Improving executive education programs through generative AI

With so much of the news coverage concerning artificial intelligence focused on perceived threats and fears, it is easy to lose sight of the immense benefits currently being unlocked. ‘Good news' stories are hard to come by, yet the world of executive learning is certainly offering one. Generative AI - creative AI able to produce data, text, and images - is very soon to make a significant contribution to the improvement of executive development, particularly through experiential action learning.

This view is confirmed by Gilles Basset, Director of custom-training programs at emlyon business school—a school that has been at the forefront in the deployment of ‘action learning' as a method of delivering transformational executive development—learning able to influence real workplace outcomes. Although the AI technology is new to the sector, its influence is growing rapidly, and new opportunities for enhancing corporate learning are being created month-on-month.

“The first challenge we have is to clarify definitions,” says Basset, “the opportunity we have is limited to generative AI. Not to be confused with AI in a global sense—that is the global AI that speaks of changing processes in supply chains, global sales, marketing, finance, and so on.” Generative AI has the potential to change the way people work day-to-day, “to explore new ideas and opportunities, to create new design, and to reshape our organizations.” Business schools offer an advantage in this area, as Basset points out, “an executive education program is a great environment to create new things. It allows executives to experiment, take risks, and learn about AI.”

Generative AI, as an invaluable tool for experimentation, fits perfectly with action learning, a methodology based on a continuous cycle of ‘doing' and ‘reflecting' to reach solutions. Action learning programs are based on the idea that to increase their impact as transformational leaders, executives need to learn by living the theory in real working environments. This is an antidote to the now well-understood ‘knowing-doing' gap whereby executive program participants receive knowledge, far removed from the daily challenges of their workplace by distance and time, and then struggle to apply that knowledge when they get back there. As Basset explains, “Action learning is a way for learners to assimilate ‘know-how,' as opposed to just knowledge, and to do this through actions.” Using case study scenarios, action learning is a “very effective way to explore, understand, and tackle real problems.”

Generative AI has the exciting potential to be able to accelerate the action learning process. It can create prospective scenarios and problems to solve and, by monitoring how each individual learner responds, tailoring their learning journey as they go. In line with the ‘doing' and ‘reflecting' cycle, “it can bring new possibilities, and new ideas to find new solutions. You can employ AI tools to help develop a project which participants then work on. As instructors we can monitor the project as it progresses, occasionally adding direction, coaching, or guidance,” explains Basset. For participants, this new way of working provides a unique challenge, described by Basset as, “Working together in teams, but working with a new member of the team, the AI - a companion that enables them to accelerate their learning, to be creative, to be more efficient, and then at the end to improve their performance.”

The ability of AI to support in-depth data monitoring can reveal strengths and weak spots in an individual learners' understanding and capability, and with this data it can develop or redirect the project to support them. This might, for example, be used to help strengthen their strategic thinking around multiple factors and systems that affect the company—to help a marketing executive understand financial issues or an engineer appreciate HR issues. Basset adds that, “You can use generative AI to resolve contradictory ideas, to design things, for ideation, for scenario-planning, and you can use AI to test and learn about the market and the consumer. By using AI in these different ways, you can notice where people have some trouble with a certain skill or competency, and AI can be there to support them.”

Another area where AI looks set to play a significant role is in monitoring and measuring the impact of training programs, both for the individual and for the organization. Currently Basset believes this is done well at emlyon—but AI can help. “We monitor things by compiling data with the individual learner about competencies at the beginning of a program and then again at the end to measure the gap between both. If we collect a big range of data, AI can easily see things differently and more quickly than humans. So, it's a way to do the same things more rapidly.”

With detailed rapid monitoring of competencies throughout a program AI can dentify which competencies a learner is developing well and adjust accordingly to place less emphasis on these. “Modifying scenarios, adjusting content, changing competencies, putting forward new scenarios. You can say we are not monitoring impact, but we are working on continuous development,” asserts Basset.

Asked how far down the line emlyon business school is in using AI to enhance its programs, Basset replies, “We are just beginning. We are proposing some opportunities to some companies, to use generative AI with teams participating in programs. To include generative AI in the way to start with the project ideation they will have to cope with during the program.”

As far as the tools Basset's team are using, “We already have some specific tools in mediation, strategic thinking, decision making, storytelling and so on. We are not yet sure which tools exist for creating programs, then monitoring things, and changing the scenarios during the program.” For the staff responsible for creating the programs, the support tools are not yet specifically identified. They may simply be integrated into the standard tools.

It is very early days, but it is clear that action learning programs will benefit significantly from the capabilities generative AI can offer. It is also clear that, due to the co-creation aspect in the design of these programs, that the business school environment can be the ideal place for learners to experiment, take risks, and learn about the potential for AI—gaining valuable ‘know-how' in the use of the technology they can take into their work and for their organization.