The one by Neil Perkin
On the importance of contextual understanding
Friends,
I hope that all is well with you and yours, and that this e-mail finds you on a boat with shoddy connection, in the tropics, three months after I sent it.
In today’s newsletter, guest writer and organizational thinker extraordinaire, Neil Perkin, breaks down the importance of contextual understanding, market patterns, and corporate metamorphosis. For premium subscribers also the implications for transformation projects and the three most important things to consider.
Now accepting keynotes for 24Q4-25Q2
Every year for the last decade or so, I have created three main presentation decks. For 2025, however, I have (for the first time) added a fourth due to popular demand. They are:
What to Do When You Don’t Know What to Do: How to turn change into a competitive advantage. (Based on the new book by the same name.)
Leadership in a Time of Change: How to steer an organization through a sea of uncertainty. (Executive audiences only.)
Resilient Retail: How to build a profitable retail business in the modern marketplace. (Based on the 2025 follow-up to the highly praised 2022 white paper The Gravity of e-Commerce.)
Artificial Intelligence Beyond the Hype: How to understand the narratives, risks, opportunities, and best uses of a new technology.
If you want to book me for your event, corporate speaking slot, or workshop, merely send me an email. To make sure I am available, please do so at your earliest convenience; my availability is limited and the schedule tends to fill up fast. More information may be found here.
A couple of updates before we go-go
A week on, and life has somewhat normalized. Our house is still split in two; I am still sleeping in the guest room downstairs, as is still the eldest. Partly, though, this has to do with it being my turn to pick up the sniffles.
Meanwhile in business, signs that Musk is losing some of that supposed star quality are continuing. Twitter/X is going downhill fast, with both users and advertisers abandoning the site. In fact, things are so bad that the $13B loan he took to finance the takeover is now considered the worst buyout for banks since the financial crisis.
Translated to non-financial lingo, what it means is that seven banks (including Morgan Stanley and Bank of America) lent the money to Musk’s holding company. Banks that provide loans for takeovers typically then sell the debt quickly to other investors to get it off their balance sheets, making money on fees. However, nobody is buying. Theoretically, the banks could get the money back, but given that the social media platforme had halved its value last year and is showing precious few signs of improving, it is becoming unlikelier by the day.
But hey, the man’s a genius. Or something.
I often argue that few firms take into account their own success. For example, if I were to ask you what would happen if a firm beat expectations, you would answer that its stock would increase. Well, Nvidia did just that. But the market deemed it did not beat the expectations by enough, and the stock dropped. Without a track record as stellar, it would not have happened.
Given the continued hype around AI, and the fact that more and more players are moving into the space (including the aforementioned oddball billionaire), I would imagine Nvidia will continue to do well so long as it can maintain production. Demand does not appear to be an issue. But somewhere down the line, AI solutions must also begin to gain traction on the consumer side. If they do not, a lot of tech companies will suffer, as will the broader economy.
Before we move on to today’s guest writer, a reminder that prices will be adjusted on Sunday: monthly subscriptions will be $8, yearly $70, founder $125 due to the inflation that we have experienced over the last few years. In other words, if you want to save some silver, be sure to purchase a year-long subscription within the next two days.
The reason is, to be blunt, the survival of this newsletter.
As a special treat to kick off the new “season”, next week will see the return of the inimitable Claire Strickett - whose guest post from last year currently holds the position as the second most read newsletter in Strategy in Praxis history. Will she beat it?
Moving on.
JP’s note: today’s guest writer, Neil Perkin, is someone who I had long admired from afar before eventually getting the chance to meet him at a conference in Dublin a few years ago. Among those who try their hardest to push the proverbial envelope and push the industry forward, Neil has long been among those who lead the fray. His newsletter, Only Dead Fish, is (deservedly) immensely popular as a result. I highly recommend a subscription.
Navigating change
The importance of context and defaulting to optimization
As someone who’s worked with senior leadership teams across multiple sectors, one of the biggest obstacles that I see to successfully navigating change is a lack of contextual understanding.
Every good strategic process begins with situational awareness and an understanding of context. John Boyd’s OODA loops (Observe, Orient, Decide, Act). Richard Rummelt’s ‘strategy kernel’ (diagnosis, guiding policy, coherent actions). Stephen King’s planning cycle (Where are we? Why are we here? Where could we be? How could we get there? Are we getting there?).
