As we look toward the state of the 2020 workplace, the pace of disruption continues to accelerate and is showing no signs of slowing down. The need for an evolving workforce with new skills, mindsets and behaviors is just as urgent now as it has ever been. In the learning and development (L&D) space, we have seen several technology predictions, such as the use of virtual and augmented reality (VR/AR) and increasingly global workforces, come to fruition. However, many of the predictions regarding the integration of technology are being realized at a slower pace, including predictions about the use of artificial intelligence (AI) and automation in L&D.

Although an organization’s L&D function plays an important role in enabling a continuous, connected and personalized learning ecosystem, success lies in our ability to embrace automation and AI to deliver on the promise of the future.

One of the key causes of resistance to AI and automation is the fear that they will displace worker expertise. Despite initial concerns, knowledge-intensive industries, like supply chain management, that have embraced AI and automation have not eliminated human workers but have heightened their impact. The reality is that AI and automation will not take over our work — they will alter the nature of the work we do for the benefit of the entire system.

Organizations will benefit from the reduction in cost and effort by reallocating humans from routine and repetitive tasks and challenging them to design and support systems that are more personalized, intelligent and effective. The real value of AI and automation is in amplifying — not replacing — human capabilities, and we must effectively map the human-AI connection in the value chain.

Over the next several years, we will see how AI and automation can enable our industry and our designers to:

  • Streamline the design and development process.
  • Reduce the complexity of the learner experience.
  • Increase the responsiveness of the learning enterprise in meeting the learner’s need.
  • Continuously measure to prove that the system is working and to improve the experience in real time.

As we turn our focus even further to the 2030 landscape, AI and automation will undoubtedly play an even greater role in the learning industry.

Hyper-personalized Journeys

AI will be a critical component in creating highly personalized learning and performance improvement experiences that flex over time and support multiple entry points. Learners will be able to personalize their learning journeys and grow their networks of coaches, peers and mentors to address their unique situations, needs and interests as they perform in the flow of work.

For example, dynamic learning routers will help learners access the most relevant experiences and content by asking them a set of questions related to the areas where they want to achieve, learn or innovate. They’ll be able to interface with these systems in a range of ways, including chatbots and wearable devices.

Learners will also be able to access these systems verbally through tools like Amazon’s Alexa and Apple’s Siri. With improvements in natural language processing (NLP), these systems are becoming more effective at delivering a personalized experience in a range of physical environments. They will also address emotional and physical needs by performing tasks like suggesting that learners take a break, switch to a different learning path when their needs have shifted, or turn to a human learning coach when they become frustrated or are struggling.

We have already seen examples of this approach in other industries, such as supply chain and video gaming. Logistics companies have blended real-time information, like traffic conditions and changing delivery points, with AI algorithms to map out optimal delivery routes. Many gaming systems now have built-in triggers to suggest that players take breaks or go for walks when they have spent an inordinate amount of time playing.

Enhanced Adaptive Learning Platforms

These platforms will continue to evolve their algorithms and grow their data sets, expanding as more organizations adopt them. They already create relevant learning pathways and promote mastery based on a range of demonstrated learner characteristics, including knowledge, confidence, resilience and curiosity, but they will progress to integrate live and virtual learning coaches and mentors to increase the personalization of the experience.

Intelligent Learning and Performance Improvement Search Engines

Organizations will finally be able to deliver an internal search experience that yields actual value for learners. AI and automation, supported by humans, will scan content across an organization’s federated data silos. As they do so, they will identify relevant assets and connections and tag them for future retrieval.

These engines will not only provide access to information and content, but they will also include insights from human networks, links to established experts and peer communities, and access to relevant coaches available for micro-coaching opportunities. These intelligent search engines will feed into other systems, creating personalized learning platforms and design and development tools for learning system architects.

AI-supported Instructional Design and Development Tools

Learning designers and architects will be able to move up the value chain and more effectively design personalized learning journeys that evolve and support multiple moments of need. They will have increased access to relevant source content; insights from human networks, established experts and peer communities; and coaches. These resources will all be available for the learning architect to efficiently and effectively use to create sophisticated learning journeys.

Learning architects will also have access to recommended architectures for a range of learning outcomes and structures to meet a variety of needs. AI-enabled feedback will help designers craft the experience and learners complete microlearning courses. With advances in NLP, these tools will become more accurate and authentic, which will increase learner, designer and facilitator adoption.

Human-AI Hybrid Micro-coaching Systems

These systems will be able to meet a range of needs, including learner engagement, emotional and physical needs; performance evolution; and career planning. These dynamic systems will draw from a broad range of communities to identify coaches and peers, who will provide relevant, structured micro-coaching interactions based on their shared insights, growing experience base and personal learning journey. Additionally, the AI-driven system will be able to wrap the interaction with relevant assets to extend and enhance the coaching experience.

Although we have identified changes from the learning experience perspective, it is also important to explore three new roles that learning professionals — in their co-performance with AI systems — will need to perform. They focus on how designers will work directly with AI to optimize the learning ecosystem:

  • Training AI-enhanced learning systems, which includes helping the AI system evolve and improve (For example, enabling natural language processors to make fewer errors and improving AI systems’ emulation of human behaviors).
  • Analyzing outputs from AI-enhanced learning systems to ensure accountability for AI decision-making processes.
  • Maintaining AI-enhanced learning systems, continuously endeavoring to identify and remove bias from the system and ensure that it is meeting its goals.

Learning professionals will need to adjust to work effectively with a range of intelligent technologies, from robots to virtual agents. The learning architect role will look more like a digital learning data scientist who can manage learning models, evolve algorithms, and train automated design and feedback systems. The importance of strong analytical skills will fuel the demand for learning architects with a digital learning data skill set.

In many ways, the L&D industry is facing a similar path that the supply chain management discipline has already taken. At one time, supply chain management was a human-centric activity that focused on the location of warehouses, the price of gas, shipping routes and union relations. Then, companies like Amazon and Wal-Mart changed the application of supply chain through the innovative use of data and intelligent systems. The nature of work in the supply chain industry has changed, and, as a result, the humans in the system evolved their roles, skill sets and mindsets to be effective.

In the L&D industry, AI and automation will fundamentally change the work we do. Our key to remaining relevant as professionals will be to embrace the technologies. Our key to remaining relevant as professionals will be to embrace the technologies. We need to create a new normal, where learning professionals and machines don’t just coexist but collaborate to create a better experience for our learners and yield better outcomes for our business.