Organizations are racing to put AI tools in front of employees. Universities are weaving AI into the classroom. Workforce programs are teaching people how to prompt, summarize, and generate. All of that activity assumes the hard part is adoption. Stanford’s 2025 AI Index found that 78% of organizations reported using AI in 2024, up from 55% the year before, so the adoption curve is real and it’s fast.
Alan Boyer, a former Fortune 500 executive who now designs AI-readiness curricula for universities and organizations through his firm iThrive Learning Architects, doesn’t see adoption as the finish line. Teaching people to use AI isn’t the same as preparing them to work with it. Knowing how to open a platform and get an answer doesn’t tell you whether that answer is right, what to do with it, or who’s accountable when it’s wrong. Those questions determine whether AI actually strengthens an organization’s work or just accelerates its mistakes, and the difference comes down to whether people are equipped to catch the second case before it ships.
Alan has seen this from various perspectives, leading teams as an executive, teaching the students those companies would eventually hire, and working with universities to redesign the curricula meant to prepare them. His new book, Power Skills Are Not Enough, is a reflection of what he’s learned across those years.
The title is a deliberate provocation. Alan isn’t proposing that communication, collaboration, and adaptability stopped mattering. He’s arguing they were never going to be enough on their own, and AI is the thing making that shortfall impossible to ignore.
As execution gets faster and cheaper, the value of a person shifts upward, from doing the work to validating it, from participating in a process to interpreting what it means and deciding what happens next. Picture a team that used to spend two days building a market analysis by hand. AI can produce a draft of that same analysis in minutes now. That two-day window simply moved to a different point in the process. The team still has to question whether the AI pulled the right comparables, whether one stray data point skewed the conclusion, and whether the recommendation actually fits the client’s situation or just sounds confident. That’s the shift Alan is describing.
Preparation Has to Start Earlier and Never Stop
Alan’s clearest examples come from higher education, where he evaluates curricula and helps design interdisciplinary programs. He’s adamant in his belief that a university can’t bolt AI tools onto an existing course and call the graduate workforce ready. The question every program has to answer is what a graduate needs to be capable of doing once AI can already perform much of the execution that used to define entry-level work. That reframes the target. Judgment and discernment, systems thinking, cultural fluency, and the ability to work alongside intelligent systems become the outcomes worth designing toward, not electives bolted on around the edges.
Alan is just as adamant that readiness can’t stop with college students. Businesses already employ millions of people whose jobs are changing right now, and they can’t wait years for a new generation of AI-ready graduates to arrive. Handing current employees a training library and hoping they figure it out isn’t a strategy either. One of the first questions Alan asks when he sits down with a learning and development team is whether their plan is actually tied to what the organization is trying to accomplish. If it isn’t, the training becomes an activity instead of a bridge between where the organization is and where it needs to go.
The data supports what he’s seeing. The World Economic Forum estimates that 39% of workers’ existing skills will be transformed or become outdated by 2030, and that 59 out of every 100 workers will need training to keep up. Skills gaps are already the top barrier to transformation for 63% of employers, which is exactly the gap Alan is describing when he talks about training that isn’t tied to strategy. Workforce readiness has stopped being a milestone. It’s become continuous, for graduates and veterans alike.
The Responsibility Sits With Leadership
This is where Alan’s argument sharpens into something leaders can’t delegate to IT or hand off to L&D. If leadership introduces the systems that change how people work, leadership also takes on the responsibility for preparing people to meet the expectations those systems create.
Elevating people doesn’t mean a motivational speech about becoming a lifelong learner. It means building the actual opportunities for people to develop judgment and discernment, confidence, and the higher-level capabilities the organization will increasingly need from them. That’s a design problem, not a slogan.
And it doesn’t stop at individual development. In our conversation, I pushed Alan on an obvious tension: what happens when an organization teaches people to be curious, proactive, and willing to challenge assumptions, and then drops them back into silos, politics, and functional boundaries built for a different era? His answer was that the better-performing organizations he’s seen bring functions together around problems instead of routing every issue into its own department to solve alone. You can build people who are ready for more. If the structure around them wasn’t built for that, the investment stalls. Developing capable people and leaving the organization exactly as it was are two different projects, and only doing the first one won’t get you the outcome you’re after.
Where the Advantage Actually Comes From
The workforce isn’t competing against AI. It’s learning to operate differently because AI exists. Alan describes the people who will pull ahead as those who can retain their own judgment and discernment while working with intelligent systems, people who can think clearly, interpret complexity, and guide those systems responsibly rather than defer to them by default.
The labor market is already pricing that in. PwC’s research on AI-exposed roles found that the new tasks being added to those jobs are two and a half times more likely to depend on empathy, judgment, and creativity than the tasks they’re replacing. As AI absorbs the routine parts of a role, what’s left over is precisely the human layer Alan is describing.
That becomes the competitive advantage, because access to the technology won’t stay a differentiator for long. Most organizations will have it. What separates them will be what their people are capable of doing with it, and whether leadership built the conditions for that capability to show up.
Alan’s pathway traces a continuum: student, graduate, employee, leader. We’ve traditionally treated those as separate stages, handed off between separate institutions with separate responsibilities. AI is making that division harder to defend. Universities have to prepare graduates for workplaces that will keep changing after they arrive. Employers have to keep developing people as the work itself shifts under them. And leaders have to build organizations where higher levels of human capability aren’t just expected, but actually developed and actually used.
As AI accelerates work, leaders have to elevate humans. The future of any organization will depend as much on how deliberately it develops the people working alongside AI as on how quickly it adopts the technology itself.
Sources Alan Boyer, Power Skills Are Not Enough: The 7 Communication Superpowers That AI Cannot Automate Stanford HAI, The 2025 AI Index Report World Economic Forum, The Future of Jobs Report 2025 PwC, 2026 Global AI Jobs Barometer
