The AI Skills Gap Is a Leadership Problem, Not a Technical One

McKinsey's research estimates that up to 30% of work activities could be automated by 2030. That projection prompted a predictable organizational response across industries: training programs. Workshops on prompt engineering. AI literacy certifications. Vendor-sponsored sessions on using the latest tools. Organizations everywhere are working hard to close what they have defined as a technical skills gap.

The problem is that they have diagnosed the wrong gap.

The organizations struggling most with AI adoption are not struggling because their employees do not know how to use AI tools. Most employees, left to their own devices, figure out the tools quickly enough. They are struggling because their leaders do not know what to automate, how to bring teams through the change effectively, and when the answer to an AI proposal should be no. Those are leadership skills. And you cannot close a leadership gap with a technical training program.

What the Data Actually Shows

Consider two data points side by side. First: McKinsey estimates that 30% of work activities could be automated by 2030. Second: Forrester projected in their 2025 Predictions report that 60% of Fortune 100 companies would appoint dedicated heads of AI governance by the end of 2026.

If AI were primarily a technical challenge, the response to a 30% automation horizon would be technical training. But the actual response from the world's largest organizations is to appoint senior leaders with governance authority over AI. That is not a technical signal. It is a leadership signal. It says that the most sophisticated organizations in the world have concluded that the constraint on effective AI adoption is not technical capability — it is leadership judgment.

Joel Salinas has worked with leaders across sectors who have invested heavily in technical AI training programs and found themselves, a year later, with teams that can use tools but still lack strategic direction about when and how to deploy them. The tools are not the problem. The leadership framework is.

The Three Questions Technical Training Cannot Answer

When it comes to AI adoption, there are three questions that determine whether the investment generates real value. None of them are technical questions.

What should we automate? This is a strategic judgment call, not a technical one. It requires understanding which workflows are bottlenecks to your core mission, which human activities become more valuable when AI handles the routine work, and which aspects of your operation should deliberately not be automated because the human element is part of the value. A team that knows how to use AI tools but lacks leadership direction on what to automate will automate what is easiest, not what matters most.

How do we bring our team along? AI adoption fails in organizations not because the technology does not work, but because the people do not adopt it. And people do not adopt technology when they feel threatened by it, confused about it, or unconvinced that leadership has thought through the consequences for them. Managing that transition requires adaptive leadership, clear communication, and the ability to hold team members' concerns seriously while still moving the organization forward. No amount of technical training prepares leaders for those conversations.

When should the answer be no? This may be the most undervalued leadership skill in the AI context. Not every AI proposal is a good one. Not every use case that looks impressive on a demo will serve your mission in practice. Not every vendor pitch deserves a yes. The ability to evaluate AI initiatives critically — to ask whether this genuinely improves our core work, whether our team will actually use it, whether the ROI story holds up — is a judgment capacity that develops through leadership experience, not technical training.

The AI Leadership Triad as the Answer

The framework Joel Salinas uses to address the real AI skills gap is the AI Leadership Triad — three leadership capacities that, developed together, equip leaders to navigate AI adoption with judgment and clarity. Those three capacities are Adaptability, Innovation, and Creativity.

Adaptability is the foundation. It is the ability to hold mission clarity steady while continuously evolving the methods through which you pursue it. Leaders with high adaptability can manage AI disruption without either resisting change or abandoning structure. They keep their teams grounded in what matters while remaining genuinely open to how the work gets done.

Innovation is the discipline of asking whether your AI initiatives are actually making your organization better at its core work, as opposed to appearing more innovative. Leaders with strong innovation judgment cut through the noise of vendor pitches and competitive anxiety to focus on the AI investments with genuine strategic value. They answer the "what should we automate" question with rigor instead of enthusiasm.

Creativity is the capacity that makes everything else more effective. It is not artistry — it is the ability to see connections that are not obvious, ask questions AI cannot ask, and bring fresh perspective to problems that seem to have no good solutions. Leaders with creative thinking capacity are better at all three of the questions above: they see automation opportunities others miss, they find unexpected approaches to managing team transitions, and they evaluate AI proposals from angles that reveal flaws the obvious analysis misses.

These three skills together form the actual answer to the AI skills gap. And as Joel Salinas has observed across dozens of coaching engagements, they are learnable. They develop through deliberate practice, the right coaching relationships, and organizational structures that reinforce them.

Three Shifts Organizations Should Make

Understanding the problem is not enough. Here are three specific organizational changes that close the leadership skills gap rather than the technical one.

Shift 1: Train leaders on AI judgment, not just AI tools.

This means moving beyond "how to use ChatGPT" workshops and investing in programs that develop strategic thinking about AI. What problems are worth solving with AI? How do you evaluate the real ROI of an AI investment versus the projected ROI? How do you manage a team through AI-driven workflow change? These are the questions that matter for organizational leaders, and they require a different kind of training than technical literacy.

Specifically, this means leadership development programs that include AI strategy components, executive coaching engagements focused on AI decision-making, and peer learning structures where senior leaders can work through real AI decisions together. The goal is judgment development, not tool proficiency.

Shift 2: Measure adaptability and creativity alongside technical competency.

Organizations measure what they value. If your performance evaluation system measures technical AI proficiency but not adaptability, not creative problem-solving capacity, and not the ability to lead teams through change, you are signaling that the leadership skills do not matter. Over time, that signal shapes behavior.

Building adaptability and creativity into leadership competency frameworks — and then actually evaluating against them — changes what leaders invest in developing. It also changes who gets recognized, promoted, and positioned as role models for AI leadership. As the AI Leadership Triad framework makes clear, these are not soft skills. They are strategic capacities with measurable behavioral indicators. They can be assessed, developed, and tracked over time.

Shift 3: Build cross-functional AI teams led by business leaders, not just engineers.

The composition of AI teams inside organizations reveals a great deal about how those organizations think about the problem. When AI teams are staffed and led primarily by engineers and data scientists — with business leaders consulted occasionally — the team optimizes for technical sophistication rather than business impact. The solutions it produces may be technically impressive but strategically misaligned.

Effective AI adoption requires cross-functional teams where business leaders are not peripheral stakeholders but active decision-makers. The engineer's role is to assess what is technically feasible. The business leader's role is to determine what is strategically valuable and operationally viable. Both are essential. But the strategic direction must come from the business side, and that requires leaders who have developed the AI judgment to fill that role effectively.

Why This Matters Now

The Forrester data about AI governance appointments is not a prediction about a distant future. It is describing what is happening right now, in 2026, across the largest organizations in the world. The structural shift from AI as a technical project to AI as a leadership responsibility is already underway.

Organizations that continue to treat AI adoption as primarily a technical training problem will find themselves in an increasingly uncomfortable position. Their teams will have tool proficiency without strategic direction. Their AI investments will produce mixed results that are hard to interpret and hard to improve. And their leaders will be increasingly ill-equipped for the governance, judgment, and team leadership demands that AI adoption places on them.

Joel Salinas works directly with executives navigating this shift — helping them build the adaptability, innovation discipline, and creative thinking capacity that the moment requires. The work is specific, practical, and grounded in the real decisions these leaders are facing. As detailed in the AI coaching for executives guide, the goal of that work is not to make leaders dependent on a coach. It is to build the internal capacity that lets them lead AI adoption confidently on their own.

The AI skills gap is real. But the organizations that will close it are the ones honest enough to recognize that the gap is in their leadership — and invest accordingly.