Learning in the flow of work has been something I have been thinking about and working towards since the mid-1990s. I was working on a call center project with Gloria Gery at the time. The call center was a 401k answer center for three different companies. Employees of those companies would call in and ask questions like “How much does my company match?” or “What are the rules around making a withdrawal?” The call center worker had the choice of either memorizing each of the three companies’ plans or looking them up in real time on one of the three (imagine!) workstations that sat on their desks. The challenge was for them to be able learn the answers quickly and with the least amount of effort. Additionally, the operators of the call center wanted to understand how the same person could do this for ten, twenty, or even more companies. That’s a lot of workstations on one desk and not a very scalable concept. Remember, this was happening around the first waves of the internet where connectivity was still largely tied to main frame computing, especially in enterprises.
Fast forward to the emergence of Google Glass in 2013. I was excited to see a wearable device that kept both hands free. I experimented with it at KFC, where I worked at the time, to teach people how to perform tasks at their moment of need versus some eLearning course that was detached from the workflow and out of context. It went well, but the device was not ready for mainstream use. The battery life was short, the UX was clunky, and the connectivity was challenging. But augmented support in the flow of work had taken another large step forward.
Since then, there have been significant advances in the world of augmented reality (AR). With improved processing power, the miniaturization of microchips, and the ubiquity of high-speed internet, AR as a means for learning in the workflow has never been more accessible.
In the consumer world, Pokémon launched the first widespread use of AR technology via a smartphone in 2016. Today, companies like Ikea, Kate Spade, Cadbury, Converse, Tesco, and Lacoste are actively using AR as a way to help consumers connect more closely with their brands during the shopping experience—a different moment of need.
In the business world, we have seen the most AR advances in manufacturing. Companies like Volkswagen, BMW, and Porsche are actively using it with their service technicians as a way to ensure proper maintenance of new vehicles that are becoming ever more sophisticated and even of older models that could be 50 years old. In each case, technicians can use AR to ensure they are following the proper sequence of tasks and performing correctly with feedback in real time.
While manufacturing is certainly a great use case for leveraging AR to learn in the flow of work, several others exist. For example, in the restaurant businesses where I worked, an equipment breakdown could directly impact sales in real time. We have all heard about McDonald’s ice cream machines being notoriously out of service. Why isn’t AR helping to solve that problem in real time? After all, it is a real opportunity for learning in the workflow. A problem that a local person doesn’t have the experience or skill to resolve could easily be rectified with an AR solution. Another example is in retail, where merchandising could easily be supported with AR. Gone would be the days of the Plan-A-Gram. Visual guidance could provide real-time instruction on what to do and could even record the finished product to share back to a head office.
With so much promise, what is holding AR back?
This question lies at the heart of the challenge with both AR and VR (virtual reality). Because most AR solutions are device dependent, the paradigm for deployment is far beyond the everyday challenges of launching a new curricula or course. There is a considerable “ask” from our colleagues in IT that learning professionals need to anticipate.
First, we need to understand that we are essentially asking IT to take on and support one more piece of infrastructure. They already have desktops, laptops, tablets, and smartphones in the hands of their end users. Now, we are asking for another device that has all the same considerations, which need to be worked through–and then some–like security, connectivity, operating system updates, access control, and more. Our best path forward is through strong collaboration and mutual understanding to define how it could work. That might mean experimenting beforehand.
Next, we need to have a solid business case for what we want to do. We can get mesmerized by the “coolness” of the technology and often lose sight of the fact that it still needs to have a compelling purpose in our organization. Our business case must include all the “costs”: deployment, support, equipment, and development. All of those need to be contemplated and calculated against the performance improvement measures we think we can achieve in real numbers. If you have a business case that shows the investment is merely a fraction of the return, it will get funded, regardless of the size of the investment.
Conversely, if the business case is not strong, we need to be able to articulate that as well. We might settle for less-than-ideal solutions, but they might better demonstrate the business case. Sometimes getting two-thirds of the return at one-fourth the investment is a great idea too!
Personally, I hope AR can make it into the mainstream of workplace learning and performance. It has a lot of promise but clearly is not ready for prime time. The good news is that, with time, the costs should get lower, the technology should get better, and our understanding of how best to leverage it in workflow learning will grow exponentially.