Volume 197: Time For A New Management Orthodoxy.
Time For A New Management Orthodoxy.
tl;dr: Orthodoxies always change. Looks like another is coming.
Based on today’s corporate leadership, you’d be forgiven for thinking that tax-minimization and legal tax avoidance had always represented management orthodoxy. However, you would be incorrect. In the 1950s, General Motors, then the world’s largest corporation, bragged in its annual reports about much tax GM paid, and specifically, how much more it paid than other corporations - viewing this as both a sign of post-war patriotism, and as a way of keeping score in the corporate success stakes. And GM was not alone. This was the orthodox position of the time. (I’m sorry I can’t find good links for this. If you are wondering, the specific years are 1953 and 1955. Hardcopies of these annual reports sell on eBay for about $50).
Why the history lesson you might ask?
Well, it’s to make the point that management orthodoxies, and the underlying philosophies that drive them always change over time. Sometimes, as in the example above, they might change diametrically, sometimes more subtly, but they always change.
Since the 1950s, dozens, perhaps hundreds, of management philosophies have emerged as natural outputs of both academic theory and the process of creative destruction that underpins capitalism. Some became popular enough to become orthodoxies—blitzscaling among startups during the ZIRP years, for example—while others fell by the wayside. Does anyone remember “holacracy?” No, me neither; I had to look it up.
To understand where we are today, let’s go back to 2000. Under the leadership of Jack Welch, GE transformed itself from a sleepy industrial conglomerate to the world’s most valuable corporation. As a result, the financialized management philosophy of its then CEO, “Neutron Jack” Welch, became orthodoxy, focused as it was on three things:
Rigorous financialization of the corporation for stock market gain
Six-sigma process improvement in support of point 1
Ruthless elimination of underperformers in support of point 1
At the peak of GE's market power, executives were poached by other corporations to sprinkle its perceived magic over their stock prices too. The results are enlightening. Rather than driving success, GE management alums went on to destroy value at corporations as varied as Home Depot, Chrysler, Boeing, and ultimately GE itself, which last year split into three separate corporations worth a fraction of its market peak.
This matters because, in addition to changing more often than we might think, management orthodoxies often begin an inexorable slide toward irrelevance at the exact moment they appear most dominant. And if initially isolated examples of value destruction are the canaries in the coalmine of such a slide, we’re currently seeing the same GE pattern repeat as technology execs fail after being brought in as CEOs at non-tech firms based on a similar desire for stock market magic.
I can’t empirically evidence this, but my thesis for why management orthodoxies seem to become so vulnerable at the exact moment they dominate is that by this point three conditions have been met:
These orthodoxies hit a wall of diminishing returns relative to whatever advantages may have driven their initial success
Competitors figure out these weaknesses and exploit them for their own gain, spawning new, competing management philosophies in the process.
Today, what I refer to as Measureship has scaled so widely that I believe it to be the dominant management orthodoxy, certainly in the US.
In historical terms, Measureship metastized directly from the technologically enabled philosophies of “move fast and break things,” “data-driven management” and “digital transformation.” It was a necessary evolution for formerly ‘move fast and break things’ startups as they scaled exponentially and suddenly found themselves managing massive, complex organizations. For others, it was embraced as they digitized to keep up with these fast-growing titans. This creates meaningful context as we consider what might replace it, since emergent management philosophies almost always form as reactions to what came before.
At its core, Measureship reflects a shift in how we use data. Businesses have always made decisions informed by, and sometimes driven by, data. This isn’t at all new. What changed is that all management data used to be slow, fuzzy, and expensive to gather and interpret, which meant you had to pick and choose your sources wisely. As a result, leading managers used experience, intuition, and pattern recognition skills to run corporations. In essence, this was management by pattern.
Then, over the past twenty years, digitization fundamentally changed this equation. While our available technology could not meaningfully accelerate our use of unstructured, qualitative data, it utterly transformed its structured, quantitative cousin into an unrecognizably fast, accurate, and, by historical standards, cheap decision-making engine, which we invested heavily in as it unlocked significant new value.
This means the path to Measureship was laid by the significant gains we initially realized from the systemization of quantified data, which then led us to do two things we’re now suffering from:
We began to deprecate and then ignore the value of qualitative signals entirely, or treat them as if quantitative when they aren’t.
We started building complex yet simplistic quantitative models of the corporation, which became our management operating system.
This is why today’s business leaders are far more likely to be quantitative systems thinkers than experienced pattern recognizers. What’s interesting about this is that when you ignore qualitative signals and overwhelmingly rely on quantitative models to manage organizations, three things happen:
You flatten management context in much the same way algorithms have flattened culture. The removal of qualitative signals removes critical context, empathy, and humanity, while at the same time, the standardization that quantitative management requires inexorably commodifies the corporation.
