Over the past few weeks, we’ve published a series of cases on dealing with disruptive change. This series of cases was created as a service to Commoncog members, which I announced at the end of The Sensemaking Series. (The Sensemaking Series was a short sequence of three essays on how to make sense of AI, which then broadened into a discussion of how experts sensemake differently from novices. If you haven’t read it, you should; it gives us the grounding to talk about the current series of cases.)
It should be obvious why we’re doing this. Some of you may be feeling discomfort as a result of AI. Still others might also be emotionally affected by predictions of widespread societal disruption, or job losses, or rapid change in your workplace as a result of new technology. Or perhaps you just find it exhausting to keep up with (mostly dumb) AI news.
I wrote The Sensemaking Series for two reasons. The first reason was to discuss how humans improve at sensemaking more generally. The second reason was to talk about how sensemaking helps with expertise acceleration and tacit knowledge acquisition. But at the end of that series I said that it’s just as important to look at actual cases of people responding to disruptive change in order to calibrate ourselves. We want to know what good and bad sensemaking looks like, so that we know how to conduct ourselves when we’re in the midst of the storm.
And so here we are.
As of press time, there are 15 total cases in the Navigating Disruptive Change concept sequence. You’re welcomed to read all the cases in order; some of them deal with doing business under conditions of colonialism or war (conditions far more disruptive than our current AI moment!)
However, I want to focus on the five most recent cases in the sequence, which were all commissioned and published after The Sensemaking Series, and were therefore published with a specific set of ideas in mind.
Those five cases are:
I said that there are enough similarities here across cases that you should go ”hmm”. I encouraged readers to record voice notes in reaction to each of these cases as they were published, and to leave notes in the members-only forums. I also said that I would record my reactions later. This way, we may compare what we’ve noticed with each other.
Well, that time has come. If you’re a member, click through each of the cases above and scroll to the bottom. You’ll find my reactions at the end. As always with Commoncog Case Library reactions, you may choose to listen to or read my reactions. In each case, I hope that you’ve recorded your own voice note before listening to mine — the way to improve at your own sensemaking is to pre-commit your notes first!
I want to make three points. Let’s begin.
My first point is an observation: we all have a made-up disruption narrative in our heads. The narrative goes something like this:
The key thing is that second point. It is so easy to say that “folks are disrupted because they are stubborn! Why else would they be ‘fixated on the old way of doing things’?” What a neat explanation! How simple! Surely the solution to disruption is to ‘not ignore the new technology / change.’
If you did not actually seek out these case studies, you would probably have some version of the following narratives in your head:
I still remember a conversation I had in 2023, in the ‘early days’ of the current AI boom, when a startup executive I knew was speculating wildly about the impact of AI on data infrastructure. “I’m not sure that’s going to be the case.” I said, cautiously.
“Well, you have to keep up! Do you want to be left behind?” He replied.
”Do you want to be left behind?” If you squint carefully, folks pose this question because they have the simple narrative in their heads. People think that companies and careers fail in times of rapid change because folks are stubborn. Therefore the solution is to ‘not be stubborn’ and to ‘keep up with the times’.
And yet … if you’ve read all the five cases, you will know that stubbornness is never the reason for death.
In all the cases we’ve examined, people were adapting to the changes as best they could.
Note that in every one of these cases, and in every other case where I have looked, people did not get disrupted because of mere ‘stubbornness’. And how can they? After all, if this simplified narrative of ’stubborn, therefore disrupted’ exists in everyone’s heads, then most folks are intelligent enough and wary enough to keep an eye on changes. Most companies will experiment with new technology, or put feelers out in response to rapid change.
‘Stubbornness’ is almost never the root cause of failure in the face of disruptive change. It is something else.
What is that something else? The short answer is that inability to respond is nearly always due to some structural cause. This is more believable. People are not dumb; they tend to cope with whatever situation they are thrust into. But certain constraints in their environment make it easier or harder for them to cope with a particular change.
