AI Literacy and the GARTNER’s “Trough for Disillusionment.”

By Luca Collina

Introduction

I am pleased to have completed the course “Instructional Design Foundations and Applications” offered by Illinois University at Coursera. This course, among other things, revealed that literacy’s key objective is vital for a learning program.

AI literacy?

The first thing I noticed was that there is a little bit of confusion about the following definitions:

AI literacy / Ai education / Ai training.

AI literacy consists of AI education and AI training. AI education is typically about AI theory – e.g., methods and problem-solving processes via computer-based tools that emulate human cognitive ability; on the other hand, AI training focuses on applying what one has learned so far to solve practical problems of everyday life or achieve specific goals. The importance of executives seeking excellence in AI and leadership becomes evident. Today’s programs might not be entirely fulfilling executives’ need¹s.

The recent survey by the European Business Review (EBR)² reveals an increasing need for flexible executive education programs. Based on this requirement, future programs will likely incorporate deeper education about artificial intelligence and machine learning to improve learning (practice) outcomes ( i.e. Decision-making).

Another study conducted by TEBR³ (The European Business Review) also puts forward the following findings:

1. Personalised training and development: Increasingly, those who enrol for executive courses prefer a program tailored to their Thus, institutions are being encouraged to design more tailored programs to meet this requirement.⁴
2. Experience-Based Learning: Easy access to applications such as simulations, projects among others, has also increased executives’ interest in practical training rather than just theory.⁵

The Coursera Job Skills of 2024 Report⁶ points to the growing urgency of AI skills in the business world. The issue is that, as they matter most, a significant variance is visible in the effectiveness of executive courses on deploying AI. This variance demands a kind of learning under which theoretical knowledge combines with business in a way that develops AI literacy.⁷

In a related vein, Gartner⁸ pointedly said that leaders must learn the distinctions of AIs. ‘Everyday AI covers productivity improvements (I have called it Business as Usual). In contrast, ‘Game-Changing AI’ — which I have called Transform AI — covers innovation (product and business model), the latter of which leaders must learn about. This is another area that has been considered in the development of the course program.

A gap analysis

While programs like those offered by qualified providers give foundational and intermediate knowledge in leadership and AI, they fall short of addressing the practical application of AI in strategic decision-making. For example, the Job Skills of 2024 Report⁹ reveals that while AI skills are in high demand, only a fraction of executives feel adequately prepared to implement AI in their organisations. This gap indicates that, though valuable, existing courses may lack the depth or specificity needed for advanced business leaders.¹⁰

If we evaluate the explosion of the course opportunities offered so far, it can be said that they were supporting executives, swimming in the hype waves. From technical courses for development and programming to explanations for machine learning, AI, and, recently, Gen-AI. Plus, organisational approaches for adoption and implementation. Reminding (only) the connections with business goals, ROI, and other business-related links to innovative technology.

A new phase in the GARTNER Hype cycle confirms that a NEW AI literacy is required.¹¹

In this infographic, GARTNER Hype Cycle shows that Gen Ai is beyond the hype (finally) and has started the Trough of Disillusionment.¹²

GARTNER shows us that new technologies in this period generally are grown but need to be more stable. Let’s see more detailed cases related to possible companies’ behaviour:

• Disillusionment and Abandonment: Those unhappy would rather not use it at all. They may return to what they did before or wait for better options.
• Cautious Optimism: others might reduce their reliance on technology while keeping the benefits they get from it. They look for progress observed before a full commitment is made.
• Demand for Support and Clarification: Other unhappy customers may require further help and less biased personnel. Before investing more, they may also want concrete examples of successful cases.
• Continued Experimentation: In the end, some will still insist on using it for exploration or research purposes (even after the hype has hurt them), mainly those who initiated its use. In this case, they keep refining its use in close collaboration with its developers.¹³

New AI literacy-Why?

The different behaviours during this phase also bring different needs for AI Literacy.

Businesses that need a cooling moment to reflect upon and be supported in clarifications to then move to continuous experimentation, while those companies that continue to experiment and, luckily, proceed with AI solution activations are more likely to need to connect TECH more effectively with the business aspects that should benefit from AI adoption. This is also because they can speed up the implementation and activation of AI features.

How AI and Skills Growth Is Linked to the Trough of Disillusionment

The Gartner Hype Cycle’s concept of the “trough of disillusionment” is necessary in understanding why companies go through both dips and peaks when they are implementing certain technologies such as AI. This stage is characterised by the hype fading away and limitations of a technology coming into play, creating frustration and disillusionment in most cases. At this point, businesses may decide to stop using that technology, continue using it cautiously or solicit additional assistance to exploit its full potential. It, therefore, serves as an important time in the business cycle for organisations to invest in staff-training programs that would drive a higher-level understanding of AI. During this period an organisation may choose to invest in courses focused on advanced AI techniques to ensure that they keep abreast with the changes in technology.¹⁴

AI Literacy and Instructional Design

Looking back over the course materials, especially focusing on sections that pertain to instructional theories and models, there seems to be much stress laid on creating systematic well-structured learning experiences meant to foster both concept comprehension skills as well as practical application proficiency. Instructional methods such as behaviourism, cognitivism as well as constructivism are very critical in the design of a course for companies that are in this trough of disillusionment phase.

Targeting AI Capability Growth During the Trough of Disillusionment

Critical elements for inclusion in a course designed for this period include:

• Behaviourist Approaches: Focus on clear, measurable outcomes that help executives and employees understand AI concepts (e.g., defining what AI can realistically achieve) and develop specific skills through repetition and reinforcement.^15 16
• Cognitivist Strategies: Integrate methods that help learners connect new AI knowledge to existing business practices, aiding in the internalisation of AI principles and the strategic application of AI in decision-making processes.¹⁷
• Constructivist Techniques: Encourage experiential learning through simulations, real- world projects, and collaborative problem-solving exercises, ensuring that executives can apply AI insights directly to their specific business contexts.¹⁸

Summing up

Moving Beyond the Trough of Disillusionment

For these companies to cope with AI adoption-related problems like a trough of disillusionment, there’s an urgent need to create an entirely new AI literacy, which goes hand in hand with practical business acumen. Through AI educational programs that focus on theory and practice and executives’ and senior managers’ needs, organisations undergoing disillusionment can eventually reinvent themselves as they move to the next steps in AI progression.

About the Author

Luca Collina is a transformational and AI Business consultant at TRANSFORAGE TCA LTD. York St John University awarded him the Business – Postgraduate Programme Prize and CMCE (Centre for Management Consulting Excellence-UK) for his paper in Technology and Consulting Research Prize. Author/External Collaborator of CMCE. 

References

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2. TEBR Survey Report Part 2-Navigating Executive Education Preferences and Needs in Contemporary Leadership
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