Modernizing training metrics in the AI era

Did you know that more than 9 in 10 organizations assess learning programs with training metrics like satisfaction surveys? Meanwhile only 4% excel at using actual learning data for evaluation.

If there’s one thing about L&D that’s becoming increasingly obvious in the AI era, it’s that the ways organizations measure the effectiveness of learning programs aren’t good enough. So, what problems do they have, and how can we improve training metrics in a meaningful way? 

This discrepancy alone is bad enough. Unfortunately, there’s still more to talk about. Keep reading to learn what top-performing organizations get right compared to the majority. Then learn how the right AI-powered tools and tactics can make a world of difference. 

The measurement gap 

Just about every business has some form of employee training. Most of those will assess its effectiveness in one way or another. Unfortunately, some of the most popular training metrics tend to fall short. 

According to our sponsored ATD report, The Future of Evaluating Learning and Measuring Impact: 

• 93% of organizations measure learner satisfaction. 

• Less than half (42%) measure actual behavior change. 

• Only 16% of respondents rated their organization “proficient,” while just 4% rated their organization “excellent.” 

It’s understandable up to a point. Learning programs must continue to satisfy employee expectations to ensure they buy into and engage with other programs in the future. Otherwise, even well-thought-out courses could fail to have an impact. 

But Intel’s senior HR data analyst, Zsolt Olah, thinks you should drop them: 

“I recommend getting rid of satisfaction surveys (ones that ask how well attendees liked the course) and focus on gathering more actionable data from surveys.” 

This is because overreliance on satisfaction scores means you’re bound to overlook vital considerations. Satisfaction doesn’t tell you how much people have actually learned, nor does it reflect the impact of training on performance. 

Why it matters 

Without training metrics that genuinely reflect impact, L&D teams will struggle to provide courses that genuinely benefit the business and its employees. As a result: 

• L&D remains a huge cost center: Whether you’re manually building new resources, paying for external libraries, or using SaaS-based AI tools, there are always costs associated with new training. But, if learning continue to fall short of impact goals, it just becomes a hole to drop money into with no promise of improving the business. 
   

• Stakeholder buy-in suffers: Rising L&D costs with no sign of a pay-off are bound to make senior leaders impatient. When training consistently fails to support business goals, C-suite buy-in dries up, and they become much more likely to shoot down future proposals. 

If you’re already in this situation, you may feel trapped in a spiral. It’s not be easy to restore stakeholder trust once damaged. But it’s certainly possible. For insights into how to improve, let’s look at what the best-performing businesses do. 

What top performers get right 

Many businesses struggle with training metrics. That said, ATD’s report also highlighted the habits that separate top performers from the rest. Here are the three habits of highly successful L&D teams when it comes to evaluating learning impact: 

Comprehensive proficiency 

For businesses to be considered “proficient” at measurement and evaluation, our sponsored ATD study required participants to rate them “good” or “excellent” at the following: 

• Measuring learning program effectiveness 

• Communicating impact to stakeholders 

• Using learning program data for business decisions 

This is why only 16% of respondents rated their organization as proficient. While more businesses did well at informing internal stakeholders, far fewer were effective at using learning data to make business decisions. If they don’t use training metrics effectively, it calls the validity of those insights shared with stakeholders into question. 

Data source variety for stronger training metrics

Businesses most commonly use surveys and informal conversations as data collection methods. Meanwhile, user reaction (i.e. satisfaction) and program completion data are the most commonly collected training metrics. 

Proficient organizations tend to use four or more data sources as part of training metrics to inform L&D and business decisions. These typically include but are not limited to: 

• Learning goal attainment: Tracking individual learning goals as milestones goes a step further than simply tracking completion. It’s true that someone could feasibly coast to the end of a course without taking anything in. But recording each distinct goal within a course at least provides some indicator of learner engagement. 
   

• Time to competence: How long it takes your people to complete training can tell you a surprising amount about its effectiveness. If it’s too quick, your course probably isn’t challenging enough. If, on the other hand, it takes too long, then your materials or exercises probably lack clarity. 
   

• Behavioral change: Once training is complete, how does it impact employee actions going forward? This is easily one of the most important training metrics you can track, as it most clearly highlights the difference before and after learning. If training has no discernable impact on behavior, it’s a safe bet that it’s not fit for purpose. 

The role of AI in training metrics

One question you may be asking is how your L&D team can focus on collecting and analyzing data without: 

A) Neglecting the human support side of learning management. 

B) Hiring more people for your department. 

The simple answer is that you don’t. 

According to our ATD report, 53% believed AI would have a “somewhat positive” impact on measuring and evaluating learning programs over the following two years, while 28% believed it would be “extremely positive.”  

Findings from other analyst groups seem to support this. For example, Gartner predicts that, by 2030, AI will perform 50% of HR tasks while automating the rest. 

On top of that, according to CIPD, 85% of those who have had tasks automated by AI report improved performance. So, in summary, AI can support L&D by automating data collection and analysis. This provides better training metrics while freeing team members to focus on the human elements of learning management. 

The playbook for better training metrics 

Of course, AI investment alone won’t make for more effective training metrics. You’ll need an effective data collection and management framework to get the most out of it. While that may sound complex, you can boil it down to three simple steps: 

• Audit: First, you need to run an information audit to get a full list of all possible data sources. These include direct learning program data (like completion rates, time to finish, and course feedback), and behavioral data, which obviously includes performance habits, but can also include engagement stats. 
   

• Integrate: Once you’ve identified a diverse array of data sources, it’s time to bring them together. You can do this manually or through AI analytics. Zensai users can leverage the full value of our Human Success Platform by bringing together learning, engagement, and performance data for a fully fleshed-out view of talent management. 
   

• Analyze: Once your data is in place, it’s time to collect actionable insights. With the right AI tools, you can gather this info in real time. Learn365 includes built-in reporting functionality. This allows users to centralize learning data into custom dashboards and reports, so your stakeholders always get the latest insights. 

By following these steps, you can improve the quality of your training metrics so they actually reflect the impact of learning on your organization. Better yet, with AI, you don’t even have to crawl through the data yourself. Just update the dashboard in your AI-powered LMS or compile the latest report through Microsoft 365, and you’re ready to go! 

Leave smile sheets where they belong: In the past! 

It’s time we moved beyond smile sheets and other shallow training metrics. The professional landscape is rapidly changing. Organizations need to know that they and their employees can keep up. It’s time to modernize training metrics by connecting learning with performance and engagement insights. 

Zsolt Olah was right about the need to ditch ineffective satisfaction surveys. With the power of AI analytics, your L&D team can deliver clearer results while focusing on what matters: Finding the best actionable insights based on real experience, then working to implement them as part of your learning culture.