Now, we could say that many of these folks suffered from ‘frame fixation’. But this a more complicated thing than ‘stubbornness’. The frame fixation that the Swiss watchmakers faced due to the Statut Horologer is very different from “they refused to take quartz seriously” that many business historians levelled at them at the time. And the challenges that BlackBerry faced was less “the iPhone is a stupid threat” and more “we have so many internal issues to deal with we are overwhelmed; we don’t have time to think about external threats”. (Note that BlackBerry executives did lob ‘iPhones are dumb’ statements in the press in those early years, but what else do you expect them to say? Even Reed Hastings was saying “Blockbuster Online is stupid” in public whilst the incumbent was successfully destroying his company.)
“Ok, fine,” I hear you think, “Suppose I buy your argument. It is not a good idea to tell people to ‘not be stubborn’; things are usually more complex than this. But this in itself is not very useful.” This is a fair point. If you’re a businessperson facing down a disruptive threat, knowing not to say things like “don’t be stubborn” isn’t much help.
So what is helpful?
There are two things that I think are worth paying attention to across all of these cases.
Let’s examine the two things in order.
One way of interpreting these cases is “it’s a good idea to have a lot of cash on hand.” But that’s not the most useful conclusion. One of the reasons I commissioned these cases is that they are all different; I wanted to show you that there are actually many configurations of healthy balance sheets — all of which may work to accomplish our goal of resilience.
To wit:
On the other hand:
You could say that RIM wasn’t prepping for survival. It was thrashing about, spending nearly everything extra it made. If you’d like a completely different view of a turnaround — in the face of a cataclysmic demand collapse — read A Good Unwinding: Bill Anders and General Dynamics. Ask yourself: what parts of Anders’s strategy could you adapt to BlackBerry?
There are other, minor points that I wanted you to notice, and most of them have already been pointed out by the Commoncog membership in the forums. Most obviously:
This second point leads me to my next major observation.
The big problem with disruptive change is that you do not have a way to frame the solution.
Sure, the problems thrown up in the immediate wake of a crisis are all bad. Layoffs are dispiriting. Uncertainty feels horrible — remember the pandemic lockdowns, anyone? But the nature of disruptive change is that it often takes awhile to figure out how to respond to a fundamental change.
And actually it’s worse than that. You don’t even know how to think about the situation.
The big problem with disruptive change is this ‘framing problem’. As we’ve previously discussed, the Data-Frame model tells us that humans construct a frame to understand events. We continue reframing whenever data points emerge that our current set of frames cannot explain away. When this keeps failing — when we cannot construct a satisfactory frame to explain what we’re seeing — we begin to feel overwhelmed by events.
This is psychologically very taxing.
Perhaps you might already know how this feels. Put yourself in the shoes of the primary actors of each of these cases. In the face of an existential threat like the iPhone, or the Internet, or quartz watches, or the GFC — or, hell, our current AI moment — you should find yourself asking:
And often it’s not clear what the answers are to any of these questions … for years.
How many cases do you know where the incumbent dabbles in a new technology, only to have a viable strategy appear a couple of years later … by which time the incumbent had exhausted its willingness to respond?
We’ve already seen this in the current handful of cases. Balsillie and Lazaridis thrashed about for a few years with no coherent response to the threat, before throwing in the towel. The Swiss watchmakers were in a disarray for about six years before the creditors gave up and brought Hayek in. Most of Amazon’s executives burnt out and left in the five year period through the dotcom bust. Netflix themselves were overwhelmed and at a loss for what to do when BlockBuster Online started taking subscribers away from them.
Thanks to the Data-Frame model, we already have language for what is happening. It is exhausting when you’re trying to sensemake fast moving events without a working frame. So folks fall back on one that allows them to stop sensemaking: “It’s too difficult.” “We can’t win this.” “I’m out.”
Conversely, notice how morale and execution improves when you have a frame to deal with the disruptive shift:
In fact, the only exception to this was Ford, facing down the GFC. This was why I said in my case reaction that — while it is a little unfair — Mulally and his team did not have to rethink the fundamentals of Mulally’s ‘One Ford’ turnaround strategy; the GFC was terrifying but it did not change the way Ford thought about itself. As a result, Mulally’s One Ford plan served as a frame that brought the executive team together through the worst parts of the crisis.
