There is no shortage of enthusiasm around AI skills training right now. Budgets are flowing, content libraries are expanding and L&D calendars are packed with upskilling initiatives. Organizations of every size are racing to prepare their workforces for an AI-driven future and that urgency is understandable. But in the rush to act, a critical question is getting lost: How do we know any of it is working?
This is not a new problem for L&D. The field has long struggled to connect learning activity to business outcomes. But with AI, the stakes are higher. We are talking about transformational investments. Not just in technology, but in the human capability required to use that technology effectively. And yet, by and large, organizations are measuring these efforts the same way they’ve always measured training: completions, attendance and smile sheets.
That approach won’t cut it anymore. If AI skills training is going to deliver on its promise, L&D needs to fundamentally shift its measurement strategy.
Start With the Business Problem, Not the Training
The most common mistake organizations make with AI skills programs is that they launch training without first answering a deceptively simple question: What business problem are we trying to solve?
It sounds obvious. But in practice, most AI training initiatives are driven by a mix of competitive anxiety and vendor enthusiasm rather than a clear-eyed alignment to specific business goals. The result is a lot of activity with very little accountability.
Consider a practical example: if the goal of an AI program is to accelerate the speed at which sales teams respond to RFPs, then the training should be designed with that outcome in mind from day one. What does success look like? How much faster do responses need to be? What does that enable for the business? More proposals, higher win rates, freed-up bandwidth for strategic work? These are the questions that should shape the learning strategy, not the other way around.
L&D professionals need to be business partners, not order takers. That means pushing back when AI training requests arrive without a clear link to performance outcomes and working collaboratively with business leaders to define what good looks like before a single course is built.
Measuring AI Skills Is Not Different, But It Demands More Discipline
Here’s something that often surprises people: measuring AI skills is no different, conceptually, from measuring any other kind of training. The fundamentals of learning measurement still apply. What changes is the level of rigor and intentionality required, because the expectations around AI are so much higher.
Effective measurement requires establishing baselines before training begins, capturing data at multiple points during and after learning and connecting learning outcomes to real performance indicators. For AI skills, this means going beyond whether employees completed a course and asking whether they are applying AI tools in their work and whether that application is producing the results the business expected.
This is precisely where solutions like Explorance’s Metrics That Matter (MTM) become strategically valuable. MTM is purpose-built to help organizations design, deliver and measure L&D programs that connect directly to business outcomes. With access to the world’s largest L&D benchmark repository (over two billion data points!), MTM gives learning leaders the context they need to evaluate performance not just in isolation, but against industry peers and best-in-class standards.
MTM’s integration of Explorance MLY, an AI-powered analytics engine, takes this a step further by blending quantitative and qualitative data to surface insights that would otherwise remain buried. Natural language feedback from learners can now be analyzed at scale, revealing patterns in perception, application and performance that traditional metrics would miss entirely. For organizations trying to measure something as nuanced as AI capability development, this kind of depth is not a luxury but a necessity.
The Scrap Learning Problem in AI Training
One of the most telling indicators of whether training is working is scrap learning, or the portion of what employees learn that they never actually use on the job. In well-designed programs with strong business alignment, scrap learning rates are low. In programs that exist primarily to check a compliance box or satisfy an executive mandate, those rates can be alarmingly high.
The AI training landscape right now is ripe for a scrap learning crisis. Organizations are pushing enormous volumes of content to large employee populations, much of it generic, much of it disconnected from the specific workflows and decisions those employees make every day. Without a measurement strategy that tracks application and impact, L&D has no way to identify what’s sticking and what’s being forgotten the moment the browser tab closes.
Metrics that track Net Promoter Score, skill application rates and performance improvement over time provide a far more honest picture of training effectiveness. They create the feedback loop that allows learning leaders to continuously refine their programs and shifting resources toward what works and away from what doesn’t.
The Accountability Moment for L&D
The volume of investment flowing into AI adoption right now represents an extraordinary opportunity for L&D to demonstrate its strategic value. But we have to rise to the moment. Business leaders are watching. CFOs are scrutinizing budgets. And increasingly, executives want to see evidence that their AI workforce investments are paying off in ways that matter to the bottom line.
L&D leaders who can walk into an executive conversation with data and say, “Here is the capability gap we identified, here is the program we designed to address it, here are the performance outcomes we’ve seen as a result” will earn credibility and resources. Those who can’t will find their programs deprioritized in the next budget cycle.
This is the accountability moment the field has been building toward for years. AI has simply made it impossible to delay any further.
Getting the Foundation Right
For organizations that want to get ahead of this challenge, the path forward starts with three commitments. First, build alignment before building content. Every AI skills initiative should begin with a documented agreement between L&D and the business about what problem is being solved, what success looks like and how it will be measured. Second, invest in measurement infrastructure. Whether that means deploying a purpose-built platform like Metrics That Matter or strengthening internal data practices, organizations need tools capable of capturing learning impact across the full performance arc before, during and after training. Third, use data to drive continuous improvement. Measurement is not a post-program exercise. It is an ongoing feedback loop that should inform program design, delivery and iteration in real time.
The organizations that treat measurement as an afterthought will struggle to justify their AI training investments. The ones that build it into the foundation from the start will find themselves with a compelling, data-driven story to tell, as well as the organizational trust that comes with it.
Brandon Hall Group™ Institute and Preferred Provider Program
Explorance is a proud client of the Brandon Hall Group™ Institute and a recognized Gold level Smartchoice® Preferred Provider, reflecting a shared commitment to advancing the science and practice of learning measurement. Through this partnership, Explorance and Brandon Hall Group collaborate to help organizations build the measurement strategies, tools and capabilities needed to connect learning investment to business performance. The Brandon Hall Group™ Preferred Provider Program recognizes solution providers who have demonstrated alignment with best practices and the ability to help organizations achieve excellence in talent development.
To learn more about Brandon Hall Group™, click here.
