Building an AI-ready workforce is one of the biggest challenges leaders face as AI moves into everyday work. AI is no longer separate from the work employees do every day. It’s starting to shape how people write, analyze information, serve customers, make decisions, manage time, and solve problems. That puts leaders in a difficult position: they need to help people move faster without creating confusion, fear, or dependence on tools people don’t fully understand.
Employees need to know where AI can help, where it can create risk, and where their own judgment still has to lead. They need guidance that’s clear enough to create confidence and flexible enough to keep pace as the tools keep changing. To better understand how leaders can build an AI-ready workforce, I asked leaders to share their best advice.
Leaders on Building an AI-Ready Workforce
Jamie Bergeson Lyon, Chief Product and Strategy Officer, Lucid Software: The most important thing leaders can do to build an AI-ready workforce is create a shared understanding of how work actually gets done. You can’t automate what your organization can’t explain. Start by bringing critical processes and institutional knowledge out of people’s heads and into a shared source of truth. From there, teams can see where AI can genuinely add value, where processes need to change, and where human judgment still matters. Visual mapping can make those connections clear and give people a common view of how humans and AI should work together. The organizations that thrive as AI evolves will be those that build readiness into the way they operate. That means giving teams the visibility and shared context they need to continually adapt processes, roles, and workflows as new AI capabilities emerge.
Paul Maguire, co-founder and CEO of Knowmadics: Building and growing an AI-ready workforce is becoming more of a leadership challenge, not a hiring one. Leaders are managing a workforce shaped by uncertainty about AI. From recent graduates to experienced executives, people are asking the question: How do I continue to add value in a workplace filled with digital assistants? The greatest risk is not that AI replaces human ingenuity, but that we outsource our innovation to agents we deploy. As workflows become more automated, curiosity, critical thinking, and creativity can retreat. The cybersecurity sector illustrates this well. The global cyber workforce deficit sits at 4.8 million unfilled positions. AI did not eliminate the need for cybersecurity professionals; it raised the bar for what they must know. The organizations that succeed will be those that navigate both the technology transformation and the confidence gap within their workforce. AI will automate routine tasks and compress cycle times, but it cannot manufacture original thought. Our responsibility as leaders is to help people understand where they create value, invest in hands-on learning, and build confidence alongside technical capability. Equip your people to work with AI while fiercely guarding human judgment, creativity, and strategic decision-making. That is where the competitive edge lives.
Janeen Speer, Chief People Officer at Benevity: Provide access to impactful tools and encourage broad experimentation to get people started. Educate employees with drop-in sessions and embed “AI Champions” within each team to offer real-time coaching to solve team challenges. AI readiness is about being resilient, taking risks, and overcoming failure. We encourage our people to get out of their comfort zone and stay open to learning at an accelerated pace. This includes celebrating wins and successes across the team to demonstrate what is possible, as well as highlighting learnings from failures along the way. While AI use is accelerating, human-centric skills, like empathy, creativity, and social intelligence, are becoming even more critical.
Ross Patrick, Chief AI Evangelist at Academy of Art University: AI literacy will soon be assumed, not admired. What will matter is conceptual thinking, taste, and judgment. With the right instruction, AI platforms can produce almost anything, which means the real constraint has moved. The question is no longer whether you can generate an image, write copy, or build a presentation. It is whether you know what to ask for, how to guide the system, and whether you can recognize the right answer when you see it. That is the skill we need to teach now.
Casey George, Chief Revenue Officer at Monday.com: Building an AI-ready workforce starts with getting your own people to view the technology as a direct report that can do real work for them, not just conduct research or summarize meetings. We put that to the test with what we called an agentic week, where the entire company stopped their day jobs and built AI agents. We ended up with over 3,000 of them, and about 40% are still in active use today, which tells you people found real work to hand off. But that kind of experimentation only pays off if you pair it with structure. Each of our departments has an AI leader who acts as a center of excellence, so people aren’t duplicating effort or burning credits rebuilding something someone else already solved. The other shift is in what you hire for. As AI takes over repetitive work, the people you need are the ones who can go deeper: sellers who understand a customer’s business and industry, not just a product pitch, and technical talent who can sit with a customer and build something useful in hours, not weeks. Leaders shouldn’t treat AI readiness as a tooling rollout. It’s a talent and structure problem.
