Recently, I had the pleasure of interviewing both Fuse President and Founder Steve Dineen, and David Perring, Director of Research at Fosway. We had a great in-depth discussion surrounding learning strategies and their application in business. If you prefer to dive straight into that conversation you can check it out here 👇 otherwise read on!
However, as is probably no surprise, Steve and David quickly moved past the basics of learning in the flow of work into discussing the ways in which learning in the flow of work is becoming more embedded and more powerful in enterprises - and why.
Call it a quick glimpse of the future of learning, or just the typical inspiration that comes when two such great learning visionaries get together. Either way, I came away feeling that more now than ever before, companies have only scratched the surface of what transformative learning strategies such as learning in the flow of work can do. Read on to tap into my key takeaways from the discussion with Steve and David.
Technology Can Industrialise Learning in the Flow of Work
I love a good bit of technology chat especially when it’s focused on AI, so I was all ears when the discussion turned to how intelligence and automation can take learning in the flow of work one step further. What interested me the most about Steve and David’s discussion was the focus on how AI is industrialising learning in the flow of work. When I say ‘industrialising’ I mean the act of developing something at scale and in a repeatable way.
It means that smart platforms like Fuse aren’t reinventing the wheel every time they want to convey some knowledge. Firstly, we make it really easy for subject matter experts to create knowledge-based content and get it onto Fuse so that it can be viewed countless times by an unlimited number of people.
More often than not, the content is video files, or text based answers to every day problems. In other words, why not do what YouTube and Google are doing? These methods of delivery have proven to work en masse, to say the least. And unlike courses, they are fully complementary to learning in the flow of work.
We’re using AI to power search intelligence, language intelligence and of course our Knowledge Intelligence Engine. Beyond this, we’re using Machine Learning to build real-time learner experience layers for our customers, which are providing the easiest and most relevant content, research and recommendations.
I could go on (and I will, check the next section) but I wanted to make this point about industrialising learning in the flow of work first, because everything forward thinking in L&D leads on from this foundation. If you can’t industrialise knowledge and have intelligent technologies like AI make knowledge more personalised, more contextual and more available in the flow of work, you’re unlikely to be winning when it comes to accelerating business performance with your L&D programme.
Predictive, Proactive: Data will Drive Learning in the Flow of Work
The boundaries between learning and business data have started to fade. Why is this relevant to learning in the flow of work? Because as important as technology is in empowering and industrialising knowledge in the flow of work, data is equally as important - and it’s now more accessible than ever.
Data analytics are such a hot topic right now, and many enterprises have only begun to scratch the surface of what a data-driven, analytic-centric organisation looks like.
With Fuse Universal Analytics, companies can track interactions - be that content views, engagement, social, or assessment data - it’s all there. Our users can get as granular as they like in tracking, measuring and making actionable insights in order to identify trends in how people are engaging with learning.
The next step is in using Machine Learning to spot trends to predict what learners may need to know in the flow of work to perform optimally, depending on their job role and typical daily tasks. Predictive analytics will prescribe the quickest path for individuals to become high performers and maintain high performance, without skipping a beat.
After that, technology will look to data to make learning in the flow of work more proactive, from a systems point of view. While most data-driven companies will be manually looking at data insights from their learning platforms and applying hypotheses to improve them, the optimum solution for encouraging learning in the flow of work is actually a self-modifying system that automates this based on the data it processes.
What this means is that learning platforms like Fuse will proactively look at systems data to see how users are interacting with it in order to recommend changes to a company’s learning strategy. This may include sending notifications to users who are not fully engaged in the platform, or alerts surrounding content that may not exist, but which could collectively help many learners. Proactivity and data will go hand in hand to ensure learning in the flow of work continues to become better over time to drive better performance.
There’s a Ying, and a Yang
As both Steve and David noted, it’s essential to find a good balance between learning in the flow of work, and what we might call ‘out of flow learning’ - think course-based learning, certifications, compliance.
We need to set boundaries around where learning in the flow of work makes sense, because many organisations do need to achieve compliance, and there can be high consequences when their people do not have the level of skill when they need it. This is particularly the case in industries such as pharma, and manufacturing goods, where organisations also need extensive records of their training to feed into business risk and continuity models.
The balance can be addressed with a focus on access to knowledge and determining just what people need to complete a task. We should also be aware of finding a balance between upskilling and reskilling: if we focus too much on reskilling people, we risk forgetting about how they are performing today. Likewise, if we don’t give people enough foundational skills, they may never be able to practice optimally and may never gain the experience required to become experts. The key is in breaking down barriers of knowledge for individuals and striking the right balance for each and every employee.