Opening Note from Sandy
If you’ve ever worked with me, you have heard me say this sentence, “Yes, it’s a great program, but how has it helped the business?” After more than 25 years leading learning organizations in large organizations, I have seen how difficult it can be for L&D teams to demonstrate meaningful business impact. But without it, you are creating events, not true organizational change.
Most organizations have plenty of learning data. We can report completion rates, assessment scores, participation, learner confidence, satisfaction, and time spent in a program. The harder question is one that leaders continue to ask: “What difference did this make to the business?”
AI can help L&D professionals answer that question more effectively. Used responsibly, AI can organize scattered information, identify patterns, surface important questions, and help translate evidence into language leaders understand.
AI doesn’t replace professional judgment. It should never be used to create unsupported claims or make a learning program appear more successful than the evidence shows. The best use of AI is as a thoughtful learning analytics partner.
Begin with the Business Problem
A strong impact story begins with the business problem, not the learning activity.
Before asking AI to analyze data, clarify what the organization was trying to change. Was the goal to improve sales performance, reduce errors, strengthen manager capability, shorten time to productivity, improve customer service, or prepare employees for new responsibilities?
Explain who was affected, what needed to improve, and why the issue mattered. Then provide AI with the program goals, learner population, timing, performance measures, and known limitations.
A useful prompt might be:
“Act as a learning analytics partner. Review the following learning and business data. Organize the information into themes. Identify what is useful, what is missing, and what questions we should ask before drawing conclusions.”
This step helps prevent a common mistake. L&D teams sometimes begin with the data they have instead of the business question they need to answer.
Bring Together Multiple Sources of Evidence
No single data source can tell the complete story.
Learning platforms can show completion, attendance, assessment results, and participation. Surveys can show whether learners found the content relevant, whether their confidence changed, and whether they intended to apply what they learned.
Human resources systems can provide information about roles, tenure, teams, and learner groups. Performance systems, customer relationship systems, quality reports, productivity measures, and business results can help connect learning to organizational outcomes.
This type of triangulation has been especially important throughout my career in financial services. Learning leaders often work in environments shaped by regulation, product changes, digital transformation, cybersecurity, mergers, and constant shifts in customer expectations. In those settings, completion data alone is rarely enough. Leaders need to see how learning supports readiness, risk management, performance, and organizational capability.
AI can help organize information across these sources and identify relationships. It might show that employees who completed a program were more likely to meet a performance target or that one team improved more than another.
However, a relationship between two measures does not automatically prove that one caused the other. Other factors may have contributed to the improvement.
Use careful language. Say that performance improved among participants, not that the program caused the improvement, unless the evidence supports that conclusion.
Ask AI to Find Meaningful Patterns
Once the evidence has been organized, ask AI to look for the patterns leaders care about.
You might ask:
“Based on this information, identify the most important insights for a senior business leader. Separate strong evidence from weak evidence. Flag claims that require more data.”
You can also ask:
Which learner groups improved the most?
What patterns stand out?
What findings are surprising?
What additional information would increase our confidence?
These questions move the analysis beyond basic activity measures. Leaders want to know what changed, for whom, by how much, and why the change matters.
AI can also identify gaps in the evidence. Perhaps you have survey results but no comparison group. Perhaps performance data is available for only part of the learner population. Perhaps the time between learning and measurement is too short to draw a conclusion.
Those gaps do not make the analysis useless. They simply need to be acknowledged. Credibility grows when L&D is transparent about what the data can and cannot show.
Translate Findings into an Executive Story
Organize the strongest findings into four parts.
First, explain the business problem. What needed to change?
Second, describe what the data showed. What moved, for whom, and by how much?
Third, provide the executive insight. What does this mean for the business?
Finally, recommend the next action. What should leaders do now?
For example, an L&D team might explain that new sales employees were taking too long to reach expected productivity. After a redesigned onboarding experience, participants reached an important performance level more quickly than the previous group. Managers also reported stronger confidence in the new employees.
The evidence may not prove that the program alone caused the improvement. However, the results suggest that the redesigned experience may be supporting faster readiness. The next action could be to expand the program while collecting additional performance data.
Challenge the Story
One of the most valuable uses of AI is as a skeptical reviewer.
Ask AI to identify unsupported claims, vague language, missing evidence, and places where the story may overstate impact. Ask whether the sample size is large enough, whether there is a comparison group, and whether the measures reflect business performance or simply learner activity.
A useful prompt is:
“Review this learning impact story as a skeptical executive. Identify claims that are not supported by the evidence, unclear language, missing context, and places where the story may overstate impact.”
Always check AI generated conclusions for accuracy. Protect confidential information, avoid selecting only positive results, and have a human review every claim prepared for executives.
In my current role as a D2L thought leader focused on employee training, I speak with learning, talent, and business leaders across industries. One theme comes up repeatedly: L&D wants to be viewed as a strategic partner, but many teams still communicate primarily through activity reports.
The opportunity is to move from reporting what happened to explaining why it matters.
AI can help L&D organize information, identify patterns, draft clear language, and test the strength of an argument. The final story should be concise, honest, and connected to action.
The strongest learning leaders are not the ones who present the most data. They are the ones who help leaders understand what the data means.
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