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IMD AI Strategy

Artificial Intelligence

Harnessing AI to build expertise 

Published 10 May 2024 in Artificial Intelligence • 7 min read

Sarah Toms explains how IMD is balancing risk and opportunity as it seeks to capitalize on AI .

Suddenly, generative artificial intelligence (GenAI) is everywhere. But while organizations are thinking hard about the advantages the technology might bring, many are also cautious. They’re unsure about where to start, unnerved by stories of others’ mis-steps, and anxious to proceed in a way that reflects their ethics and values. 

A double-edged sword

Such caution is understandable. GenAI offers transformational opportunities: it can deliver a myriad of gains in productivity, creativity and innovation. But there are also real concerns. The mechanics of GenAI are yet to be fully understood and there is still a significant possibility of bias and error.

These are dilemmas that IMD is grappling with. In the education domain, GenAI is already supporting richer and broader experiences for learners through hyper-personalization and interactivity. But to lean into this latest tech frontier without due care and attention would be irresponsible.

The reality is that GenAI excels at some tasks but not others. Areas where it outperforms include content generation, data analysis and pattern recognition, simulation and modelling, personalization, automation, and translation. There is the potential to innovate, enhance research and development, and to solve complex problems.

By contrast, GenAI often struggles to understand context, nuance, and ambiguous questions. It lacks emotional intelligence and empathy, and cannot be relied upon to adhere to commonly agreed ethical and moral standards.

In the worst cases, GenAI “hallucinates” – generating responses that are wrong, invented or even nonsensical. It is also crucial to be aware of the risk of bias, particularly given the way that most GenAI models are trained. There have been many examples of GenAI showing gender, race, ethnicity, and socio-economic bias. Confirmation bias is also a common problem, and many models appear to over-emphasize recent trends and events at the expense of long-term context.

IMD’s approach to harnessing the undoubted potential of GenAI has been to proceed carefully. We have sought to move forward in four carefully considered steps, a framework that would work effectively for many other organizations.

“IMD also adopted 10 overarching principles to govern its use of AI.”

# Step 1: Achieving strategic alignment through governance and principles

IMD’s priority was to build a framework that would allow the organization to pursue GenAI implementation in a coherent and compliant fashion.

IMD has an AI steering committee with a clearly articulated charter and mandate. The committee’s remit is to lead, oversee and ensure the successful integration and ethical use of AI, in line with IMD’s innovation strategy. It reviews and approves or rejects project proposals for AI-enabled learning innovations, as well as setting priorities and focal areas for IMD’s AI workstreams. The committee is also responsible for ensuring that communications with staff and students are transparent.

At the same time, IMD also adopted 10 overarching principles to govern its use of AI. These principles ensure that the organization’s use of AI will be:

  • Ethical and responsible – AI users will consider potential impact on individuals and society.
  • Transparent and explainable – motivation for decisions taken by IMD’s AI systems should be explicit.
  • Inclusive – IMD will promote fair and unbiased AI systems that avoid discrimination and promote diversity and inclusion.
  • Secure and private – IMD will prioritize security, protecting its AI systems and data against unauthorized access and cyberthreat. 
  • Human-centric – AI-enabled teaching systems will enhance human potential and wellbeing, rather than replacing or undermining human employees.
  • Collaborative – IMD will seek partnerships in AI to develop and promote knowledge-sharing and co-operation among both internal and external stakeholders.
  • Continuously improving – IMD will re-evaluate its AI systems and strategies on an ongoing basis and adapt to new technologies and changing circumstances. 
  • Building and buying – IMD will use commercially available AI offerings where appropriate, developing its own specialist capabilities to drive strategic advantage.
  • Sustainable – IMD will aim to shrink the carbon footprint of technology use and other human activity required to deliver AI innovations.
  • Brave – IMD will seek to pioneer AI innovations, aiming to challenge and inspire. 

# Step 2: Developing a clear AI strategy

With this type of governance structure in place, organizations can begin to move forward with AI innovation. But it makes sense to identify and articulate priorities for innovation.

In IMD’s case, the organization set itself a learning innovation mission. It targeted pedagogical innovation in order to have a transformative impact on individuals, organizations and society as a whole.

That mission statement provides a lens through which to analyze and evaluate opportunities for AI. The opportunity for IMD in this regard lies in areas where education is currently limited – and where those limits can be overcome in ways that drive personalization, interactivity and engagement.

Time is an obvious limiting factor in education: how long the class lasts, how much time individual learners have face time with faculty to ask specific questions. Application of classroom topics into personal context is a second limitation: learning should be aligned with the learners’ real world interests and needs; be available in the appropriate language; and the learning support should be accessible as required.

These two limitations prompted IMD to think about how AI could enable learners to tap into specifically what was covered in class, as well as the combined knowledge and experience of IMD faculty and research centers at any time during and after the class. This innovation is called the Program ChatGPT capability.

IMD’s AI approach to this challenge is based on contextualizing its corpus of expertise in the form of thousands of articles and publications made available to an AI application built on ChatGPT.

Rather than a general-purpose AI, the IMD Expert AI constitutes a central AI offering that can be rolled out across degree and non-degree programs, and de-siloing its thought leadership. IMD Expert AI is technology-agnostic, GDPR-compliant, and built to provide flexibility, security and trust.

IMD’s implementation of ChatGPT tools in its Orchestrating Winning Performance program recently took gold in the Best Advance in Emerging Learning Technology category at the Brandon Hall Tech Awards.

# Step 3: Starting small to test and learn

IMD’s approach has been to prioritize agility. In action, this means starting out with small pilot projects, testing and learning, and then scaling.

These projects can take shape very quickly. For example, IMD successfully launched both its “Program ChatGPT” and “AI Art Gallery” initiatives in six weeks.

Indeed, these GenAI innovations have picked up awards. IMD’s implementation of ChatGPT tools in its Orchestrating Winning Performance program recently took gold in the Best Advance in Emerging Learning Technology category at the Brandon Hall Tech Awards.

Students and AI
“In time, IMD is confident that it will be able to offer every learner the opportunity to enhance their education through AI.”

# Step 4: Scaling for maximum impact

Successful pilot projects and early-stage innovation provide organizations with the confidence to step up their AI activity, as well as applying critical learnings about what works well and what is less effective. The goal then becomes to extend the depth and reach of AI innovation in order to maximize its transformational impact.

In IMD’s case, that means scaling AI-enablement to the organization’s entire MBA, EMBA and open-enrolment programs. While the scale of early pilots is taking flight, IMD has embarked upon creating new GenAI pilots to test the waters of other ways to drive personalization and immersive, experiential learning.

In time, IMD is confident that it will be able to offer every learner the opportunity to enhance their education through AI.


Sarah Toms

Sarah E. Toms

Chief Learning Innovation Officer

Sarah Toms is Chief Learning Innovation Officer at IMD where she leads the Learning Innovation and AI strategy. Sarah previously co-founded Wharton Interactive, an initiative at the Wharton School that has scaled globally. A demonstrated thought leader in the educational technology field, she is fueled by a passion to find and develop innovative ways to make every learning environment active, engaging, more meaningful, and learner-centric. Sarah is an AWS Education Champion, and has been on the Executive Committee of Reimagine Education for 8 years. She has spent more than 25 years working at the bleeding edge of technology, and was an entrepreneur for over a decade, founding companies that built global CRM, product development, productivity management, and financial systems. In addition, Sarah is coauthor of The Customer Centricity Playbook, the Digital Book Awards 2019 Best Business Book.


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