You over-rely on the incremental optimization of simplistic, closed models of reality. In other words, you’re no longer seeking to understand and manage objective reality, instead you’re optimizing a flattened abstraction of it.
There’s nobody left to tell you when this isn’t working because your operating environment now rejects empathy, creativity, and human understanding. As a result, people with these qualities have left or been fired, leaving you with a closed-loop of groupthink in the corporation's managerial ranks.
Since quantitative models represent abstractions of reality rather than reality itself, we could reasonably observe that we’ve shifted from management by pattern to the management of abstraction.
As a result, I view Measureship as the first post-truth management orthodoxy. Almost cult-like in its adherents’ faith that success will emerge from the rigorous and continuous incremental optimization of simplistic, flattened, quantified systems, even as the evidence turns increasingly disastrous: Nike, cough. Starbucks, cough, cough. Boeing, ugh. Walgreens, words fail me. And many more than I can mention.
Unfortunately, having realized its early gains, this post-truth management orthodoxy is now becoming increasingly problematic as the easy value has been captured, and the diminishing returns of Measureship force its adherents toward value extraction, and ultimately value destruction. I’ve detailed its hallmarks before so won’t repeat them here, but the easiest way to think about this is to consider it relative to value and the sustainability of advantage:
The challenge when your management orthodoxy inexorably slides you toward value-extractive behavior is that unless you can halt the slide, the inevitable result will be value destruction and, ultimately, oblivion. Even for monopolists, it just takes them longer—sometimes a lot longer.
Today, we are at peak Measureship. As diminished returns force more corporations toward value-extractive behavior to drive growth, this orthodoxy is ripe for replacement.
As a result, the real question is less about whether it will happen and more about when it will happen and which inflection points will enable us to move beyond this empathy-free and joyless period.
Floating a trial balloon, I believe application-layer Generative AI may be the precipitating technology a frustrated cohort of business leaders will use to forge a new path. Now, I know what you’re thinking: Good Christ no, not another AI rah, rah, rah, but please hear me out.
Because of the dominant mindset of Measureship, corporations are almost certainly thinking about GenAI wrong. Technology corporations are the high priests of Measureship, and because faithful managers across the economy are also filtering the idea of GenAI through the same philosophical lens, everyone is coming to the same conclusion: that GenAI exists to further the holy trinity of their faith: productivity, efficiency, and incremental optimization.
But, what if this isn’t the only thing it’s good at, or even what it’s best at?
While GenAI’s ability to drive productivity and efficiency is proving patchy (so far), we already know that it’s excellent at turning unstructured data into structured outputs.
Another way of saying this is that GenAI already appears to be excellent at ingesting messy qualitative data and spitting out structured stories, narratives, and metaphors that humans respond to. This could potentially unflatten management context. We just need to harness this new power to manage unstructured qualitative data in the same way a prior generation of management harnessed the power of structured, quantified data.
As a result, GenAI’s greatest value within the corporation might not be as a productivity machine at all, but as an inspiration factory that re-balances quantitative and qualitative management inputs, unflattens context, deprecates abstraction, and awakens a new management philosophy in the process. And, since GenAI appears to be surprisingly adept at pattern recognition, there’s a non-zero chance this will end up being its true superpower, which perhaps ironically, might enable a reboot of management by pattern.
Of course, to realize this value, we will need to shift our management philosophy to actively seek a rebalancing of how we manage corporations. While GenAI might provide a means of doing so at scale, this will only happen if we embrace a new approach to business decision-making that explicitly marries quantitative data with qualitative understanding through a lens of value creation.
It will also require us to re-think how we use GenAI and other algorithmic systems within our businesses. Specifically by forcing these tools to align to the requirements of our distinct management philosophies and not the Measureship orthodoxy the builders of these tools almost certainly follow. (As an aside, any GenAI system built to support management decision making should first be trained in the management philosophy it exists to support, not the management philosophy of whoever built it, or whatever content it was trained on.)
As a “what if” along this thread of thinking, what if, outside of the myriad of dashboards and metrics that proliferate today, we also had a qualitatively trained AI inspiration partner we could work with. One that tells us stories, finds patterns, and helps shape narratives designed to influence the management decisions we then make? And what if we hired more creative, empathetic people to use these new tools, who were incentivized less to optimize efficiency and productivity and more to create breakthrough value via a superior qualitative understanding that drives delightful, joyful, results?
It’s unclear what form this might take or how fast it might appear. But what is abundantly clear is that no matter how massive the so-called “Magnificent Seven” may become, the management orthodoxy these corporations have spawned is far from the dominant force it may at first appear.
Instead, if history is any guide, it’s brittle, vulnerable, and ripe for disruption. It’s a matter of when, not if.