Some quotes stand the test of time and even gain popularity during periods of massive technological change, when businesses are surprised that they are not reaping the rewards of their investments.
This one, most often attributed to Einstein, is one such quote.
Insanity is doing the same thing over and over again and expecting different results
Personally, I see it as an exhortation not to fall into technological solutionism (To solve anything, click here) and, more generally, a reminder that applying cosmetic improvements to an organization and ways of operating that remain unchanged is tantamount to repeating the mistakes of the past, or even amplifying them.
In brief:
- The text warns against technological solutionism and the risk of thinking that a tool or superficial improvement is enough to profoundly transform an organization.
- It emphasizes the importance of questioning not only practices, but also the beliefs that underpin them, particularly in transformation processes.
- Persistent dysfunctions are often a sign that the changes made do not address the real causes, but are merely a methodological facelift.
- In a complex environment, it is not a question of avoiding repetition per se, but of paying attention to its effects, adjusting, learning, and avoiding both stubbornness and managerial instability.
- The quote above serves primarily as a starting point for encouraging a clear-headed, adaptive, and experimental attitude toward organizational and managerial challenges.
The author
This quote is most often attributed to Einstein, who needs no introduction, but there has never been any tangible proof that he ever said it.
This goes against my taste for accuracy, but ultimately it doesn’t matter: if we attribute it to him, it’s because we think it’s really important.
Context
Since we cannot discuss the context in which Einstein uttered this phrase, let’s talk about the context in which it most often comes to mind.
How often do we see teams persist in repeating the same rituals, cumbersome and outdated processes, and meetings with no clear purpose, while hoping to collaborate better, innovate, become more agile and improve performance, under the pretext of simply tweaking their practices or implementing new technology?
This quote highlights a common bias in the professional world: believing that repeating what we know will be enough to solve what routine itself has created.
It belongs in the same category as certain quotes or slogans that tell us that “you don’t drive by looking in your rearview mirrors” and that “the people whose decisions got us into trouble aren’t those going to get us out of it“.
Explanation and managerial implications
“Always doing the same thing” is not limited to operating procedures; it also, and above all, means perpetuating the same beliefs. For example:
- Believing that more digital tools will solve collaboration problems and, more generally, that technology is self-sustaining (The limits of technology-driven transformation).
- Thinking that people resist change on principle rather than questioning the meaning of the proposed changes, how they are designed and implemented (Change and transformation need a new approach).
- Thinking that every problem can be solved with a new indicator or more reporting (How to Manage Complexity without Getting Complicated).
The problem is therefore not repetition itself, but the lack of questioning, and what this quote highlights is our inertia when it comes to questioning our beliefs, habits, and routines.
Even Einstein can be wrong
I often say that just because a quote is brilliant, or because it suddenly opens your eyes, or because its author’s fame gives it the authority of absolute truth, that doesn’t mean it can’t be questioned.
This is particularly true when it is closely linked to a specific context, which seems to be the case here.
It assumes a certain stability in causes and effects. Conversely, in a complex world, where the same actions can produce different results depending on the context, it loses some of its relevance and can even prove counterproductive if it pushes for change too quickly by misidentifying the causes.
In complex systems, it is often necessary to repeat, adjust, and learn.
Here, it is not repetition that is crazy, it is the failure to observe what it produces. In fact, two extremes must be avoided: blind obstinacy on the one hand and managerial zapping on the other.
A quote that remains useful, but only if interpreted as an incentive to lucidity rather than agitation.
And since generative AI is all the rage, it is the perfect illustration of the impossibility of perfectly replicating a result with the same causes (Businesses are deterministic, generative AI is not, and that’s a real problem): try 10 times the same prompt, even perfectly optimized, and you’ll get 10 different answers.
Put into perspective
In the current context of hybrid work, information overload, and technological adoption, this quote invites us to:
- Observe recurring dysfunctions without settling for a methodological patchwork in a logic of transformation rather than improvement or patchwork.
- Accept experimentation as a normal management practice.
- Encourage discussion within teams about what really works and what we do out of habit (Improving a team’ s work: a story of continuous improvement).
In other words, it is not about innovating for the sake of innovation, but about daring to do things differently, not for the sake of change, but to test the hypothesis that the problem may lie in what we believe to be a proven solution.
Bottom line
This quote is an invitation to break out of inertia, rigid processes, outdated managerial beliefs, and tools that we pile up for lack of anything better.
But in an increasingly complex world, with all that that entails, the desire to change everything can be as irrational as the desire to change nothing.
The real challenge is not to be conservative or innovative, but to be attentive, adaptable, and able to ask ourselves whether what we are doing today will be relevant tomorrow and whether, conversely, in some cases, repeating best practices will never achieve a satisfactory result every time.
Image credit: Image generated by artificial intelligence via ChatGPT (OpenAI)
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