- Change, IT Basics
- Behavioral Economics, Change Management, IT
- 6 min reading time
Dr. Yvonne Avaro
In this wiki, Yvonne explores—from the perspective of behavioral economics—why change is so difficult in organizations. Why don’t people take action even though they realize that change is necessary? What does a surfing wipeout in San Sebastián have to do with the implementation of AI? And what do the research findings of Kahneman, Tversky, and Kübler-Ross reveal about how communication actually works during change? With concrete success factors and a clear message: Change management is not a side issue in AI implementation—it is the primary task.
💡New here? In the FAQ section at the end of the article, you'll find answers to common questions, including: What are the most common reasons why AI adoption fails in companies? How long does it take? And what specific steps can executives take?
Table of contents
1. Introduction: The wave is coming, whether you're ready or not
San Sebastián, Summer 2010: It's a wonderful morning. A pleasant 25 degrees. Barely any wind. The surfing conditions are perfect.
I'm standing on the beach, watching the waves. All around me, everyone is getting ready. They grab their boards, paddle out, and ride the waves. It looks so easy. So natural.
So I grab a board, too, and paddle out. And then it comes: a huge wave rolling straight toward me and sweeping me away. A total wipeout.
This image captures remarkably well the feeling many organizations have when introducing AI. The technology is here. Expectations are high. Many people are standing on the shore, thinking, “I want to ride the AI wave, but I don’t know how.”
I learned one thing that morning in San Sebastián: It takes more than just talent and a good sense of balance. You also need someone who knows the water, who can explain how the waves break and what the currents are like, and who can tell you when to paddle and when it’s better to save your energy for a perfect takeoff.
And the same is true of change within companies. No one can successfully navigate an AI transformation alone. It takes someone who knows the terrain, who can explain how the change will unfold, when the right time to act is, and when it’s wiser to bide one’s time first.
2. Why Change Is So Hard—and What Really Helps
The human brain is not a neutral decision-making machine. It is a survival system that has been optimized over thousands of years to conserve energy and avoid risks. In their groundbreaking research on behavioral economics, Daniel Kahneman and Amos Tversky have shown that people make decisions that are systematically irrational—not out of stupidity, but because of deeply ingrained cognitive patterns. Three of these are particularly relevant in the context of change:
2.1. Status-quo Bias
People prefer the status quo, even when a change would objectively be better. The new option doesn’t just have to be good; it has to be significantly better to overcome the psychological barrier to change. This effect was first described by Samuelson and Zeckhauser (1988) and further explored by Kahneman, Knetsch, and Thaler (1991): https://www.aeaweb.org/articles?id=10.1257%2Fjep.5.1.193
2.2. Loss Aversion
Kahneman and Tversky have shown that, psychologically, the pain of a loss weighs roughly twice as heavily as the joy of an equivalent gain. Anyone who communicates only the benefits during a change process is therefore ignoring half of human decision-making logic. People first ask themselves, “What will I lose?” rather than “What will I gain?” (Kahneman & Tversky (1979): https://www.econometricsociety.org/publications/econometrica/1979/03/01/prospect-theory-analysis-decision-under-risk
2.3. Hyperbolic Discounting
People tend to place a high value on immediate benefits and underestimate future benefits. As a result, an AI tool that will make work easier in six months loses out to the familiar Excel process that works today, even if it is inefficient (Laibson (1997): https://academic.oup.com/qje/article-abstract/112/2/443/1870925)
Note: Hyperbolic discounting is often mentioned in connection with Kahneman and Tversky, as it is conceptually linked to Prospect Theory. However, it was primarily formalized as an independent concept by David Laibson; his influential paper “Golden Eggs and Hyperbolic Discounting” (1997) is considered a key reference.
3. What does this mean for the practical implementation of change process communication?
The evolutionary brain reacts to immediate threats: a shark, for example, triggers an immediate response. Organizational change, however, works differently. It is not a moment of shock that forces a reflexive reaction. And that’s a good thing. Fear may mobilize people in the short term, but it is not a sustainable foundation for lasting change.
What really works is empowerment.
Those who can’t swim won’t venture into open water—no matter how inspiring the message may be. Those who aren’t familiar with the current will hesitate, even if the goal sounds incredibly tempting. Sustainable change, therefore, doesn’t begin with an appeal, but with preparation: People must develop the necessary skills, understand the context, and feel confident enough to take that first step.
So it’s less about getting people to change and more about empowering them to do so. In this sense, change management is primarily about building competence, providing guidance, and fostering psychological safety.
That is why communication during change is not merely a supporting element, but a strategic tool. It must take people’s cognitive reality seriously.
Don't say, "Use AI, or you'll be left behind."
It’s better to say, “AI can make your work easier. AI skills help you grow.”
