The Ground Shifted While You Were Managing
Post 1.1 — "The Ground Shifted While You Were Managing"
Series 1: The Information Age Has Outpaced Leadership
Published by LeAnne Coulter | BlueWave Supply Chain
There is a particular kind of blindness that comes from competence. When an executive has spent years mastering the rhythms, tools, and rules of an environment, that mastery can quietly become a liability. Keep in mind Marshall Goldsmith’s phrase: “What got you here will not get you there.” The skills that earned the corner office are not always the skills required to navigate what comes next. And for a significant portion of today's executive class, something important happened while they were busy being competent: the ground shifted beneath them.
The first step is to understand precisely when and how the environment changed. This is not an abstract perspective, but a documented sequence of transformations that each demanded a different kind of thinking from executives.
The Myth of the Stable Environment
Ask most senior executives when they believe business technology became genuinely disruptive, and they will name something recent, such as AI, cloud computing, or smartphones. This is recency bias at work. The reality is that business has been in a state of near-continuous technological transformation since the early 1960s. Why is it important to understand the scope of technological transformations? It’s not that the transformations happened. We’ve seen it and felt it. It’s the speed at which one wave ends and the next begins.
For most of the postwar era, transformation arrived in decade-long cycles. A technology would emerge, disrupt, and then stabilize. This gave organizations enough time to reorganize around it, train their people, and institutionalize their responses. Leaders could be reactive and still survive. The cycle compressed. And then it compressed again. Somewhere in that compression, a generation of executives lost the practiced muscle of creative adaptation. They weren’t lazy or incapable, but the stable intervals between disruptions rewarded a different set of skills.
Understanding what those transformation eras looked like and demanded of executives is the starting point for understanding the leadership challenge of today.
Ten Eras That Rewrote the Rules of Work
Era 1: Mainframe Computing (1960s–1970s)
The mainframe era was the first major technological forcing function for business leadership. IBM's System/360, launched in 1964, standardized enterprise computing and forced companies to reorganize entire departments around centralized data processing. For the first time, executives had to make decisions about data governance, IT investment, and the change or displacement of entire job categories, such as the payroll clerk, bookkeeper, and filing department. This was not a marginal operational change. It was an organizational redesign at scale, and the leaders who navigated it well were those who could imagine a different configuration of human and machine work. I highly recommend you watch Hidden Figures again. So many lessons held within one movie.
Era 2: The Personal Computer Revolution (Late 1970s–1980s)
The personal computer revolution was a seismic event. My father brought home a novelty of a Zenith Z-100 in 1982, and we began to learn Z-BASIC. I started college with an electric typewriter, and in less than two years was using Macs in newly converted computer labs. In business, the personal computer moved analytical and writing power from specialized operators to individual employees, changing what was expected of every worker and manager. VisiCalc (1979) replaced the manual ledger. IBM's PC (1981) decentralized computing from IT departments to individual desks. Lotus 1-2-3 (1983) and Microsoft Excel (1985) transformed financial modeling. Microsoft Word (1983) replaced dedicated word processing machines. Counter-intuitively, these tools did not reduce workloads. They expanded the complexity and volume of work. Longer reports. More detailed analyses. Higher executive expectations. The tools that were supposed to free time instead created new categories of demand.
Era 3: Rule-Based AI and Expert Systems (1970s–1980s)
This is the era most executives have forgotten entirely. Expert systems, the first commercial form of artificial intelligence, emerged in the 1970s and proliferated through the 1980s. These systems used "if-then" rule logic to simulate specialist decision-making in fields like medical diagnosis and financial analysis. Companies invested heavily, believing expert systems represented the future of AI. They didn't. The systems hit fundamental scaling limits, the economics didn't hold, and an "AI winter" followed. This first encounter with AI's promise and disappointment established a pattern that would repeat itself: technology overpromised, executives overcorrected, and the real capabilities arrived later, quieter, and harder to recognize. Sound familiar?
Era 4: The Internet and Email (1990s)
The public internet (1990) and widespread email adoption transformed communication speed, competitive intelligence, and supply chain coordination beyond recognition. Google's founding in 1998 began the shift toward information as a competitive resource. By 2000, half of U.S. households were connected to the web. This era forced companies to rethink customer interaction entirely. How people searched, bought, and engaged changed faster than most organizations could adapt. The first casualties were companies that dismissed the internet as a distribution channel rather than recognizing it as an entirely new architecture for commerce. Hello Amazon in 1995.
Era 5: Statistical Machine Learning (1990s–2000s)
As expert systems faded, another revolution emerged. Machine learning shifted from rule-based logic to data-driven pattern recognition. Algorithms learned from experience rather than following pre-coded instructions. Bayesian networks and Support Vector Machines enabled classification and prediction. Recurrent Neural Networks enabled early language modeling. Random Forests and Gradient Boosting powered fraud detection and sales analytics. Critically, this era's impact was largely invisible to executives. It ran inside systems they used but rarely saw, powering credit scoring, logistics routing, and supply chain forecasting. Leaders who were not curious about their own infrastructure missed the most consequential technology shift of the decade.
