What does the landscape look like now?

Amongst all the hype and froth about the future, it can be easy to miss where the learning and development industry is at the moment. Broadly, the market has been challenging for most, with learning budgets under pressure and headcounts similarly squeezed. At the same time, L&D leaders are tasked with responding to the skills challenge (now their top priority), supporting business change and career development needs, amongst many other requirements. All whilst managing the continuing demands of compliance learning, leadership development and onboarding. The notion of ‘doing more with less’ is being thoroughly tested.  The need to demonstrate value to the business for these efforts is a more common demand from stakeholders, whilst learner relevance and attention require ever sharper design and learning experiences. So, analytical and consulting capabilities are rising in significance too.

Traditional formal learning, however, has tended to focus on the design and deliver phases of the learning cycle. The analogue model of classroom instruction shifted to the content-led digital learning model that creates the mainstream of the industry today. This shift accelerated during the COVID years. But the current skills-focused environment demands closer attention to the planning, application, measurement and analysis phases and the application of skills in addition to their instruction.

There are some pressing gaps to attend to. It is against this backdrop that the disruption and innovation of AI is arriving.

 

Where is innovation effort focusing?

The digital learning market is, in many ways, still a content-focused market and this is the focus of the current wave of roadmap development. Content alone may not be enough to deliver real change or performance, but it is the staple of the industry and an ever-present component of pretty much all solutions. The trend of the last decade towards quicker and lower cost creation has now been supercharged by the arrival of AI technologies in the last two years. Synthetic media solutions have been widely adopted by vendors and corporates alike, as have Gen AI tools for design and authoring tasks in digital learning creation. e-Learning production has typically been a repeatable and predictable process; ideal for Gen AI automation. AI-based translation is also transforming time to readiness and costs for international projects. Both these AI use cases have rapidly become commonplace, further eroding the barriers to learning content production, as authoring capability is increasingly everywhere. AI automation is impacting instructional design also, reshaping the skills and roles needed, as well as productivity and resource planning decisions.

To borrow from Warren Buffet, “businesses get the learning and development they deserve…”. The concentration of innovation effort on the content segment of the industry reflects the priorities and goals that most L&D functions are funded to provide. There are signals of broader innovation, but the focus, for now, is on process innovation, with the efficiencies it provides, rather than product innovation and the fresh solutions it might create.

 

What is coming and how will it impact our industry?

This is not to underestimate the deep and widespread impact these efficiency innovations are creating. As the economics of content development are changing (and doing so rapidly), expectations of what is possible are changing and of the resources required to bring solutions to life. Fosway analysis of vendor roadmaps shows that most AI developments that are live with customers or in beta testing, congregate around the design and development stages of learning content workflows. Similarly, capabilities set to go live during the remainder of the year are aimed at the same tasks and processes. More efficiencies are on the way, as well as fresh potential.

It is possible that a freer view of innovation might dominate in time of economic plenty, but these new judgements about what it takes to create learning solutions are changing at a time of relative economic pressure, whilst savings are close to top of mind.

It is not only an efficiency story, however. The potential to bring personal experiences to life can be seen more clearly now. The ability to assess and target highly relevant skills learning to an individual at organisation-wide scale is possible in ways we have not seen before. Equally, the opportunity to rehearse skills as we develop them is available in new ways like software labs for technology skills and conversational AI tools for interpersonal skills. These applied skills can now be monitored and measured at scale and personalised feedback is available as they are put into practice. Automated coaching via AI products is bringing what is often a costly and exclusive service to a broader employee population. These are all considerable changes in what is possible for people development.

 

L&D needs stronger data foundations to steer a valuable course

For L&D, underneath this landscape of disruption and potential, lies a fragile data foundation. The ability to tell a compelling story of performance value and real business outcomes needs more than narrow or traditional learning metrics. The ability to plan for, design and optimise successful solutions relies on evidence-based decisions. The ability to confidently train AI models rests on relevant, accurate, consistent and up-to-date data sources. Often, in L&D these are generally lacking. The industry is more alive to these needs than it has been in the past, but real progress towards fresh sources of value will be limited until the nettle is grasped more vigorously.

The digital revolution was fuelled by data and connectivity to create enduring change and audience value. L&D never quite grasped this reality, preferring control and the systems that enable it. As the AI revolution accelerates, the data challenge is more urgent than ever.

 

Myles Runham 

Senior Analyst at Fosway Group