Harold Fields, EdD, Dean of Students at Canisius University: If leaders want to build an AI‑ready workforce, the starting point is recognizing that AI doesn’t replace the human skills that matter. It makes them more important. As AI takes on routine tasks, people will stand out for their judgment, empathy, ability to work across differences, and capacity to build trust, the very human qualities that keep people meaningfully in the loop with AI, a dynamic Ethan Mollick explores in his book Co-Intelligence. My advice is to create environments where those skills are practiced consistently. In higher education, we’ve learned that students grow when they have structured opportunities to reflect, engage in dialogue, and understand perspectives different from their own. When those experiences are built into the student journey, they strengthen the interpersonal and decision‑making skills employers tell us they need. We’re already seeing results: we’ve embedded constructive dialogue and reflection intentionally and progressively in our curriculum and co-curricular experiences, and a strong majority of our first-year students in a recent cohort report applying these skills in their personal or professional lives. An AI‑ready workforce isn’t just technically fluent. It’s made up of people who can think clearly, communicate well, and lead others through change.
Brent Gordon, President of Elsevier’s Global Healthcare Education: Leaders building an AI-ready workforce should focus on developing judgment, not just technical skills. As AI becomes more integrated into daily work, employees need to understand how to use these tools responsibly, evaluate outputs critically, and recognize when human expertise should guide decision-making. In healthcare, future clinicians will enter workplaces where AI supports everything from administrative support and training to clinical decision-making. They need experience working with credible, evidence-based AI tools that help them ask better questions, assess information carefully, and understand where answers come from. Leaders and organizations can prepare for this by investing in AI literacy and enterprise AI tools, creating opportunities for hands-on learning, and establishing clear expectations around transparency, accountability, and responsible use. The goal is to build a workforce with the confidence and judgment to use AI effectively while maintaining the trust, accuracy, and human oversight that high-quality healthcare depends on. The organizations that invest in these capabilities today will be better positioned to adapt as AI continues to evolve.
Punsri Abeywickrema, founder and CEO of Cloud of Goods: AI is evolving faster than almost any workplace transformation we have experienced. As a result, an AI-ready workforce is not simply a team that knows how to use today’s AI tools. It is a team that is curious, adaptable, and motivated to keep learning as the technology changes. Leaders should build a culture that encourages innovation, continuous learning, responsible experimentation, and learning from failure. They should recruit people who demonstrate adaptability and then provide them with the tools, training, and time needed to develop their AI capabilities. The goal should not be to replace human talent with technology. It should be to empower people to use AI to think more broadly, work more effectively, and create greater value.
Marge Rizzo, Senior Director of Learning Excellence, Year Up United: Leaders need to focus on investing in their people so they can build the confidence and skills to use AI effectively. That means creating a culture where continuous learning is expected, and employees at every level have access to training to learn how to use AI properly and effectively. It also means strengthening durable skills like adaptability, critical thinking, and communication, which are increasingly valuable in AI-enabled workplaces where human judgment matters most.
Sarah Morgenthau, CEO of the “I Have A Dream” Foundation: Leaders should start by looking closely at the jobs where young people get their first real experience. Many of the tasks that once helped people learn how an organization works can now be done much faster with AI. That creates tremendous opportunity, but it also means employers need to think differently about how people develop judgment, confidence, and experience early in their careers. For young people, the answer cannot be to wait until they are hired to figure this out. They need earlier exposure to how work is changing, what employers value, and how to use AI without losing the ability to think critically, communicate clearly, and make sound decisions. At the “I Have A Dream” Foundation, we see the impact when employers create meaningful opportunities to learn through internships, project-based work, mentorship, and time with experienced professionals who can explain not only what they do, but why they do it. The organizations that succeed in the AI-economy won’t simply adopt better technology. They’ll become better teachers.
