The third decade of McKinsey’s journey, from 1946 to 1956, marks a critical transition in technology’s societal role. The breakthroughs of the previous decade—many forged under the extreme pressures of war—expanded during this decade far beyond military settings. Technology moved into civilian life, reshaping industries, economies, and everyday experiences. Many consumers in postwar countries embraced technological change as a positive force for good.
Across energy, computing, biology, and transportation, technology moved out of experimentation into wide application. Technologies developed for specific military purposes had to prove themselves in the complexity of the real world, where cost, reliability, safety, and public acceptance matter. For today’s technology leaders navigating AI deployments, the parallels are direct. AI is here to stay, and the question is no longer whether AI will work but how to harness its power to create lasting value.
Energy drives economic growth
Electricity had already become a core part of industrial and urban life by the early 20th century. In the decade after 1946, advances in generation and distribution, combined with falling costs, brought electric appliances such as refrigerators, washing machines, and televisions into middle-class homes. Electricity became an essential utility and drove widespread economic growth.
At the same time, nuclear energy began to move from theory into real-world application. In 1954, the Obninsk Nuclear Power Plant in the Soviet Union became the world’s first grid-connected nuclear power station, demonstrating that atomic energy could be used for continuous civilian power generation. While nuclear energy’s origins were rooted in wartime research, its postwar development focused on civilian applications. Nuclear power promised abundant, low-cost energy, but it also introduced new complexities regarding safety and long-term sustainability. The dual nature of this technology—high potential paired with significant risk—became apparent early on.
What can modern technology leaders learn from that decade’s energy shift? The true impact of a technology often emerges only when it becomes embedded in everyday workflows and experiences. AI is now approaching a similar inflection point. Early use cases have demonstrated potential, but the real value lies in integrating AI into core business processes and customer journeys. That integration requires companies to carefully prioritize use cases, balancing short-term gains with long-term implications, and ensure that risks are understood and managed from the outset.
Biology reveals its underlying code
One of the biggest scientific breakthroughs of the decade came in 1953 with the discovery of the structure of DNA. For the first time, scientists understood the molecular basis of heredity, unlocking the ability to understand the fundamental building blocks of life. This discovery laid the groundwork for advances in fields such as medicine and biotechnology that would unfold over decades—and are still unfolding today.
That progress quickly began to translate into real-world impact. In 1955, Jonas Salk’s polio vaccine was deployed at scale, marking one of the first times a breakthrough in biological science was rapidly turned into a global public health intervention.
The implications of DNA discovery were far-reaching. Improved understanding of genetics enabled the development of new treatments, more resilient crops, and entirely new fields of research. At the same time, the ability to alter biological systems raised ethical and societal questions that remain unresolved. Public perception varied widely, shaping the pace and direction of DNA research and manipulation.
Technology leaders today can find parallels in DNA’s discovery to their own transformation work. Technologies that alter underlying foundational systems—whether biological or digital—can’t scale on merit alone. They require trust, alignment with societal values, and thoughtful governance. AI, of course, faces similar challenges. Its capabilities are advancing rapidly, and it will soon unlock untold discoveries, but widespread adoption depends on how well organizations address concerns about trust and how well leaders support their teams with effective change management. CIOs must therefore think beyond technical deployment and actively shape how their technologies are perceived and governed.
Computing becomes practical
The decade from 1946 to 1956 also marked a turning point in the evolution of computing. Early machines developed during the war were large, fragile, and difficult to operate. Their potential was clear, but their practicality was limited. The invention of the transistor in 1947 changed that trajectory. By replacing bulky and unreliable vacuum tubes, transistors enabled smaller, more reliable, and more efficient machines, setting the stage for modern computing.
As reliability improved, computing began to move beyond experimental settings. In 1951, the Universal Automatic Computer (UNIVAC) became the first commercially available computer in the United States, used by organizations such as the US Census Bureau to process data at a scale previously impossible.
Of course, computers were still rare and not widely adopted. But as computing systems became more cost-effective, new applications emerged across industries. Over time, computing moved from specialized environments into broader organizational use, ultimately becoming the backbone of modern enterprises.
As CIOs approach the future—trying to implement AI into legacy tech stacks in ways that enable a full rewiring, not just a patchwork transformation—they can learn from how computing evolved from 1946 onward. Breakthrough performance is rarely enough to drive adoption. Reliability, cost efficiency, and scalability are what determine whether a technology can rewire everything about how we work. With AI, early models demonstrated impressive capabilities, but only with improvements in reliability and integration have they begun to unlock enterprise-wide use cases. Leaders who focus on strengthening these enabling factors will be better positioned to scale AI beyond isolated pilots.
Innovation meets the realities of risk
Military technologies such as jet propulsion and early computing moved from controlled environments into widespread use, but not without failures. In aviation, the introduction of jet-powered commercial aircraft marked a major step forward. Yet early models, such as the de Havilland Comet, experienced catastrophic failures due to structural fatigue—issues that became apparent only under real-world operating conditions. These incidents forced engineers to rethink designs and safety standards.
In medicine, similar challenges emerged. The mass production and distribution of vaccines introduced new complexities in quality control. The Cutter incident in 1955, in which improperly inactivated polio vaccines led to infections, highlighted the risks associated with scaling life-saving technologies without sufficient safeguards.
These examples illustrate a broader pattern that technology leaders know well. Technologies that perform well in controlled settings can fail when exposed to the variability and scale of real-world use. The response in both aviation and medicine was the development of more-rigorous testing protocols, regulatory frameworks, and operational controls. With AI systems now being deployed in increasingly critical contexts, failure points will emerge that cannot be predicted. Ensuring responsible AI requires robust governance, continuous monitoring, and clear accountability. Organizations that invest early in building trust with AI—and in training their people how to partner safely with AI agents—will be better equipped to scale safely and sustain trust.
A leadership lesson that endures
The decade from 1946 to 1956 was when technology found its purpose. Breakthroughs developed in earlier years began to shape industries and societies—not because they were new, but because they were applied to the real world. Success depended on the ability to identify where technologies could create value, adapt them to real-world conditions, and manage the risks that emerged along the way.
That same challenge defines the role of today’s CIO. Advanced technologies such as AI offer unprecedented potential, but realizing that potential requires more than technical expertise. It requires disciplined execution, thoughtful governance, and a relentless focus on where and how technology is used. The leaders who succeed will be those who move beyond experimentation and turn technological possibility into sustained impact.
Chandrasekhar Panda is a partner in McKinsey’s Riyadh office, Henning Soller is a partner in the Frankfurt office, Klemens Hjartar is a senior partner in the Copenhagen office, and Sven Blumberg is a senior partner in the Istanbul office.