Era 6: ERP Systems (1990s)
SAP and Oracle's enterprise resource planning platforms forced companies into massive organizational redesign. The results were often more disruptive to daily workflows than the internet itself. ERP implementations required companies to rethink their data architecture, retrain their workforce, and accept that their operational processes would now conform to software logic rather than the reverse. The implementation disasters of this era, such as projects that ran years over schedule and hundreds of millions over budget, were among the first clear evidence that technology adoption is fundamentally a leadership and change management challenge, not just a technical one.
Era 7: Cloud Computing and SaaS (Mid-2000s–2010s)
Amazon Web Services launched in 2006 and fundamentally altered the economics of technology deployment. Cloud computing eliminated the need for large capital investments in physical servers and democratized access to enterprise-grade software for companies of all sizes. This quietly transformed how executives made technology investment decisions, shifting from five-year capital expenditure planning to monthly operational subscriptions. The leaders who recognized this shift gained agility. Those who remained in Capex thinking found themselves defending legacy infrastructure costs against competitors who had none.
Era 8: The Smartphone and Mobile Revolution (2007–2015)
The iPhone's launch in 2007 triggered a foundational shift. The smartphone revolution opened new business models, social channels, and customer behaviors that executives were almost universally unprepared for. Marc Andreessen's 2011 essay "Why Software Is Eating the World" articulated what many leaders were already feeling: software-centric companies had a structural advantage in every industry. This era also coined the term "Digital Transformation", thus giving a name to the adaptive challenge executives had been struggling with for years, without necessarily giving them a better strategy for meeting it.
Era 9: Deep Learning (2010s)
Deep learning became commercially feasible in the early 2010s, driving breakthroughs in computer vision, natural language processing, and recommendation systems. This was the first wave of AI that average employees encountered in their daily work tools, such as voice assistants, search relevance, and personalized content. It reshaped consumer expectations of business interaction speed and personalization in ways that most executives experienced as a customer service and marketing problem rather than a fundamental technology shift. Most of us call this the “Amazon Effect”.
Era 10: The Pandemic Acceleration (2020–2022)
COVID-19 compressed years of digital adoption into months, and it was breathtaking. Virtual meetings shifted from niche convenience to universal infrastructure overnight. Remote work, e-signatures, digital supply chains, and contactless operations became mandatory. Digital transformation spending hit $1.6 trillion in 2022 and is projected to reach $3.4 trillion in 2026. What this era proved is that many leaders had been choosing not to adopt available technologies. The pandemic removed that choice, and the results revealed how much institutional resistance had been masquerading as strategic deliberation.
The Compression Problem
What these ten eras reveal is a clear pattern, not continuous chaos. Transformation cycles that once lasted a decade now last three to five years, and in some cases compress to eighteen months. Sit with that for a moment. Astonishing.
From the 1960s through the early 2000s, the gaps between major technological shifts were long enough for organizations to absorb the change, retrain their people, stabilize their processes, and develop institutional responses. Leaders who were slow adapters could still survive because the pace of change allowed for catch-up.
That tolerance disappeared around 2007. The smartphone, cloud, deep learning, pandemic, and generative AI waves arrived in rapid sequence, each demanding a creative organizational response before the previous one was fully digested. The executives who entered their senior roles during the relative plateau of 2010–2019 (the SaaS and mobile period), when the tools were improving incrementally but the fundamental architecture of work was not, absorbed a false lesson. That the environment was essentially stable and that operational excellence was sufficient.
It was not. It is not.
What Creative Adaptation Actually Requires
Each of these transformation eras rewarded a specific form of executive creativity: the ability to imagine a new configuration of work. It wasn’t just a new tool, but a new way of organizing human effort, customer relationship, and value creation around that tool. The mainframe demanded reimagined back-office operations. The PC demanded reimagined analytical workflows. The internet demanded reimagined distribution and customer engagement. Cloud computing demanded reimagined capital allocation. Each transition required leaders to let go of processes that had worked and build new ones before the evidence for doing so was comfortable.
The leaders who succeeded in each era were not the ones with the deepest technical knowledge of the technology itself. They were the ones with enough creative confidence to ask: Given that this now exists, what becomes possible that wasn't possible before, and what does that mean for how we organize?
That question is harder than it looks. It requires tolerating ambiguity, trusting incomplete data, and making decisions whose payoff lies beyond the next earnings cycle. It requires, in a word, creativity.
That is precisely what the next posts in this series will explore. How the information explosion that accompanied these eras has quietly consumed the cognitive space where executive creativity lives, and what it will take to reclaim it.
Next in Series 1: "You're Drowning in Data and Starving for Insight" — how the knowledge doubling curve is degrading the quality of leadership decisions.
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