Janet Russell Norton, Chief People Officer of the Center for Creative Leadership: The best advice for building an AI-ready workforce is to begin with business strategy and the work that must be accomplished, not with the technology itself. The fundamental question is not, “How do we use AI?” but rather, “Where can AI help us execute our strategy, create greater value, and improve the work people do?” Start by identifying the work that creates the greatest value for customers and differentiates the organization. Then examine where AI can improve decision-making, connect disparate data sources, accelerate analysis, automate repetitive activity, or create entirely new ways of working. One of AI’s greatest opportunities is to reduce time spent on low-value administrative and routine tasks so people can devote more attention to innovation, customer relationships, judgment, problem-solving, and other higher-value work. Next, move from thinking primarily about jobs to thinking about capabilities. Rather than simply asking how many analysts, engineers, or managers will be needed, leaders should ask: “What human and technological capabilities will we need over the next several years to execute our strategy?” This skills-based mindset creates a more agile and adaptable workforce. Broad AI literacy is also essential. Employees do not all need to become sophisticated AI users, but they should understand what AI can and cannot do, how to use it responsibly, and its limitations related to accuracy, privacy, bias, ethics, and accountability. AI adoption is therefore as much a cultural and leadership transformation as a technological one. Most importantly, resist framing AI and human capability as competing alternatives. They are interdependent. Organizations should invest as intentionally in human capabilities – critical thinking, judgment, curiosity, creativity, empathy, communication, influence, collaboration, and ethical leadership – as they invest in technology. Maintaining a “human in the AI loop” protects accountability while combining human ingenuity with AI capability. Ultimately, an AI-ready workforce is not one that simply uses more technology. It is one that redesigns work intelligently, continually builds future-critical capabilities, and enables people and AI to create greater value together than either could create alone. Done well, the result can be a workforce that is not only more productive and adaptive, but also wiser and more deeply human.
Roger Gomez, Campus President of UTI-Lisle: The key to building an AI-ready workforce isn’t just investing in technology but investing in people. Organizations that succeed will be those that teach employees how to work alongside AI, combining human judgment, technical expertise, and adaptability with the power of emerging technologies.
Brian Peterson, co-founder and CTO of Dialpad: Don’t try to boil the ocean all at once. Start small. Use a specific project or small team as a starting point. Let them try everything and learn from it before you distract the entire company with the wrong training. Then, when they are successful or unsuccessful, use them to learn from and to train the rest of the company or the next team. Have them talk about their experiences and learnings to the company. Document what they’ve done for all to see. Measurement is the absolute most critical thing. There is zero point in implementing AI if you can’t track if it’s working. In order to know it’s working, you need the before and after because AI is not good at telling you how much impact it had. Before adding AI, know your run rate without AI. How fast are you? How good are you? How long would this take normally without AI based on historic data? Then, when you add AI, you can look at the before-and-after difference. If you don’t have metrics before, then after AI you will have no idea if it helped or not. That is the most common problem I see companies having. They throw AI into the mix and then later the execs ask how much better/faster are we now, and teams say “I’m not sure.” The only way to know that is to have the data before AI was introduced. Dedicate people to AI Transformation. It’s too hard to get every person amazing at AI all at once. Assign people to be full-time AI transformation engineers. Dedicate them to the different parts of your business aligned to the executives. Where do you get these AI Transformation Engineers? Definitely not from hiring people with previous experience because all of this is too new. The best way to get them is to train them. Take technical people inside your business or anyone who seems AI proficient already and make them AI transformation engineers. Or, hire people right out of university who are interested. They’ve already been using AI to cheat anyway, so they’re already well versed in it.
Daniel Burrus, CEO of Burrus Research: It’s important to start with an Anticipatory Mindset versus a Reactionary Mindset because AI is moving so fast that, if you are reactionary, you will always be behind the AI curve. Next, develop your AI skill set, for example, how to apply critical thinking to verifying AI output and knowing how to write good prompts. Then look at your AI toolset and make sure the AI tools used are best suited to augment the role of the user. Give employees clear use cases, safe rules, and hands-on time with the tools tied to their actual jobs. Training should focus on real work, such as improving research, reducing repetitive tasks, speeding up analysis, or creating stronger first drafts. Then build a culture of continuous learning, where teams regularly identify tasks AI can assist with, skills that need to be added, and new work humans should own because it requires judgment, trust, creativity, or understanding. Leaders also need to model the behavior themselves. When employees see leaders using AI thoughtfully, asking better questions, and setting clear boundaries, adoption becomes practical instead of abstract.
