The Role of AI in Corporate Learning

Generative artificial intelligence has taken the world by storm. It attracts a huge amount of venture capital and pushes up the market valuation of any organisation connected with AI. The current hype also raises questions about its long-term impact on the world of work. There are a lot of projections out there, from dystopian to utopian, that all anticipate enormous change. Hence, we would like to explore here the role of Corporate Learning in facilitating AI-driven corporate and workforce transformation.

As depicted in Figure 1, we see two forces at play in today’s corporate world, rooted in both the industrial as well as the postindustrial era. In the industrial context of the 20th century with clearly defined repetitive processes, robots and algorithms will increasingly perform tasks that were previously accomplished by humans. In the knowledge era of the 21st century, humans will augment their intelligence and propel their productivity with the help of AI.

This could lead, in the remainder of this decade, to significant gains in labour productivity. However, only under the condition of a highly adaptable labour market with flexibility on behalf of all stakeholders: governments, employers, unions, workers, and the education sector.

As with any new technology, some observers focus on the risks, whereas others emphasise the opportunities. We must decide if we declare AI as our friend, being aware of the inherent risks, or if we try to ban it as our enemy. Only a critical-constructive approach will allow us to realise the underlying potential of AI technology i.e., we must allow for experimentation, whilst monitoring outcomes to inform regulation.

Corporate Learning should, in our view, address four action areas (see Figure 2):

Firstly, AI can significantly improve the efficiency and effectiveness of learning practices and the learner experience. It is a must do for L&D to keep their own function competitive.

However, learning functions must look beyond their immediate remit to remain an integral part of value creation and hence, embrace the following other three areas:

Secondly, with AI transformation, repetitive tasks will get automated, provoking a significant demand for up- and reskilling that Corporate Learning must anticipate and manage.

Thirdly, to realise the power of AI to augment knowledge worker productivity and improve user experience, AI literacy must be ramped up across the board.

Lastly, Corporate Learning should contribute to the organisation’s deployment of an ethical framework and public discourse on the responsible use of AI.

AI applications in L&D

When it comes to the many forecasts of AI applications in the Human Resources domain, learning and development is regularly put in the front seat.

EFMD and HEC Paris recently teamed up to run a workshop for L&D executives with the provocative title Will AI give us superpowers, or will it replace us? Among others, four companies presented an AI use case. Forvia’s use of an AI powered recruiting tool has significantly increased efficiency and effectiveness of its talent acquisition practice, leading it to now explore its use in internal staffing and mobility. dsm firmenich trained its learning community in using Chat GPT to develop personalised learning journeys for specific skills, and for them to then teach the broader workforce. Intercorp Group can now consider any feedback from learners in refreshing its programmes and sharply reduce the time designing learning programmes, by using a variety of AI tools, including Chat GPT. And Schneider Electric shared their use of AI to support new habit adoption through nudging e.g., in their efforts to empower women to rise into managerial ranks.

An ever-increasing number of published use cases show that AI can be a game changer along the entire learning value chain. Future skills and respective learning needs can be anticipated early on. The more skills databases are properly populated with quality data, the better.

The design of learning programmes and learning journeys can be accelerated dramatically, down to a very personalised level. When using a learning experience platform with many digital learning assets, curated by L&D, AI can fully personalise the learning environment, given a person’s role, peer group, current and desired future skills and learning preferences. Chatbots can provide instant support and improve learner experience. With increased digitalisation, AI can seek learner feedback and monitor learner activity which again should drive the impact of learning.

The opportunity space for AI applications is similarly large when it comes to assessment, performance management, DEI, succession management, talent analytics, engagement, wellness, etc. It is high time for learning leaders to invest time and provide opportunity for their teams to experiment with, and build confidence in, the proper use of generative AI tools such as OpenAI’s Chat GPT, or Google’s Bard, or Microsoft’s Bing, in addition to using talent-specific AI-powered tools.

Up- and reskilling

The skills required to complete tasks that get automated will become obsolete. At the same time, humanistic skills (empathy, teamwork, critical thinking, judgement, curiosity, etc.) and digital skills will continue to gain in importance. The ability to co-pilot with AI and augment own’s own productivity will become most important. This workforce transformation has been going on for a while but will further accelerate with AI adoption. Skills will become the preferred unit of analysis, thereby complementing, or even replacing jobs as the planning unit in strategic workforce planning.

Unemployment rates are down, and the tight labour markets will become even tighter with the upcoming retirement of the baby boomer generation. Recruitment will not be the solution to acquire new skills alone. Retention of talent will become essential, even when significant investments in up- and reskilling are required. This also positions AI as our friend, and not the ‘enemy who kills my job’.

Workers in lower-wage jobs (and women) are much more likely to need a change of occupation. Thus, workforce transformation will also become a social and DEI issue. Corporate Learning must get prepared for this up- and reskilling challenge, through specific learning journeys and badges, part-time degrees and certificates, or talent innovations such as an internal talent marketplace, and dedicated multiyear workforce transformation funds.

AI literacy

Several studies show that HR and L&D are not yet at the AI strategy table which, in our eyes, is a huge mistake: AI transformation is more of a people than a technology challenge.

AI literacy can be systematically developed by self- and peer learning toolkits or by cascading a team-based AI exploration journey throughout the organisation whereby teams are assigned with creating an AI use case, combined with reflecting ethical considerations (more about that below).

Corporate Learning could also run a series of AI workshops in all businesses and functions with the aim to identify automation and augmentation opportunities that could then be aggregated and form the basis of an AI implementation roadmap. The prize is high, and productivity gains of 15% and more are being anticipated.

The most obvious areas for AI application, across industries, are customer service and personalised marketing for improved user experience. Recognising patterns and weak signals in huge amounts of data is the other ubiquitous application. And then, any knowledge worker should be able to augment his or her productivity by smart and responsible use of generative AI. A well-orchestrated AI literacy initiative, in combination with an AI roadmap buildup, should be, in many cases, the best approach to realise the potential of AI.

AI ethical framework

AI is highly disputed because it comes with many risks that we need to learn to properly manage. There are privacy concerns about data usage, and cybersecurity finally gains the importance it should have had years ago.

AI tools get trained on as broad a range of data sets as possible. However, these data should represent the desired future state, but often enough, represent only the past. Hence, the tool might provide answers and recommendations that are biased toward past realities.

In addition, most AI tools don’t explain how they came to their conclusions, and even the engineers of these tools can’t explain. Occasionally, AI tools tend to hallucinate i.e., make things up which are not true.

Therefore, we need to understand the frontier between automation and augmentation. This frontier is dynamic and might allow for more automation in the years to come. However, we should only automate processes where risks are clearly understood and managed.

Organisations are well advised to draft, clearly communicate, and follow through on Responsible AI Use Policies that leave the accountability for AI use with the user. Augmentation stands for using a co-pilot for the enhancement of one’s capability and productivity. Any piece of work that uses AI must be owned by the user, and AI use must be made transparent. The more the use is demystified, the better for its transparency.

Conclusion

AI is anticipated to clearly accelerate labour productivity, which has been flat for many years, and to enhance user experience. As with any new technology, there are opportunities and risks, and all kinds of fantasies about its impact. Only an AI-literate workforce knowing its limitations and risks, will reap the successful harvest of an AI dividend. And only an organisation that generously facilitates up- and reskilling and expects a responsible use of AI will create a climate where AI is regarded as a friend. This provides Corporate Learning with vast opportunities to facilitate and add value to AI transformations.

this article is based on a presentation at HEC Paris on 25 September 2023 and will be published in Global Focus Vol. 18 Iss. 1 (Jan 2024)