also need to think beyond today’s requirements. AI is evolving too quickly to hire for every future need. The most successful organizations are not simply building AI talent; they’re building teams that can continuously learn, adapt, and evolve as workflows and productivity models change. In many cases, the most valuable employees won’t be those who build AI systems themselves, but those who can integrate AI into business processes, improve productivity, and help others work more effectively alongside it. The question is becoming less about which jobs AI will replace and more about which professionals can redesign work to capture the productivity gains AI makes possible. The future of work won’t be defined by AI alone. It will be defined by how effectively leaders prepare their people to adapt, innovate, and create value in an increasingly AI-enabled world.
Geoffrey Toffetti, CEO of Frontline Performance Group: My best advice is to think of AI as a partner, not a substitute. The organisations that succeed will use AI to strengthen human capability, not replace it. Let AI handle repetitive, low-value tasks so people can focus on the moments that require judgement, empathy and human connection. Successful managers of the near future will be great at managing their team and orchestrating AI deployment. The biggest mistake I see is companies adopting AI without a clear strategy. Rather than asking AI to do everything, identify one specific problem and solve it exceptionally well. AI performs best when it’s given a narrowly defined task. If you don’t invest the time to understand the problem and how AI works, you risk poor results, wasted investment, and a loss of confidence in the technology. That’s why leaders need to invest in their own AI education. Don’t rely solely on others to tell you what’s possible; understand the technology yourself so you can make better decisions about where it creates value. The future belongs to organisations that combine AI with human strengths. Use AI as a force multiplier that improves productivity and decision-making, while keeping people at the centre of customer interactions. The businesses that get this balance right will build stronger teams, deliver better experiences and create a lasting competitive advantage.
Frequently Asked Questions About AI Adoption in the Workplace
What is AI adoption in the workplace?
AI adoption in the workplace is the process of integrating artificial intelligence into the way employees actually work. AI adoption in the workplace goes beyond providing access to AI tools. Successful adoption means employees understand where AI can help, use it appropriately, and incorporate it into workflows in ways that improve outcomes.
Why do employees resist AI adoption?
Resistance to AI adoption in the workplace can grow when employees do not understand how AI will affect their roles, do not trust its output, lack clear guidance, or have not seen how it makes their work better. Leaders can strengthen AI adoption in the workplace by explaining why AI is being introduced, establishing clear guardrails, providing opportunities to experiment, and showing employees how the technology can help them solve real problems.
How can leaders increase AI adoption in the workplace?
Leaders can increase AI adoption in the workplace by starting with specific business problems rather than broad mandates. A clear AI adoption framework can help leaders determine where to start, how to build trust, and when to expand adoption. Give employees useful tools, clear guidance, practical opportunities to experiment, and examples of how AI improves real work. Leaders should also use AI themselves and share what they are learning.
What are the biggest barriers to AI adoption?
Common barriers to AI adoption in the workplace include lack of trust, uncertainty about appropriate use, insufficient training, weak leadership support, poorly chosen use cases, and tools that do not fit employees’ actual workflows. Organizations can also undermine adoption when they focus on deploying technology without addressing how people experience the change.
What policies support responsible AI adoption in the workplace?
Responsible AI adoption in the workplace requires clear policies around approved tools, data privacy, security, human review, accountability, and appropriate use. Employees should know what information can be entered into AI systems, which decisions require human oversight, and who is responsible for the final output. Good policies create boundaries without making responsible experimentation unnecessarily difficult.
How should companies train employees to use AI?
Successful AI adoption in the workplace requires training that combines foundational knowledge with hands-on application. Employees need opportunities to use AI on work they already understand, evaluate its output, identify its limitations, and learn from one another. Training is most useful when it continues as the technology and its applications evolve.
How can organizations measure AI adoption?
Organizations can measure AI adoption in the workplace by looking beyond logins or licenses. Useful measures include frequency of use, the number of workflows incorporating AI, employee confidence, time saved, quality improvements, business outcomes, and whether successful applications spread across teams.
