As we reflect on 100 years of technological progress, we see more than a catalog of inventions; we see ideas that evolved into systems. The first decade of McKinsey’s journey, from 1926 to 1936, represents a critical inflection point. This was a period in which modern technology began to transition from isolated innovation to industrialized, networked deployment. This was the decade when technology stopped being episodic and started becoming systemic.
Across aviation, manufacturing, media, and fundamental science, the most consequential advances of the period were not singular eureka moments. Quietly at first, and then faster at the end of the decade, these new technologies altered how organizations, economies, and societies functioned. Individual technological advances transitioned into scalable systems. For technology leaders navigating today’s transitions—such as shifting AI pilots to full-scale agentic platforms or migrating cloud applications to cloud-native operations—the parallels are striking.
Heroic feats become dependable systems
Aviation provides the clearest illustration of how technology evolved between 1926 and 1936 and is the closest parallel to today. By the mid-1920s, powered flight was no longer a novelty. Aircraft had already crossed oceans and captured public imagination. What changed most about aviation during these ten years was not just that they could travel faster and farther, but that they could so reliably. This shift happened when commercial aviation emerged as an industry. Airline companies learned to operate aircraft safely and predictably. This required advances far beyond airframes and engines, including standardized maintenance practices, pilot training programs, air traffic procedures, route planning, weather forecasting, and regulatory oversight. Airlines began to resemble logistics companies rather than daredevil clubs.
The lesson for modern technology leaders is enduring: The moment a technology becomes strategically important is the point at which its surrounding operating model matters more than its core performance metrics. Many AI deployments today sit precisely at this inflection point. The models exist. The demos impress. What separates leaders from laggards is not how many AI experiments they have tried or how far they have pushed the models but the ability to industrialize deployment. High-performing companies have surpassed AI pilots to transform into agentic organizations. They consider governance, monitoring, talent and operating models, and accountability for outcomes as important as technology deployment. They use AI to reconfigure entire ways of working and to equip their teams with superpowers.
Aviation’s first commercial decade reminds us that scale is rarely achieved by brilliance alone. It is achieved by the hard work of systems thinking.
Manufacturing learns to think in processes, not products
During the 1926–36 decade, a similar shift occurred in manufacturing. The automobile was no longer new in the late 1920s. What was new was the maturation of operational discipline: standardized parts, assembly-line optimization, supply chain coordination, and statistical approaches to quality.
This period cemented the idea that competitive advantage could be engineered into processes, not just products. Cost curves began to bend downward not because cars became radically different but because companies learned how to produce them consistently and efficiently at scale.
This same logic underpins every modern technology transformation. Cloud computing, for example, did not win because virtual machines were conceptually revolutionary. It won because hyperscalers embedded operational excellence into provisioning, reliability, security, and pricing. They turned compute power into an industrial utility.
CTOs looking to generate measurable value from their technology migrations today would do well to remember that technology for technology’s sake does not deliver ROI. Instead, true value comes from tying technology to clear organizational processes mapped to business goals. Managing deployment pipelines, cybersecurity, vendor orchestration, change management, and financial accountability are what make technology transformations stick.
Media becomes a platform business
The 1926–36 decade also paved the way for media outlets to become true technology platforms. Radio networks expanded rapidly, creating shared national experiences in real time. Film transitioned from silent pictures to synchronized sound, triggering massive reinvention across production, distribution, and talent ecosystems.
What made these technologies powerful was not merely their reach, but their economics. Advertising models, sponsorships, and distribution agreements emerged that allowed content to be monetized at scale. Technical and contractual standards enabled interoperability across regions and devices. In retrospect, radio networks look like early prototypes of today’s digital platforms: centralized infrastructure supporting decentralized content creation, governed by evolving rules and incentives.
For today’s CTOs, the relevance of yesterday’s media landscape to today’s technology road map is clear. Platform dynamics—such as who controls interfaces, who captures data, and who sets standards—often matter more than the underlying technology stack. The winners are rarely those who invent first; they are those who architect ecosystems.
The quiet power of foundational science
Not all of this decade’s most important advances were visible at the time. In physics, the development of quantum mechanics altered humanity’s understanding of matter and energy. Its immediate applications were limited, and its implications were opaque to most business leaders of the era. A hundred years later, quantum computing is moving from a bold idea to a multibillion-dollar industry with real measurable value. In fact, every technology that defines modern life, including semiconductors, lasers, and sensors, rests on scientific principles established in the decade after 1926.
This is a reminder that companies that optimize for near-term ROI risk starving themselves of future innovation. For companies looking to transform into agentic organizations, this means taking a portfolio approach that balances technology investment. First-movers fund applied AI engineering with selective investment in foundational capabilities such as research and skill building. These investments may not monetize quickly, but they can reshape competitive advantage over time.
When elegance fails to scale
The decade of 1926 to 1936 also offers cautionary tales. Airships, or dirigibles, represented a technologically elegant solution to long-distance travel. Yet safety concerns and economic constraints ultimately doomed commercial blimps. This pattern repeats throughout technological history. Elegance without resilience rarely survives contact with scale.
High-performance companies know that pilots that perform beautifully in controlled environments can collapse under real-world complexity—and nowhere is this truer than with AI. Experimentation with agentic platforms leads to learning and innovation, but it’s also a messy process to engage with a brand new technology evolving at light speed. To make this type of experimentation scalable and impactful, companies need responsible AI guardrails, robust security, and clear governance. These are prerequisites for durability.
A leadership lesson that endures
The 1926–36 decade was when technology stopped being something organizations used and became something organizations had to manage. Strategy, operations, governance, and talent all became inseparable from technical capability. The leaders who succeeded in this era were not necessarily the greatest inventors. They were the ones who understood how to embed technology into institutions. They built operating discipline and made innovation repeatable by creating governance frameworks.
That same reality defines the modern CIO role. Today’s technology tools are different from 100 years ago. The scale of disruption is larger, and the potential benefits—and risks—are greater. But the work of turning raw promise into measurable value remains unchanged.
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.