The question isn't whether people are willing to take risks. The question is whether they feel sufficiently prepared and supported to do so.
4. Side Note: The Valley of Tears: An Emotional Journey Through Change
People experiencing change typically go through an emotional journey (see Figure 1). The model, originally developed by Elisabeth Kübler-Ross to describe the grieving process, applies surprisingly well to major IT transformations or the introduction of new technologies.
It all starts with shock and denial. A new ERP system is being rolled out, an AI tool is supposed to streamline the existing process, or the familiar dashboard is being replaced. The initial reaction of many employees is disbelief. “That won’t work.” “We don’t need that.” Morale plummets even before the first training session has taken place.
Then come anger and resistance. This is the lowest point on the curve—the so-called “valley of tears.” Meetings are used as a forum for criticism. The old way is romanticized. Every flaw in the new system is documented and shared. This is not a sign of weakness or stubbornness, but a completely normal, human reaction to a loss of control.
At some point, the phase of experimentation and trial and error begins. Employees start using the new tool in small steps, often alongside the old system at first. They find their first workarounds and discover individual features that actually help them. The mood stabilizes. This is exactly where guidance is particularly important: those who receive support now will emerge from the slump more quickly.
In the end, acceptance and integration prevail. The new system has become part of everyday life. New tools are no longer perceived as a threat, but rather as resources. Often, morale even rises above its original level because new skills have been developed and work has actually become easier.
5. An Overview of Success Factors for Change
Success Factor | What that means in concrete terms |
A Clear "Why" | Communicate the purpose of the change clearly and honestly |
Early Involvement | Turn those affected into active participants as early as possible |
Highlighting Small Successes | Celebrate quick wins to build momentum |
Leaders as Role Models | Change starts at the top. Leaders must lead by example in what they expect of others |
Creating Psychological Safety | View mistakes as learning opportunities, not as failures |
Ongoing Communication | Don't even try to explain it—over and over again, in different formats |
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Read more about the factors influencing AI adoption in this wiki .
5. Conclusion: The Role of AI in the Context of Change
AI is currently one of the strongest drivers of change in companies. At the same time, it is a prime example of everything that makes change difficult:
- It is abstract and hard to grasp
- It fundamentally changes roles and responsibilities
- It creates uncertainty about one's own future at the company
That is why change management is not a secondary concern when implementing AI. It is the primary task.
Technology alone does not transform organizations. What determines the success or failure of a transformation is not the quality of the solution, but people’s ability to embrace it.
Behavioral economics offers a clear explanation for this: Cognitive patterns such as status quo bias, loss aversion, and hyperbolic discounting are not obstacles that can be overcome through better communication. They are structural features of human decision-making that must be taken into account in change design from the very beginning.
It follows that effective change management is not merely a supporting program for implementation. It is a discipline in its own right, with its own methodology, phased approach, and measurable success criteria. Anyone who implements AI without establishing this framework is optimizing technology at the expense of the people who are supposed to use it.
For specific support with IT change management and adoption KPIs:
Schedule a discussion with Robert on this topic here. The conversation will cover IT change management—from stakeholder alignment and communication to adoption KPIs and sustainable integration into everyday work.
List of Sources and Further Reading
Sources cited in the text:
- Samuelson, W. & Zeckhauser, R. (1988): Status Quo Bias in Decision Making, Journal of Risk and Uncertainty https://link.springer.com/article/10.1007/BF00055564
- Kahneman, D., Knetsch, J.L., & Thaler, R.H. (1991): "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias," *Journal of Economic Perspectives*https://www.aeaweb.org/articles?id=10.1257%2Fjep.5.1.193
- Kahneman, D. & Tversky, A. (1979): Prospect Theory: An Analysis of Decision under Risk, Econometrica https://www.econometricsociety.org/publications/econometrica/1979/03/01/prospect-theory-analysis-decision-under-risk
- Laibson, D. (1997): "Golden Eggs and Hyperbolic Discounting," Quarterly Journal of Economics https://academic.oup.com/qje/article-abstract/112/2/443/1870925
- Kübler-Ross, E. (1969): On Death and Dying https://www.simonandschuster.com/books/On-Death-and-Dying/Elisabeth-Kubler-Ross/9781476775548
Recommended Further Reading:
- Kotter, J.P. (1996): Leading Change, Harvard Business Review Press https://www.hbsp.harvard.edu/product/leading-change
- Kahneman, D. (2011): Thinking, Fast and Slow, Farrar, Straus and Giroux https://us.macmillan.com/books/9780374533557/thinkingfastandslow
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This wiki is based on Yvonne Avaro's presentation at the Analytics Summer Apéro 2026. You can watch the presentation here: https://s-peers.com/analytics-analytics-summer-apero-2026-aufzeichnungen/
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