As the labor market tilts in favor of employers and workers increasingly staying put in their roles, some organizations are becoming more comfortable eliminating or postponing learning and development (L&D) efforts. After all, training is expensive and time-consuming. And that’s hard to justify in the face of shrinking profit margins and a shaky economy.
Yet, skills gaps persist. And the labor market continues to fall short in providing the talent companies need.
Amid this, why do employers continue to approach L&D as a discretionary expense rather than a key driver of long-term business success?
Simply put: measuring the ROI of learning efforts is hard. And organizations’ emphasis on quick, short-term results and quantitative metrics only makes it harder.
Read on to learn:
- Why hard skills application can be just as difficult to quantify as soft skills application.
- How to combine qualitative and quantitative insights for a well-rounded approach to measuring L&D ROI.
- Three practical approaches for building a human-centered L&D ROI assessment framework.
The ROI dilemma: Why learning outcomes are difficult to measure
Measuring the impact of learning programs requires tracking complex, long-term developments in individual and team performance—as well as broader business outcomes. Two inescapable realities make quantifying these efforts particularly challenging:
- Tracking skills and knowledge application isn’t straightforward: It’s one thing to measure how many hours an employee spent in class or completing a learning module. But measuring how they go on to apply those skills in their jobs is a completely different challenge:
- Hard skills are easy to quantify and test, but tracking how they translate into workplace impact can be difficult for two reasons. First, they’re often used in ways that aren’t immediately apparent or visible. For example, a software engineer may write cleaner code after training, but tracking whether those outcomes resulted directly from the learning effort requires a highly-structured review process—plus input from SMEs to ensure correct assessment.
Second, most hard skills have multiple applications. A cloud engineer may apply newly-acquired skills in AWS across a diversity of tasks—from cloud infrastructure optimization to workflow automation. But, post-learning assessments that fail to consider even one of these applications risk providing an incomplete picture of skill utilization. - The ROI on soft skills development is even harder to measure because their application is often subjective, context-dependent, and influenced by interpersonal dynamics. For example, a team member may see a colleague as a strong communicator, while another may have a hard time understanding them.
Compounding the problem is the fact that soft skills are often used in conjunction with other soft skills, making it difficult to isolate and measure the impact of any one skill. For example, a project manager doesn’t just use communication skills—they also apply emotional intelligence, conflict resolution, and adaptability.
- Hard skills are easy to quantify and test, but tracking how they translate into workplace impact can be difficult for two reasons. First, they’re often used in ways that aren’t immediately apparent or visible. For example, a software engineer may write cleaner code after training, but tracking whether those outcomes resulted directly from the learning effort requires a highly-structured review process—plus input from SMEs to ensure correct assessment.
- New skills don’t immediately translate into business outcomes: Gaining enough proficiency to translate knowledge into tangible results takes time. Consider a manager undergoing leadership training in areas like conflict resolution, negotiation, and decision-making. Mastering these abilities takes months—or even years—of consistent exposure, practice, and constructive feedback.
Similarly, it may take a cybersecurity analyst learning threat detection anywhere from six months to a full year to master identifying vulnerabilities and implementing advanced defense strategies.
Beyond the numbers: A human-centered framework for assessing the ROI of L&D
Learning is a human problem. And numbers alone fail to capture the full scope of how people learn or how they apply new knowledge.
A human-centered approach to learning must extend beyond learning itself to include how organizations define and evaluate success.
This means informing human-centric quantitative measurements such as promotion rates and internal mobility and with quantitative insights, such as manager and peer observations, to build a more comprehensive measurement framework for learning efforts.
In short: Use quantitative scores for tracking trends over time and qualitative insights to understand the “why” behind those numbers. Here are three strategies combining both:
1. Establish peer and manager feedback loops
Firsthand peer and manager feedback gives you qualitative insights on an employee’s skill application. At the same time, it also captures everyday behavioral and competency shifts that traditional metrics may miss.
| Quantitative methods | Qualitative methods |
| Periodically measure new skill application and performance improvements. | Use coaching conversations to guide skill development and performance improvement. |
| Compare individual performance scores against team or industry benchmarks. | Conduct group interviews to explore the impact of new skills on team performance. |
| Collect ratings from multiple sources for a full picture with 360-degree feedback. | Get deeper insights into behavior, teamwork, and impact through one-on-one interviews. |
2. Track career progression and internal mobility
Connecting upskilling to tangible career growth shows employees the long-term value of learning, drives organizational performance, and reduces turnover by reinforcing investment in employee development.
| Quantitative methods | Qualitative methods |
| Measure the number of employees who move into higher positions after upskilling through promotion and role tracking. | Benchmark individual progress against individual growth plans and career goals. |
| Track lateral moves and expanded responsibilities post-training through internal mobility metrics. | Collect manager feedback on employees’ readiness for promotions or new roles. |
| Compare pre- and post-training evaluations to measure skill improvement through targeted assessments. | Interview employees in new roles to gain insights into their career progression. |
3. Use skills-based project assignments
Assigning employees to real-world projects soon after upskilling ensures proper application of new knowledge and provides a clear measure of how well they’ve internalized their learning
| Quantitative methods | Qualitative methods |
| Measure completion times, accuracy, and quality of work on post-upskilling assignments through project success rates. | Get insights on how new skills helped navigate the project through one-on-one conversations with employees. |
| Compare key performance indicators (KPIs) before and after upskilling through performance improvement metrics. | Use manager observations to assess individual and team performance in post-training projects. |
| Measure pre- and post-project knowledge retention through skill proficiency assessments. | Explore the impact of new knowledge in project execution through team conversations. |
The long game: Rethinking the ROI of learning
Building the business case for upskilling and reskilling isn’t a simple equation. However, by taking a long-term approach and redefining how learning ROI is measured, organizations can go beyond surface-level metrics—both proving the value of L&D and optimizing it for maximum impact on employees and the business.
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