(Yes, I am well aware this is easy to say today, because the GFC was terrifying at the time. Full appreciation for the terror of the GFC is covered my Case Reaction. The main reason I’m saying this is because the nature of Mulally’s challenge is a surprising dissimilarity, as compared to the rest of the cases in this sequence).
What is my point? My point is that a strong balance sheet buys you time to find a suitable frame … but the difficult part in all of this is finding a suitable frame in the first place. That’s why you need the time.
Oh — and you shouldn’t lose morale in the process.
All of this seems terribly easy to say. How do you actually accomplish this? The answer is actually hiding in plain sight. In all of the turnarounds that we’ve covered on Commoncog, the core frame for a response to a disruptive shift is:
“Let’s stabilise the business and ensure survival first. Survival means doing whatever is necessary to bring costs down and strengthen our balance sheet so we can spend a couple of years in the wilderness: sell off assets, lay people off, close down certain business lines, whatever … and then we run our default-alive business until we can figure out a path to win.”
Of course, finding that ‘path to win’ is the hard part in all of this. In some cases — like with Gillette and Brooks — the path came easy; the CEOs came in with a ready-made playbook that turned out to work. In others (General Dynamics, Patagonia) it took awhile. And I’m not going to lie: it’s not easy to keep a team together based on this frame alone. It’s much easier to recruit folks when you have already figured out a frame for winning.
That said, a) it’s better than having no frame at all (see: Balsillie and Lazaridis) and b) this is just the nature of such things. Keeping your team together is a leadership exercise for the alert reader.
If this core frame comes as a surprise to you, there is a good reason for this. The majority of business writing today is influenced by venture capital outcomes. VC-backed companies are not default alive: they are designed to grow fast and to constantly depend on external injections of capital. The marketing engine of the VC industrial complex is thus fine-tuned to sell this journey of “grow fast, fail fast, get rich fast”. A frame of “survive now, bide our time, and figure out a way to win even if it takes a few years” is the antithesis of that model of business.
And yet it is everywhere, if you know where to look.
So much of this essay is actually common sense. If you’ve spent any time reading investing books or listening to old investor-types, you may have heard “I want a company with a fortress balance sheet, so I know it’s going to survive whatever the market throws its way.” Which — yes, duh, fortress balance sheet good — but what does that actually look like, in practice?
This is what it looks like in practice.
To recap, what have I shown you? I have shown you that there are two things that matter in response to a disruptive shift: first, a strong balance sheet, so that, second, you can buy the time to work out a response to this shift. A minor point that goes along with those two is that keeping morale high is a key part of this challenge, and that’s always easier to say than to do.
On that note, there are many configurations of balance sheets that work:
There is one final point that I want to make, about seriousness. During this period of AI hype, you will hear extremely confident predictions about the way AI will affect society. Some of them will be positive, others will be negative. Some will signal to you that “you’re being left behind.” Others will make precisely calibrated predictions of outcomes — both positive and negative. The majority of such predictions will project confidence because there’s a selection effect here: the things that spread the furthest in our current media environment tend to be the ones that are expressed with the most confidence.
We’ve already discussed why listening to many of these predictions are a waste of time. But here’s another angle. Many of these predictions are unserious. Their arguments are peppered with statements like “I believe X will occur”, or “it is extremely unlikely Y would occur.” Some propose interventions without doing the work to study how similar interventions have worked out in the past.
But here is a quick gauge for seriousness: have they actually done the work to hold that opinion? Have they looked for past cases to ground their understanding for what they are proposing? Have they asked around? Or did they pull the argument from their ass, with no way to test it?
If you actually wanted to know how to make sense of disruptive changes, you would seek out real world cases to ground your arguments. You would study people and businesses that have faced world changing shifts, and looked for patterns in their responses. And you would follow to see what they did, and how they fared.
Which is what we’ve done, together, here.