[Bonus] Change changes but not everything changes

*Bonus!* As a companion piece to my interview on SAP SIGNAVIO's "PROCESS TRANSFORMERS" podcast, a thesis for maximizing the actual impact of your transformation efforts and avoiding unproductive "change for change's sake."

[Bonus] Change changes but not everything changes
Seek "innovation/ context fit," not change for change's sake! | Photo: Jo Coenen - Studio Dries 2.6 / Unsplash

Thank you Lukas Egger and team SAP Signavio. So good to join you in conversation for the Process Transformers podcast!

If you have not yet had a chance to listen to it, check out our conversation, along with all other episodes of Process Transformers as they come online!

The fun that we had in the conversation hopefully comes across on tape. Any chance at a conversation with Lukas is always great to get. But of course, it still takes thoughtful work to prepare. (And even that is just for guests like me, not to mention the extra work for the host team.)

If you know me at all, it comes as no surprise that I rarely skip a chance to prepare rigorously—aka geek out on a topic—anyway. But this time, it really mattered:

This seemingly-straightforward conversation topic posed a slew of tricky questions once you really consider it. So I decided to go beyond "mere" background research and combine my take into a full argument.

Here it is, covering both key points from my conversation prep and separate practical applications for innovators anywhere (along with a quick graphical summary, both at the very bottom).

May it give you interesting extra food for thought on how to wrestle with the change related to your own organization's transformation!

FYI: The sections of my argument follow the structure of our conversation’s title, with an added section at the end, i.e.:

Change changes but not everything changes, and not all change makes things equally better!

"Change changes"

The point that change moves ever faster and in ever more diverse directions is the part of the thesis that you can most easily observe. I thought it would need the least additional research.

Here then is just a quick overview of this part of the argument before we move right along and dig into the more nuanced issues of the argument's the second half:

Change enables more change.

The more humans there are, the more those humans creates, and the more the broader universe evolves, the more changes are generally possible.

Change is not accelerating everywhere.

Change is not moving faster in some domains, where it may instead slow down or reverse (e.g., global and national politics).

Change is accelerating in Tech and Science, writ large.

Based on your own background, some topic will surely pop into your mind right away. But in reality, change is accelerating across a vast range of topics:

AI, space tourism, gene editing, and energy transition, are just a few of the topics that lay the foundation for a yet faster rate of change.

"But not everything changes"

This is where things get really interesting and non-obvious!

Changes can only have an impact if they accommodate the real world

"Innovation/ context fit" is a critical precondition for having any impact

Innovations live in a context that does not owe fealty to innovators' efforts. Ignoring the context sets up clashes (aka, "let's just blame the user and tell them to fix themselves").

E.g., my razor has "AI" built in. But the "insights" that the AI produces about the quality of my shaving experience are useless. Why? Because the engineers and marketers who created the AI and the UI that surfaces the AI's insights missed that much of the shaving quality comes not just from their smarts, science, and algorithms, but also from the setting in which you shave (e.g., humid weather, post sports) and individual user needs (eg trimming vs. "actual" shaving). They don't ask for the right non-digital data. So they don't get the results they promise ... but then they blame the user in the "insights" that their app produces for "not yet being good" at using the razor. (Well, they try to word it more nicely. But they still basically say "our tech is perfect. The problem must be ... you.")

E.g., "techno optimism"–per which problems with past changes (or anything else) will supposedly be solved by yet more (tech) changes–does not even fully prevail in its mecca of San Francisco, let alone in the world beyond. E.g., in San Francisco, social activists are disabling self-driving cars by placing traffic cones on their windshields, to protest an alleged lack of community involvement in the deployment of AI that affects their quality of life. Irrespective of whether the activists' actions are legal or ethical, they happen. And companies have to deal with that reality. The promise of self-driving cars will not come to life if humans purposefully exploit its weaknesses to sabotage it.

Integration with everything else is half the battle

E.g., a client team and I recently spent a frustrating, manual hour getting supposedly "work-saving" AI tools to function across the differing "productivity" suites of two Tech giants. Each team had its reason for using the "best in class" system they were using. But in the end, we almost gave up on using all the AI and using older, simpler, but reliable and compatible tools instead.

