What Most AI Training Programs Get Wrong About Leadership

Here is the paradox that Joel Salinas encounters in almost every enterprise AI training conversation: organizations are spending more on AI skill development than at any point in history, while simultaneously seeing AI adoption rates and ROI that suggest something fundamental is missing from the training itself.

McKinsey projects that roughly 30% of jobs will see significant automation by 2030. The industry response to that projection has been a surge in AI training investment — prompt engineering workshops, tool certification programs, vendor-led onboarding sessions. The assumption is that if employees learn to use AI tools proficiently, organizations will be AI-capable. That assumption is wrong, and the gap between what is being taught and what actually determines success is costing organizations enormous amounts of time and money.

The leaders who successfully navigate AI transformation are not the best prompt engineers. They are the most adaptable in the face of rapid change. The most disciplined about evaluating whether a new approach serves the actual mission. The most capable of making unexpected connections across domains. In other words: the most skilled in exactly the capabilities that the AI Leadership Triad identifies — and that most training programs do not address at all.

The Proficiency Trap

It is worth being precise about what most AI training programs actually deliver. They teach employees to use specific tools. They cover interface navigation, prompt construction, workflow integration, and output evaluation. Those are real skills, and they are not useless. A team that understands how to use AI tools effectively is more capable than one that does not.

But tool proficiency and AI leadership capability are not the same thing, and conflating them is the source of the problem. Tool proficiency answers the question "how do I use this?" AI leadership capability answers the question "should we use this, for what purpose, in what context, and how do we adapt when the context changes?"

CDW CTO Sanjay Sood captured this distinction precisely when he cautioned organizations not to treat AI as "a hammer for every nail." That warning addresses the single most common failure mode in enterprise AI adoption: leaders who have learned enough about AI tools to be dangerous, but not enough about AI strategy to be effective. They apply AI to every problem because they are excited about the capability, not because they have evaluated whether it is the right tool for the specific job.

The result is exactly the pattern we see in AI adoption data: high initial enthusiasm, moderate short-term tool usage, and significant drop-off once the novelty wears off and the reality of integrating AI into complex, real-world workflows sets in. The tools were fine. The strategic capability to deploy them well was not developed.

What the Research Tells Us About Who Succeeds

The evidence for what actually predicts AI adoption success is fairly consistent. It is not tool familiarity. It is not technical background. It is not even prior experience with AI systems. The strongest predictors are leadership capabilities that look remarkably similar to what the AI Leadership Triad describes.

Leaders who demonstrate strong adaptability — who can change their working assumptions when new information arrives, who tolerate ambiguity without becoming paralyzed, who treat failed experiments as data rather than failures — are dramatically more likely to lead successful AI initiatives. Because AI implementation is inherently experimental. Tools behave unexpectedly. Integration surfaces unforeseen complications. Use cases that seemed straightforward turn out to require significant workflow redesign. The leaders who navigate this terrain effectively are not the ones who know the most about the tools. They are the ones who adapt fastest when the tools do not behave as expected.

Leaders who demonstrate strong innovation discipline — who consistently tie new approaches back to specific, measurable outcomes rather than adopting novelty for its own sake — select better use cases from the start. They do not chase every new model release or platform announcement. They ask whether this specific capability solves a specific problem that their organization actually has. That discipline saves months of misdirected effort.

And leaders who demonstrate strong cross-domain creativity — who pull ideas and frameworks from outside their immediate context — find the non-obvious AI applications that create disproportionate value. They are the ones who apply a supply chain optimization framework to a humanitarian logistics problem, or a customer journey mapping approach to donor stewardship. The insight comes from outside the sector. The AI implementation follows. As Joel Salinas explores in the article on building a human-centered AI strategy, the most effective AI deployments start with human and organizational insight, not with the technology itself.

Three Things Every AI Training Program Should Include

The fix is not to remove tool training from AI development programs. Tool proficiency matters. The fix is to treat it as one component of a broader curriculum that deliberately develops the leadership capabilities that determine whether those tools create value. Here is what that looks like in practice.

1. Adaptability exercises — practice with ambiguity, not just tutorials.

Most AI training is structured as tutorials: here is how to do X, here is how to do Y, here is what happens when Z. That structure is effective for teaching known workflows. It is actively unhelpful for developing adaptability, because it trains people to expect that AI will behave predictably and that there are known right answers.

Effective adaptability development uses a different structure: here is a real problem, the AI output is not what you expected, figure out why and adjust. The objective is not to learn a specific technique. It is to develop comfort with iteration, the habit of diagnosing unexpected outputs rather than abandoning the tool, and the flexibility to change your approach when your first attempt does not work. That kind of exercise is almost entirely absent from standard AI training programs, and its absence is a significant contributor to post-training adoption dropoff.

2. Innovation audits — evaluate whether training outcomes serve core work.

Before any AI training program ends, participants should complete an innovation audit: a structured evaluation of whether what they learned addresses the actual work that most constrains their organization's effectiveness. Not "what cool things can AI do?" but "which of these capabilities, if applied consistently, would create the most value for the people and mission we serve?"

This exercise serves two purposes. First, it forces the translation from generic AI capability to specific organizational application — the translation that most training programs leave entirely to the participant, and that most participants never complete. Second, it surfaces the cases where the training addressed capabilities that are not relevant to the participant's actual work, which is important information for program designers and organizational leaders.

Joel Salinas builds this kind of audit into every leadership development engagement because without it, the connection between learning and application remains assumed rather than explicit. Making it explicit is what drives actual behavior change.

3. Creative cross-pollination — expose leaders to ideas from outside their industry.

The most valuable AI applications are almost never the obvious ones that everyone in a given sector is already pursuing. They come from cross-domain insight: the framework borrowed from a different industry, the analogy noticed between two seemingly unrelated problems, the principle from one domain applied unexpectedly in another. That kind of creativity is not random. It is a skill that develops through deliberate exposure to ideas and approaches from outside one's immediate world.

AI training programs almost universally focus on use cases within the participant's industry or function. That focus is understandable — it makes training feel immediately relevant. But it systematically limits participants to the obvious applications and cuts off the exposure to adjacent ideas that produces breakthrough thinking.

Effective programs deliberately include cross-industry case studies, structured conversations between participants from different sectors, and frameworks borrowed from fields outside the organization's primary domain. The goal is not to make everyone a generalist. It is to expand the idea-space from which participants draw when thinking about how AI applies to their specific challenges.

Evaluating Your Current AI Training Investment

Here is a diagnostic question worth asking about any AI training program your organization is running or considering: if the tools being taught changed significantly in six months — which is not hypothetical, it is the current pace of the field — would your leaders still be better at their jobs for having gone through this training?

If the answer is yes, the training is building something durable. If the answer is no, the training is optimizing for tool familiarity that will need to be rebuilt on the next platform iteration. That is not worthless, but it is a poor return on a significant investment.

The goal of AI leadership development should be leaders who are more capable regardless of which specific tools are available to them. Leaders who adapt faster, evaluate more clearly, and see more creatively. That is the outcome that justifies the investment. And it is the outcome that the majority of current AI training programs are not designed to produce.

If you want to build an AI leadership development approach for your organization that develops the capabilities that actually determine results, the right starting point is a conversation about what your leaders most need to grow. Joel Salinas designs those development approaches around the AI Leadership Triad — and the starting point is always a free discovery call.