Yet so often contextual understanding ends up becoming the poor relation to what strategist and researcher Simon Wardley has called ‘magic frameworks’ or ‘secrets of success’. Most market or system maps, he says, are not maps at all since they have no representation of position or movement. Combining strategy with execution, position with movement (and the learning that comes from movement), is what enables us to navigate challenging environments.
Understanding and responding to technologically driven change is no different. The ‘S-curve’ has become a common way of representing the adoption and development trajectories of significant technologies. This represents a pattern of slow emergence (which may take years) followed by a rapid period of development and growth in adoption, before the growth plateaus as the technology moves into an advanced state of maturity and eventually obsolescence. Author Charles Handy presented the idea of overlapping S-curves as a way of showing how one paradigm replaces the other, emphasizing how important it is to begin focusing on the next curve before the current one begins its decline (so-called ‘second-curve thinking’).
S-curves appear everywhere from Carlota Perez’s views on technological revolutions to Clay Christensen’s famous ‘innovator’s dilemma’. One could easily argue that businesses are dealing with multiple and successive overlapping S-curves, meaning that continuous experimentation is required, but this idea also reveals the key challenge at the heart of technological-driven change - the ‘dilemma zone’.
As technology 1 moves through the early phase of emergence (A), rapid development and growth in adoption (B), plateauing growth (C) and eventual obsolescence (D), an organization will be establishing and then embedding and optimizing systems and models around that technology. At point C they will be well optimized, efficient and productive and so there will be little reason to change. Existing ways of thinking that have become embedded over time will be hard to unravel and the lack of an existential threat will likely mean that there is little appetite to do so anyway.
These assumptions and ways of reasoning will likely relate not just to technology but also to processes, models, ways of working and even mindsets. To break open these assumptions leaders need to make conscious efforts not to get stuck in optimisation. And as new paradigms begin to take shape there is a very real risk that leaders look at the new through the lens of the old.
A helpful way of thinking about the opportunity in new paradigms is to deliberately focus on both optimisation and transformation. I often use the metaphor of nature’s own model for change to describe this: metamorphosis. There are, of course, two kinds. With incomplete metamorphosis (like a grasshopper) the insect simply sheds its skin as it grows larger, but a big grasshopper still looks like a small grasshopper. This is akin to optimization - inevitably there will be opportunities within the new paradigm or technology to double down on what you already do, to get bigger, better, faster, more efficient.
Complete metamorphosis however, is different. Here, just like the butterfly, we begin as one thing but then reorganize it in fundamental ways to create something entirely different. This is a genuine transformation - we are working from first principles to reinvent and redesign in order to capitalize on entirely new possibilities that the paradigm is creating.
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This is why contextual understanding is so important. Without thinking of the value that can be derived from both optimisation and transformation we are at risk of not thinking big enough, and we may well end up at point D on the S-curve playing catch up with others who have been braver.
There is a natural tendency to think about change in quite one dimensional ways yet this ignores important contexts. So-called first order change for example, involves doing more (or indeed less) of something that you are already doing. It’s reversible, you can adjust and course correct as you go. It is focused on re-establishing homeostasis and restoring balance. It relies on existing stocks of knowledge and the old story can still be told. This is optimization. We are focused on ‘single-loop’ learning - evolving our actions to improve results.
Second-order change is however, altogether different. Here there is a more fundamental change in the system, the need to unlearn existing assumptions, the potential to see things in a different way, to develop entirely new models, to develop fresh learning around the new paradigm, to tell a new story. This is transformation. Here we need to be focused on ‘double-loop’ learning - changing our mindsets to develop a new set of actions that can improve results.
Inevitably this is not a question of either approach being mutually exclusive to the other, but rather how they can work together and the degree to which we choose to focus on each depending on context. Understanding where we might be on the S-curve is helpful here since the balance will shift. As we move up the curve we are likely more focused on the job of optimisation. But as we begin to reach the top of the curve, we will likely need a much bigger emphasis on transformative ideas and work. And really, in a complex, fast-moving world we should always be open to Handy’s ‘second-curve thinking’ and be systematically experimenting around the new as well as optimizing the old, exploring new possibilities as well as exploiting existing advantages.
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