The conversation
The 30% Club exists to increase the percentage of successful changes by placing the employee at the center of the change process. In this episode, Arjan Nataraj took me through the changes I witnessed firsthand: the rise of the smart meter as a driver of customer engagement, the formation of multidisciplinary teams at Eneco, and the implementation of a Next Best Action platform at Essent. Always with the same question as an anchor: why do some changes succeed while most fail?
Five insights from the conversation
- 10/20/70 is the ratio that AI change requires. Ten percent of the attention goes to the model, twenty percent to data and infrastructure, and seventy percent to the employee, the culture, and structuring the change. In practice, I see exactly the opposite: the employee remains the last priority, whereas that is where you should start.
- Trusting an algorithm is a behavioral change, not training. With a Next Best Action platform, you are essentially telling an email marketer: from now on, an algorithm determines who receives which message. This impacts the profession and the professional. That is why the work begins with data and AI literacy: first understanding how an algorithm works, then learning how to influence and control it, and only then having the conversation about what is changing in the work.
- Multidisciplinary teams shorten the distance, and you get used to that quickly. At Eneco, I brought developers and marketers together in one team. The initial resistance vanished like snow in the sun as soon as people saw the added value, until colleagues no longer wanted to work any other way. Recently, I applied the same mechanism again when a collaboration stalled: having teams get to know each other and letting them determine priorities together.
- Employee satisfaction is a hard measure of success. As a Domain Manager at Eneco, employee satisfaction was one of the most important metrics. Satisfied employees perform better and adopt change more easily. And measuring prevents a gut feeling, or the person with the most stripes, from determining whether something is successful.
- Stop changing stamps. AI change is often approached instrumentally: a standalone pilot here, a use case there, in the hope that the cumulative effect will automatically become the major change. That does not happen. Organizations need the courage to ask the bigger question: who do we want to be in ten years, and what organizational form, competencies, and processes are associated with that?
Why this matters
Research has shown for years that the majority of strategic transformations fail to achieve their goals, and AI adds an acceleration that does not make things any easier. The temptation is great to act quickly out of fear of being too late. But a successful case is not yet a changing organization. The gain lies in a consistent change approach that applies to every change: a clear 'why', a structured process, measuring the impact on employees, and daring to stop doing what does not work.
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