David Jenks, Provost of Middle Georgia State University: AI is changing the nature of work in real time, and teaching a specific skillset years in advance is problematic because it may not be a skillset that is necessary a few years down the road. Instead, leaders need to focus on building the classic skills required to include not just problem-solving, but problem identification that will enhance the use of AI in the workplace by inserting the natural capacity of being human into processes, workflows, and outcomes that benefit us all. AI will continue to evolve, and we need to evolve with it and use it as a tool for improvement.
Dan Garrison, Chief AI Officer at Slalom: The most common misstep I see is treating AI as purely a technology problem. Leaders acquire licenses, deploy tools, and expect employees to self-serve their way to productivity. That rarely works, and when it falls short, it’s typically a change management failure. Building an AI-ready workforce is a human transformation effort that has to be owned and championed from the top. The second thing worth examining is the assumption that one size fits all. A technologist, a creative, a salesperson, and a finance analyst all need different tools and training. At the same time, something interesting is happening: AI is blurring the lines between these roles in an exciting way. For example, creatives are bringing technology solutions to life without needing an engineering degree, and technologists are showing up to business conversations with instincts they never had before. The more important investment is in building a workforce that can think critically alongside AI rather than become dependent on it. Curiosity, problem-solving, and business-oriented creativity are critical skills for the future of work. Hire and develop for those qualities and keep sharpening them. Finally, don’t be spooked by the idea that some roles will look unrecognizable in five years. They won’t disappear. They will evolve, often into something more valuable. The organizations that thrive will be the ones that lean into that evolution rather than resist it, and that deliberately shape what it looks like for their people.
Bryce Carpenter, Chief Strategy Officer of Conexus: Investments in equipment and investments in your workforce can’t be separate conversations anymore. Automation doesn’t have to mean job loss; it means retraining people into higher-value roles. It also helps not to wait for perfect confidence in your ROI before making a first move. The companies that are furthest along are the ones who let people experiment with a small, low-risk use case first and built from there, rather than waiting for a fully worked-out business case. Sometimes the best place to start is just asking.
Frequently Asked Questions About Building an AI-Ready Workforce
What is an AI-ready workforce?
An AI-ready workforce is a workforce that can use AI tools thoughtfully, effectively, and responsibly. It means employees have the skills, confidence, judgment, and support they need to understand where AI can help their work and where human insight is still essential. Understanding how AI is changing the workforce is essential to building that readiness.
Why is building an AI-ready workforce important?
Building an AI-ready workforce is important because AI is becoming part of everyday work across roles and industries. Leaders who ignore that shift risk leaving employees confused, underprepared, or dependent on tools they don’t fully understand.
How can leaders help employees become more comfortable with AI?
Leaders can help employees become more comfortable with AI by making learning practical, clear, and safe. Employees need examples tied to their actual work, permission to ask basic questions, and guidance on how to use AI without replacing their own judgment. McKinsey research found that 48% of employees believe formal generative AI training would increase their daily use, while 22% say they currently receive little or no organizational support.
What skills matter most for an AI-ready workforce?
The most important skills for an AI-ready workforce include adaptability, curiosity, communication, critical thinking, judgment, data literacy, and a willingness to keep learning. Technical skills matter, but they are most valuable when paired with strong human skills.
What mistakes do leaders make when introducing AI at work?
One common mistake is treating AI adoption as a software rollout instead of a people challenge. A clear AI adoption framework helps leaders address the people, process, and strategy questions that determine whether adoption succeeds. Another mistake is assuming employees will figure it out on their own. Without clear expectations, training, and guardrails, AI can create more confusion than progress.
How can organizations build trust around AI?
Organizations can build trust around AI by being clear about how AI will be used, what it won’t be used for, and how decisions will be made. Trust grows when employees understand the purpose, the boundaries, and the role they still play in the work.