Change cannot happen faster than humans can adopt it

(Aka "The future is already here, it's just not evenly distributed." — Quote attributed to science fiction author William Gibson)

All new innovations (e.g., AI now) must follow the adoption curve. That curve will encounter the "three deaths" coined by author and physicist Safi Bahcall in addition to the single "chasm" of adoption. And adoption won't be driven by the pace by which invention is "supplied" but by the pace of adoption/ "demand."

E.g., it took me much longer to use AI productively than to "play" with it. People are terrible at changing habits to incorporate new tools, let alone completely different paradigms of work. I have seen entire process transformation/ automation efforts fail because the new technology would have required floors-full of professionals to upend how they sequence their work. And innovation's advocates were so convinced of their technology's superiority that they never put sufficient effort into helping the humans adapt (or even want to).

No change is ever complete, never mind early predictions

Change and its context influence each other

(Aka "the best laid schemes of mice and men go oft awry" — Quote from "To a Mouse" by Robert Burns, Scottish poet)

Instead, both the change vector itself and its surrounding system morph until they reach equilibrium at partial change.

E.g., we don't all shop exclusively on "the internet." But all manner of digital tools have erased the border between "physical" and "digital" shopping.

E.g., we won't all "work remotely all the time."

Externalities will interfere in chaotic ways

E.g., the pending AI lawsuits and legislation around the world may make or break companies' fortunes, decide how we can use AI, and who gets to benefit financially.

In turn, these decisions will massively affect, in particular, the future of creative fields like book authorship, photography, and movie-making.

"And not all change makes things equally 'better'"

No single innovation is ever "transformational" on its own

Not all changes matter

The relationship among "creativity," "innovativeness," "impact," and "necessity" is decidedly muddled in terms of correlation, direction of causality, and activation timing.

E.g., for electric vehicles (EVs), the further you are from engine and drivetrain development and sourcing, the less "innovative" EVs theoretically are. Now that their "cool factor" is even moderating, EVs are just regular cars that I have to fuel from a different nozzle than other cars, often even at the same fueling stations, thanks to one car subsystem having changed. Hardly "innovative"-seeming. And yet, their impact on the climate might be huge (all even assuming we make sustainable power generation happen at scale).

E.g., Ada Lovelace's invention of software was not yet "adjacent possible" (in the words of Steven Johnson) in the age of steam technology as the basis for computer hardware.

We have terrible judgment for what change will "matter"

Humans have always had a tendency to assign outsized importance to the topics about which they themselves happen to care. In reality, it's tremendously difficult to say in advance whether, how, when, and how much a supposedly "transformational" innovation will actually "transform" things. Only hindsight is 20-20. And no single "transformation" is the "only" or "most important" one.

E.g., Segways turned out not to matter all that much, despite the hype surrounding them that suggested that the Segway might be "bigger than the internet."

E.g., some of those working on AI clamor that AI is the only thing that matters or will cause any change of importance. But innovations like "making life interplanetary" (e.g., per Space X), getting good covid vaccines fast (BioNTech + Pfizer), gene editing, transformation to sustainable energy, etc etc etc also all matter a LOT, just to different domains or using different lenses to view our world.

The impact of change is hard to predict and hard to observe

Change is not linear ("punctuated equilibrium" and migratory changes), nor do people uniformly agree what change counts as "progress" vs. "regress."

E.g., change has many short-term losers. It's perfectly rational for them to fight that change and advocate for alternatives that better match their incentives.

E.g., we still have brutal authoritarian regimes in the world.

E.g., the culture wars of the U.S. abound with people claiming "progress" in ways that others would consider abysmal deterioration ... sometimes in such arcane ways that outsiders only shake their head at the absurd dialectic opposites constructed by the warring parties.

Humans change but often don't acknowledge or remember that they ever did so (per various psychological phenomena).

The key point

Ironically, a less absolute, rigid push for change often unlocks greater impact!

