How Creative Leaders Outperform Technical Ones in AI Adoption

There is a counterintuitive pattern that Joel Salinas notices consistently when working with executives on AI strategy: the leaders who make the best AI decisions are often not the ones who know the most about AI.

The leaders who struggle most are frequently the ones who have done the most technical homework. They have read every AI newsletter, attended every conference, completed every relevant online course. They can discuss model architecture and token limits and retrieval-augmented generation. And yet when it is time to decide which workflows to transform, which tools to trust, and how to lead their teams through the change, they get stuck.

The leaders who move confidently through those decisions often have a different profile. They read broadly — not narrowly. They think in analogies. They borrow frameworks from fields completely unrelated to AI. They ask strange questions that turn out to be the right questions. In short, they are creative. And that turns out to matter more than technical depth when it comes to leading AI adoption effectively.

What Creativity Actually Means in an AI Leadership Context

Before going further, let me be clear about what creativity is not. It is not a personality type. It is not artistry, and it is not the exclusive domain of people who describe themselves as "right-brained." In the context of leading AI strategy, creativity has a precise meaning: it is the ability to make connections that are not obvious and to ask questions that have not already been asked.

This is also the definition of what AI cannot do. Large language models are extraordinarily capable pattern-matchers. They identify and recombine patterns from their training data with stunning efficiency. What they cannot do is step outside the space of known patterns to see a problem in a genuinely new way. They cannot ask a question that has never been asked before, because they have no independent reason to ask it. That capacity — genuine novelty — remains human.

Creativity in AI leadership shows up in specific, practical ways. It is the executive who looks at a failing customer onboarding process and asks what a great hotel concierge would do — and then builds an AI-powered workflow based on that analogy. It is the nonprofit director who borrows a triage framework from emergency medicine to decide which AI projects to prioritize. It is the product leader who applies a lesson from a biography of an architect — about the relationship between constraint and innovation — to their AI tool evaluation process.

None of those insights came from reading more about AI. They came from thinking broadly enough that the right connection could form.

The Paradox at the Heart of AI Leadership

Here is a finding that surprises almost everyone I share it with: leaders who read less about AI but more broadly often make better AI decisions than leaders who read only about AI.

This is not an argument against understanding AI. You need a working grasp of what AI can and cannot do, the risk landscape, and the economics of implementation. But beyond a certain threshold, reading more AI content produces diminishing returns — and real costs. It narrows your frame of reference. It trains you to think about AI in the terms the AI industry uses to talk about itself. And it crowds out the reading that would actually make you a more creative decision-maker.

The leaders I have seen make the most creative AI strategy decisions are disproportionately wide readers. They read history, biography, fiction, philosophy, and books from fields completely unrelated to technology. Not because those things are directly applicable, but because that breadth of exposure generates the raw material for unexpected connections.

As I detail in my guide on building a human-centered AI strategy, the most durable AI strategies are not built on a deep knowledge of the technology. They are built on a deep knowledge of the problem — the human problem — the technology is meant to solve. Creativity is what lets you see the problem clearly enough to recognize the right solution when you encounter it.

Three Practices That Develop Creative Leadership Thinking

Creativity is not fixed. It is a capacity that develops with practice. Here are three specific habits that Joel Salinas recommends to executives who want to build more creative thinking into their AI leadership.

1. Commit to a cross-domain reading habit: one book outside your field every month.

This is the single highest-leverage habit I know for building creative thinking capacity. The constraint matters: it has to be outside your field, not adjacent to it. A technology executive reading about product management is not doing cross-domain reading. Reading about Roman military strategy, or the history of jazz improvisation, or how epidemiologists map disease spread — that is cross-domain reading.

The goal is not to find direct applications. You often will not. The goal is to build a broader library of mental models, so when you encounter an AI challenge that does not fit the obvious frameworks, you have more material to draw from. Leaders who do this consistently report that their best strategic ideas come from unexpected places — and they are right. That is how creative thinking works.

2. Use the "completely different industry" exercise before major AI decisions.

Before making a significant AI strategy decision — which tool to invest in, how to structure a rollout, how to handle team resistance — ask yourself: what would someone in a completely different industry do here? Pick a specific industry, not a vague one. What would a restaurant operator do? What would a trauma surgeon do? What would a ship captain do?

This exercise does two things. First, it forces you out of your industry's default assumptions, which are often invisible precisely because they are so embedded. Second, it generates genuinely novel options — approaches you would not have considered if you stayed inside your usual frame of reference. You will not use most of them. But occasionally, one will turn out to be exactly right, and you would never have found it without the exercise.

3. Protect unstructured thinking time every week.

Creative thinking requires cognitive slack. It does not happen in scheduled brainstorming sessions or during back-to-back meetings. It happens in the spaces between — on a walk, in the shower, during a commute without a podcast playing. Most executives have systematically eliminated those spaces from their lives in the name of productivity. This is a strategic error.

If you want to lead AI adoption with genuine creativity, you need to protect at least two to three hours each week that are structurally unstructured. No agenda, no output. Just thinking time. The returns on this investment are non-linear. The insights that come from unstructured reflection often unlock more value than anything you could accomplish in the same number of hours of scheduled work.

Creativity Inside the AI Leadership Triad

In the AI Leadership Triad — Adaptability, Innovation, and Creativity — creativity occupies a specific and essential role. Adaptability is the foundation: the capacity to hold mission steady while evolving methods. Innovation is the engine: the discipline of asking whether your tools and processes are actually making you better at your core work. Creativity is the amplifier: the human capacity that makes adaptability more resourceful and innovation more generative.

Without creativity, adaptability becomes mechanical — leaders change their methods without any imagination about what better methods might look like. Without creativity, innovation becomes incremental — leaders improve what already exists without seeing possibilities that require a conceptual leap. Creativity is what allows the other two skills to reach their full potential.

Joel Salinas spends considerable time in coaching engagements helping executives recognize and develop their creative capacity — not because it is abstract, but because it is consistently the skill that unlocks the others. When an executive learns to see their AI challenges through a broader lens, the strategic decisions that had seemed impossibly complex often become surprisingly clear.

The question worth asking is not "am I creative?" Almost every leader I work with is more creative than they give themselves credit for. The better question is: have I built the habits and the space to let that creativity show up when it matters most?