The fifth decade of McKinsey’s journey, from 1966 to 1976, saw technology move from inventions to systems backed by innovation. Earlier decades produced remarkable advances: computers, jet engines, nuclear power, and telecommunications. But during this decade, the most important breakthroughs came not from the creation of entirely new technologies, but from connecting existing ones into systems.
Networks linked computers. Communications protocols began moving data across vast distances. And the Apollo space missions demonstrated what could be achieved when thousands of technologies, organizations, and people worked together toward an audacious goal: putting people on the moon for the first time.
The broader backdrop was one of both optimism and uncertainty. The decade began with economies in North America and Western Europe enjoying rising living standards and confidence in scientific progress. It ended amid stagflation triggered by the 1973 oil crisis—forcing companies to confront a hard truth: Technological ambition and interconnected systems matter, but lasting success depends on creating value under real-world constraints.
For today’s technology leaders, there are visible parallels. AI models, cloud platforms, and digital tools are widely available and less expensive than old-school enterprise software. The differentiator is no longer access to technology, but how effectively organizations can connect those technologies to their data, processes, and operating models to create measurable business value.
The network is the computer
Before the late 1960s, computers were largely isolated systems. They performed calculations, stored information, and automated specific tasks, but they operated independently. That began to change in 1969 with the launch of ARPANET, the first operational packet-switching network that connected computers at Stanford Research Institute, UCLA, UC Santa Barbara, and the University of Utah.
In the early 1970s, researchers were also developing a new networking standard that would later become transmission control protocol/internet protocol (TCP/IP)—giving disparate computer networks a common language to exchange information. Meanwhile, optical fiber breakthroughs made it possible to reliably transmit increasing volumes of data over long distances. These foundational technologies became the basis of the modern internet. More significantly, they demonstrated that interoperability could unlock exponentially more value than any individual technology operating alone.
Today, companies have access to powerful AI tools. But most struggle to integrate them into enterprise workflows, business applications, and decision-making processes at scale. The companies creating the most value from AI are not deploying isolated copilots. These companies have chief information officers (CIOs) who understand that transforming into agentic organizations—where data, applications, agents, and people work together seamlessly—is the only way forward.
Software decouples from hardware
A second defining development of the decade was the emergence of software platforms that made computing more scalable. The creation of the UNIX operating system at Bell Labs in 1969 introduced a new approach to using software. Rather than being tightly coupled to specific hardware, different software applications could increasingly operate across multiple machines through standardized interfaces.
Meanwhile, the invention of the microprocessor in 1971 condensed the central processing unit onto a single chip—and changed how software was developed. Computing power became smaller, cheaper, and more portable. Together, these advances transformed computers from specialized to general-purpose machines. Organizations no longer needed to build entirely new systems to solve every new problem. Instead, they could build multiple software applications on top of a common technological foundation.
Technology leaders face a similar expansive opportunity today. AI is rapidly becoming a foundational layer rather than a stand-alone application. To stay competitive, companies will increasingly need to rethink their entire approach to building tech stacks. They’ll need to build the foundations for agentic AI at scale by linking data, workflows, and products on top of common AI platforms. Success with agentic AI will depend on creating the underlying architecture that allows innovation to scale across the enterprise.
Ambition succeeds through integration
No technological achievement better illustrates the power of systems integration than the Apollo program. The Moon landing in 1969 is often remembered as a triumph of engineering, but it was equally a triumph of coordination. More than 400,000 people worked across government agencies, universities, and private contractors to deliver a mission that required thousands of technologies to function together flawlessly.
The achievement is even more remarkable considering the setbacks that preceded it. In 1967, the Apollo 1 fire killed three astronauts during a launch rehearsal, exposing serious design and operational failures. Rather than abandoning the mission, NASA redesigned critical systems, strengthened testing procedures, and moved forward with a clearer understanding of the risks involved.
Every CIO knows that large-scale technology transformations rarely proceed in a straight line. Challenges emerge and failures occur. This is especially true in the age of AI, where early experimentation is essential but outcomes are not yet assured. The companies that generate mensurable ROI from their AI investments will be those that experiment, learn quickly, and recalibrate often to achieve on business objectives.
When technical brilliance fails to create value
The decade also offers an important cautionary tale of how even the most interconnected systems can fail if they don’t create real value. The Concorde supersonic airliner, which first flew in 1969 and began commercial service in 1976, demonstrated what was possible when engineering advances in aerodynamics, materials science, and propulsion were combined into a single system. The aircraft cut transatlantic travel time dramatically and remains one of the most advanced commercial aircraft ever built.
Yet the Concorde never moved beyond a niche market. High operating costs, noise restrictions, and fuel consumption limited its value for both airlines and passengers. The technology worked as intended, but the broader system around it—economics, regulation, and market demand—limited its long-term impact.
This lesson remains highly relevant in the age of AI. Organizations are often drawn to the most advanced models and the most impressive demonstrations. Yet creating real business value at scale depends on rewiring operating models for and with AI, not just hastily adding point solutions. AI deployed reliably, integrated effectively, and governed responsibly becomes a value-creating system.
What leaders can learn
The decade from 1966 to 1976 was when technology truly stopped operating in isolation. The breakthroughs that mattered most were not individual inventions but systems that connected people, machines, and information. Networks became more valuable than stand-alone computers. Platforms became more powerful than individual applications. Integration—of software, hardware, communications, and data—emerged as a competitive advantage.
That same reality defines the challenge facing today’s technology leaders. The companies that capture the greatest value from AI will not necessarily be those with access to the most advanced models. They will be the ones that rewire themselves from the inside out, connecting technology across workflows, data, operating models, and people. As in the late 1960s and early 1970s, transformative value won’t come from individual technologies, but from the integrated systems that bring them together.
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.