In our series of articles focusing on automation and AI, we provided a general overview, “Automation and AI will Advance — and Challenge — Learning Leadership.” This included an introduction to the top three areas where we see automation and AI revolutionizing the way in which successful L&D teams work and deliver on a mission growing in responsibility and expectation. For us, the key areas are: asset libraries and knowledge hubs; hyper-personalized, truly adaptive learning; and last but not least, capability mapping.

In this final piece in the series, we look at how AI empowers organizations by creating detailed capability mappings and learning needs analyses. AI provides the ability to map capability and skill across an organization, at scale and as required, fully aligned with the organization’s unique set of skills frameworks and competency matrices.

Capability maps provide keystone data for personalized training

The most fundamental pillar for successful learning and performance improvement is access to key learner information: Information such as what an individual already knows and is capable of, what knowledge and skills they lack, where their strengths and weaknesses lie, where the boundaries of competence are in each area of expertise, and where they ideally need to be.

This is by no means new; on the contrary, it is with this information, collated diligently over time, that a good personal tutor or trainer works with a person on a one-to-one basis. This works so well because it is a tailored, personalized approach for each individual instead of a one-size-fits-all approach aiming at the “average” learner and suiting no one.

Applying what works so well in a one-to-one environment between an expert and a learner has been elusive in corporate training since this approach is simply not scalable. It is not practical for the scale of skills and competencies in large organizations, and it isn’t available for the number of employees often distributed across all continents.

However, provided one did have this information as a snapshot in time—both a competence and a learning needs analysis—and with the right resources at hand, learning leaders could deliver for each learner hyper-relevant content, at the right time, in the right format. And they could do this at scale.

Suddenly, a fully personalized vision of reskilling, upskilling, talent and leadership development, professional and personal development, as well as individual employee growth opportunities is no longer a dream. In addition, a new era of AI-driven search functionality and contextualization will further drastically improve the ability of those looking for answers and information in the flow of work to find exactly what they are looking for.

Building blocks: AI contextualization and adaptive learning

In order to see how this works, we need to briefly review the two building blocks of capability which AI now provides for organizations, allowing us to deliver on the above promise.

Contextualizing content

We explored how AI contextualization engines are able to contextualize and map out everything, from individual subsets of a digital course to an organization’s entire stack of learning libraries, in “Boosting the Usability of Libraries & Knowledge Hubs with Automation.” This would not be restricted to content specifically designed for learning, but includes all digital assets that an organization produces—from company reports to presentations to product descriptions, manuals, and more.

A key point to note here is that contextualization does not merely mean to map out topics and areas in a way similar to the tagging that used to be done by hand. This new, AI-powered and automatic contextualization includes what we like to call the full “complexity tree” of the content.

This means the AI gains an understanding of how individual learning bites are related to one another, the breadth and depth of the content and concept coverage, and other useful things such as identifying the exact prerequisites for pieces of content and modules, i.e., the chapters or modules one should have covered before trying to tackle a certain module.

The AI can even identify content gaps; this works in a similar way to the way we as humans would identify very quickly if a language learning textbook we were working with was missing a chapter. Too many concepts would appear out of the blue, at once, and we would quickly realize that a logical connection between the last chapter and the current chapter seemed to be missing. The AI does basically just that.

Last but not least, the AI can map the complexity tree generally—or in any way desired. This could be, for example, mapping to any set of skills matrices, competency frameworks, or personal development plans, backward and forward, lightning fast. This is digital transformation in action, at speed and at scale.


We subsequently explored how AI adaptive learning engines would deliver a hyper-personalized learning journey for each individual user, at scale, and how continuous testing and assessment would automatically deliver a real-time picture of an individual’s competence and confidence, and thus progress, across the relevant complexity tree of the individual’s journey in “AI Empowers Scalable Personalized Learning and Knowledge Sharing.”

A map to the future

With the aforementioned building blocks of AI contextualization engines and adaptive learning engines, we have everything in place. A learning journey or course is now merely a subset of the complexity tree of all the learning libraries and digital assets within an organization. Therefore, an individual’s learning progress, strengths, and weaknesses can be mapped back to the complexity tree, or back to a set of skills matrices, competency frameworks, or development plans—or both—and of course this can be done for individuals, teams, regions, or an entire organization. Merged with performance metrics for individuals and teams, we are looking at a fully automated, purely data-driven capability mapping of an entire organization via its individuals and teams.

Mapping existing capabilities and using this data, alongside data about skills and knowledge required in roles at all levels across an organization, will empower learning leaders to keep the organization up to date and to prepare for the future. In a world where change is accelerating, improving performance through knowledge and skills is a key demand on organizations and individuals alike.

Performance metrics together with competency mappings will form the foundation of future organizational learning and performance strategy; it will also inform key business units such as HR and talent development, and guide hiring strategies.

The positive impact on hiring strategy here will be huge, particularly where a specific set of knowledge and skills is required to fill a role or gap, as it will enable learning leaders to answer this critical question: Can a specific hiring need or short-term challenge be addressed by deploying or developing someone who already works within the organization, or does the need require a hiring process?

If someone with the right set of skills and competencies can be identified, then a solution might be at hand. Should a hiring process be required, this hiring process can now kick off with a very specific set of skills and competencies identified for the role through competency data as well as performance metrics, all readily available. Added together, these impacts will enable organization to future-proof their workforce and increase its ability to innovate, grow, and excel.

There’s more!

However, there’s more good news, as these AI capabilities will have an impact beyond strategic decisions and leadership teams when it comes to capability mapping. Through access to the relevant data and content, teams and individual performers will increasingly create and curate learning experiences together. Highly agile individuals and teams will become proactive as well as reactive. The new agility could even make it easier for subject matter experts and instructional designers to identify and fill weaknesses or gaps in content coverage.

In effect, AI-powered capability mapping will revolutionize involvement in performance development across an organization, for teams as well as individuals. These will, in turn, participate proactively in planning and deploying learning and training, workshops, coaching and mentoring both for themselves and for their teams.

AI offers a collaborative—and personalized—future

Tapping into the potential of AI means that instead of learning programs being created in silos and dispersed through content libraries, organizations can utilize the potential of performance improvement program co-creation. The old “Netflix of learning” approach—building a learning library so full of one-size-fits-all courses that, surely, there will be something in there for everyone— will be replaced by purposefully created and curated, fully adaptive and personalized learning experiences based on and driven by relevant and meaningful learning and performance data.

Teams will be empowered to drive and contribute to their own learning and performance utilizing the whole suite of strategies, from social learning and sharing of good practice and expertise to strategic interventions and training.

Get a full picture of the ways AI could impact learning leaders

Don't miss the other articles in our series on AI and its impact on learning strategy:

Join us in person to learn more about AI and learning: Author Markus Bernhardt will present "Exploring the Growing Role of AI in Learning, and Why We Need It" at DevLearn 2022 Conference & Expo, October 26–28, in Las Vegas. Register today!