"Optimized" changes are brittle

Systems need balance, randomness, multi-polarity, and forces in tension to stay vibrant and adaptable.

E.g., in agriculture, biological monocultures are highly productive but only while the contextual conditions stay within narrow, optimal conditions. But their single dominant "right way" squashes newness. And so the monoculture becomes brittle, lacking resilience to adapt. If conditions ever change, the system fails catastrophically because it has lost its ability to adapt. By contrast, healthy ecosystems are diverse and dynamic. What counts for "ineffiency" to short-term-ists is actually what creates the optionality needed for thriving in the long term.

(Same for human systems of course, e.g., for "optimal" global supply chains that failed during covid for a lack of fallbacks and adaptability.)

Change causes major unintended consequences

Change causes "secondary unintended consequences" that can spiral out of control if change happens faster than the system in which it exists (e.g., society) can adapt.

E.g., the nautical compass enabled transatlantic chattel slavery at industrial scale. But that surely was not what Chinese scientists in the 11th or 12th century had in mind when they may have invented the magnetic compass.

E.g., Mark Zuckerberg et al surely did not mean to create a threat to democracy when they created social media tools that were later used for mass-scale disinformation campaigns (aka psychological warfare).

Change is a multi-source outcome, not a single-source activity

Change happens in dialogue with what else exists in the world, as a situation-specific recombination effort, not as pure "ideation" out of nowhere

(Aka "we stand on the shoulders of giants" — Multiple attributed and associated authors.)

E.g., most recently, Prof. Sheena Iyengar's book Think Bigger actionably makes the case that innovation never just "happens" like the proverbial apple hitting Newton on the head. Innovation that can achieve anything is a situation-specific recombination of things we remember.

Practical takeaways for innovation leaders

  • To understand the progress that incoming change has made so far (or not) toward widespread adoption: Be mindful of and work around the "three deaths" identified by Safi Bahcall in his book Loonshots. The three "deaths" (which are actually mistaken but persistent critiques from others) that your idea will likely also encounter and that may shut you down if you don't anticipate and avoid them are: (1) "This will never work." (2) "This can't work consistently at scale, in an economically-realistic way." (3) "The unintended consequences are too great." If the change you are considering hasn't made it through one or more of these deaths yet, rest assured that it'll still happen. You are bound to get that pushback. Depending on whether you favor or oppose this change, the next upcoming "death" is where focusing your efforts will have the most impact. (BTW, wording and any errors in it are mine. If so, mea culpa!)
  • To initiate change that has promise: Do not try to come up with solutions that can initiate significant transformation via "ideation" (which is just geek-speak for various modern versions of brainstorming). Instead, find elements from what already exists in the real world and recombine them in ways that together solve pressing problems, e.g., as described in Prof. Sheena Iyengar's "Think Bigger" framework from her eponymous book. " It's about breaking a problem into its top 3 - 7 parts, choosing the best solution option for each part, and then combining those partial solutions into a cohesive whole.
  • To make change actually fulfill its early potential: Don't use change as a battering ram to bring about "good" on its own. Don't pursue every change that sounds cool. Not every change can succeed in your current situation. Instead, seek "innovation/ context fit." Find the problems in the current context (according to your stakeholders, not you). Then find innovations that solve those problems that people already want solved, just in a meaningful, future-oriented way.

In summary:

Colorful infographic summarizes this essay in 8 headlines that follow the post's sections


Further reading:

Bahcall, S. (2019). Loonshots. St. Martin’s Press. https://books.google.com/books/about/Loonshots.html?hl=&id=b55xDwAAQBAJ

Iyengar, S. (2023). Think Bigger: How to Innovate. Columbia Business School Publishing. http://books.google.com/books?id=ECRMzwEACAAJ&hl=&source=gbs_api

Johnson, S. (2014). How We Got to Now. Penguin. https://books.google.com/books/about/How_We_Got_to_Now.html?hl=&id=Fr6JDQAAQBAJ


Photo "Time lapse photography of man riding bicycle" by Jo Coenen - Studio Dries 2.6 on Unsplash