The pace of change in business and technology has dramatically affected the need for upskilling and reskilling in the enterprise. The average shelf life of a skill is now less than five years, and some estimates suggest that as much as 50 percent of the knowledge learned during the first year of a four-year technical degree program is outdated by the time the student crosses the stage for graduation. Once they are in the workforce, employees must stay on top of tools that help them do their job more efficiently, and they’re faced with emerging technologies that force them to move faster just to keep up. For employers, AI, mobile and voice assistant technologies have create an expectation of immediate access to information for customers and employees alike.

These factors may contribute to a change in today’s workforce that is motivated more by the opportunity to develop skills and knowledge than by perks, informal work environments and even money. According to recent surveys, 87 percent of millennials say that professional development and career growth are important in a job, and 46 percent of employees say that limited opportunities to learn new skills is the top reason why they are bored in their current roles. As a result, over half of those employees are likely to change jobs to pursue opportunities that are more rewarding.

It’s clear that learning is more important than ever for both individual and organizational success, but on-the-job learning is hardly a new concept. Since the late 1990s, the 70-20-10 model of learning has advocated for 70 percent of learning to happen on the job, followed by 20 percent through developmental relationships (social learning) and 10 percent through more formal coursework. This model is still widely accepted, but it has become increasingly murky as our professional, social and personal worlds overlap. To address this phenomenon, enterprises should instead consider performance-adjacent learning.

Performance-adjacent learning does not mean embedding tools directly into a workflow but, instead, minimizing the effort and time it takes to access information, so the person is able to return to the workflow quickly. These tools sit to the metaphorical (or actual) right or left of the workflow. What these tools lack in precise input (i.e., there are no pop-ups that provide the next step), they make up for in usefulness across multiple workflows.

Many organizations employ a variation of performance-adjacent learning in performance support tools. For instance, call center employees may have a computer program that runs them through the introductory speech provided to incoming callers. From there, the software might provide customized responses to queries and solutions, enabling the employees to solve problems without having to memorize dozens, or even hundreds, of protocols. This type of learning can be effective in various settings and puts information at the employees’ fingertips. However, they require custom tools to meet each individual workflow and, therefore, are difficult or impossible to scale across an organization.

A Better Approach to Learning

The keys to successful performance-adjacent tools are frictionless access; finely tuned search functionality; and the ability to return a variety of content types so the user can quickly and easily find an answer, solve a problem, or build on an idea – and then immediately return to the workflow. Data from O’Reilly’s online learning platform during the third quarter of 2017 found that 42 percent of learning events were nonlinear. What’s more, learners who are more proficient in a particular topic are more likely to behave in a nonlinear manner. These findings have significant implications for learning professionals, because they suggest that optimizing for nonlinear learning is among the best ways to support the growth of more proficient learners.

Reporting on the ROI of Performance-Adjacent Learning

Performance-adjacent learning behavior aligns with the popular notion of ubiquitous learning, but therein lies a challenge for learning professionals. Learning that occurs all the time and at any time requires that we think differently about what metrics matter the most. For example, let’s say a software developer in your firm is stuck on a particularly challenging piece of code for a new product feature. She asks a few members of her team for help but is still unable to find a resolution. As a next step, she enters a learning ecosystem; searches for a term that describes the problem; and is directed to the precise section of a course, book or video that addresses the issue. Instead of continuing to troubleshoot, she finds the answer and returns to work within 10 minutes.

At first glance, this example demonstrates great ROI, but reporting time spent in the learning tool will paint the picture of minimal or even insignificant use. Performance-adjacent learning behavior is nonlinear and doesn’t simply progress from point A to point B. It may appear in usage reports as sporadic consumption of small portions of content, but the value of these short bursts of learning is significant.

Rather than discounting this type of learning behavior, enterprise organizations should embrace it. Performance-adjacent learning empowers employees to learn in the moment of need, and tools designed specifically for this type of learning can increase efficiency and improve productivity. L&D professionals need to rethink how they measure learning success, since traditional “time-spent” metrics can be arbitrary.

Learning is no longer confined by classroom walls, working hours, desktop computers or even books. Technology has made learning ubiquitous, and it can and should happen throughout the course of work. As such, performance-adjacent learning is an obvious path to stay in step with the pace of technological and business change.