Artificial intelligence (AI) investment is accelerating at an unprecedented pace, with Gartner revealing that worldwide end-user spending on AI models and platforms is projected to total $63 billion in 2026, up from $39 billion the previous year. However, rising costs are failing to translate into productivity gains, with just 5% of AI projects delivering real returns, according to MIT, as the gap between AI access and capability grows.

A key driver of this trend is organizations approaching AI integration as though it is a traditional learning and development (L&D) initiative, overlooking that success in this context is dependent on building workforce capability at scale. The organizations that continue measuring participation as opposed to tangible outcomes risk falling behind during one of the most significant technology shifts since the rise of the internet.

Why Current AI Training Programs Are Missing the Mark

Most corporate learning programs are designed to meet a distinct set of goals: completing compliance training, increasing security awareness or policy education. In these instances, success is typically measured by completion, which is satisfactory for meeting regulatory requirements but does little to transform how people work.

Traditional L&D was never built to create capability, because organizations have historically hired people who already possess it. Even when a junior employee joins, upskilling them into experts falls on their peers, not the human resources (HR) or L&D team.

Previous technology shifts, such as cloud computing and the internet, evolved slowly enough that this approach sufficed, but AI is moving far faster and with far higher expectations. It therefore comes as no surprise that Deloitte research identifies the AI skills gap as the biggest barrier to integration, reinforcing that traditional approaches and optional self-development courses are not an effective workforce development strategy.

Measuring Capability Instead of Completion

One of the standout differences I have observed between successful and unsuccessful AI programs is how they define success. Metrics focused on participation are easy to track but they reveal very little about impact.

Consider an employee who completes a course on building AI agents. If measured against participation metrics, it appears on paper as though the objective has been achieved. However, in reality, if the employee never applies the skill, creates an effective solution or improves a business process, the organization has gained nothing from its investment.

The real measurement must take place after training, with leaders looking at what changed in day-to-day work, and whether those changes influenced output in a meaningful way.

This underscores why managers, who are often best placed to pinpoint changes in behavior and operations, must be active participants in AI capability development. Achieving this calls for a tighter partnership between L&D, HR, business leaders and frontline managers than many organizations have traditionally maintained.

Building an AI Training Program That Works

Quantifying the right outcome only matters if you are implementing a program worth measuring. The mistake at the program design phase tends to be twofold: either everyone gets the same training, producing a uniform workforce incapable of developing solutions to their specific needs, or training is fragmented by department from day one, resulting in teams that cannot collaborate.

In my experience, the capability tree approach consistently delivers the strongest results. The trunk symbolizes the foundational knowledge employees across every business function need, including a common understanding of AI literacy, responsible use and limitations. From that shared trunk, individuals climb through increasingly practical capabilities, progressing from understanding how AI works to proactively implementing AI to reshape and optimize workflows.

Only then does the tree branch into role-specific application, because AI is fundamentally a leverage tool and as such, every role will leverage it differently. HR professionals, marketers, sales teams, finance departments and operational staff will all apply AI to different workflows and create value in different ways.

Sustaining Capability as Technology Shifts

Equally important is sustainability, a factor which is often overlooked when training programs are built. Instead, many resort to AI “strike teams” that temporarily train employees and then move on. While these initiatives can generate short-term wins, they rarely produce lasting capability and in today’s AI landscape, it’s shocking how quickly isolated training can become obsolete.

Successful programs are adaptive, scalable and trackable. They evolve alongside the technology, incorporating new capabilities, and providing leaders with visibility into workforce readiness. Without that visibility, disparities emerge, creating friction between AI-enabled employees and those who lack the same capabilities.

We are living through a technology shift that will fundamentally reshape how organizations operate. Those that continue treating AI as a traditional learning problem will soon discover that completion certificates are a poor substitute for genuine transformation.