Technology has conditioned workers to expect quick and easy experiences — from Google searches to help from voice assistants — so they can get the answers they need and get back to work. While the concept of “on-demand” learning is not new, it’s been historically tough to deliver, and though most learning and development departments have linear e-learning modules or traditional classroom experiences, today’s learners are seeking more performance-adjacent, “point-of-need” models that fit into their busy, fast-paced work environments.
Enter emerging technologies. Artificial intelligence, voice interfaces and augmented reality, when applied correctly, have the potential to radically change the nature of how we learn at work. What’s more, these technologies are emerging at a consumer-level, meaning HR’s lift in implementing them into L&D may not be substantial. Consider the technologies we already use regularly — voice assistants like Alexa, Siri and Google Assistant may be available in 55 percent of homes by 2022, providing instant, seamless access to information we need on the spot. While asking a home assistant for the weather, the best time to leave the house to beat traffic or what movies are playing at a local theater might not seem to have much application in the workplace, this nonlinear, point-of-need interaction is already playing out across learning platforms.
To understand point-of-need learning it’s important to consider the two distinct types: performance support and performance adjacent. The former is directly embedded into the workflow and the latter is easily and seamlessly accessed outside of the workflow. While performance-support technologies might appear superior, performance-adjacent tools are more cost effective and scalable because they don’t require customization to specific workflows or need to be embedded in particular technologies. Considering the pace of innovation and change in the workplace, these benefits are significant.
Technologies for the Future of Learning
Natural language processing is a field of computer science that uses AI to process large bodies of natural-language data. For learning, this allows users to jump to the exact point in a digital book, chapter, video or paper that provides the relevant information they need. This yields huge time savings for users who would typically have to comb through superfluous information to identify the content most meaningful to them.
AI is also a key component of voice technologies — think Siri — that represent an exciting frontier for learning. These technologies don’t require users to do anything more than speak, creating a new but familiar channel for learners that can run parallel to the workflow. Without taking their eyes off the screen, learners can pose a question and get an answer without any interruption to their activities. The future applications of this technology in learning are perhaps the most exciting, as speaking commands can extend from questions and answers to building lists, quizzes or requests to highlight key learning facts for future review.
The growth of AR, digital information overlaid on real-world settings, has enormous potential for point-of-need learning. Take the example of Apprentice, a company that raised more than $25 million in venture funding for its AR tool for scientists, engineers, research and development professionals and those in associated manufacturing roles. Through smart glasses, the technology enables workers to troubleshoot machinery remotely or, conversely, to share what they see on-site with others in remote locations. Users can also access manuals and other critical information with the glasses whenever they need to. In this example, users are engaged, empowered and learning simultaneously. It’s an intriguing glimpse into how AR can help workers increase productivity, solve problems and learn on the job.
Are Learners Ready? Yes.
Performance-adjacent learning tools minimize friction by making it easy to access information and quickly return to the job at hand. The key to their use and relevance lies in their precision and efficiency — the faster the tool helps the learner arrive at the solution, the better.
At O’Reilly, we examined one quarter’s worth of usage data from a selection of the more than 2.25 million users on our learning platform. The results yielded 1,622,983 individual learning events across 12 industries. Our research also found that learners were engaged in nonlinear learning behavior an average of 42 percent of the time. This means nearly half of all learning behavior is performance-adjacent — jumping in and out of the platform for short sessions as opposed to traditional learning paths.
To quantify this impact, consider the example of one O’Reilly customer, a large financial institution that suffered a crucial system outage. The company was losing thousands of dollars an hour until an engineer consulted the O’Reilly platform, found the answer he needed and was able to correct the problem. By the company’s own estimate, this example of performance-adjacent learning likely saved it more than $500,000.
Time to Rethink How We Measure Learning
More often, today’s workers are demanding the flexibility to work from home or in the field, a request made possible by cloud applications. The possibility that learning can happen anywhere — and with minimal disruption — means the potential for upskilling and reskilling is immense. With this trend in mind, the notion of “time on learning,” a more traditional L&D metric, becomes nearly irrelevant. And yet, even in the wake of learner demand, measurable benefits and reskilling potential, many organizations are hesitant to change the status quo.
A recent study by the Association for Talent Development found the average number of formal learning hours (standalone time away from regular work activities) per employee increased to 34.1 hours in 2016 from 33.5 in 2015. With all we know, is time spent in learning modules really the best measurement of learning impact at work?
To succeed, L&D professionals must overcome the challenge of enabling continuous, frictionless learning for today’s learners. With advances in technology, this challenge can readily be met and new opportunities for learning beyond what we even thought possible just a few years ago are now reality. The possibilities are endless, but one thing seems certain: Point-of-need and on-the-job learning experiences are about to get a lot more creative and drive stronger results.
Karen Hebert-Maccaro, Ph.D., is chief content officer at O’Reilly, responsible for leading the organization’s content and learning strategy. Comment below, or email editor@CLOmedia.com.