The evolution of AI mirrors that of microprocessors in many ways, though AI's pace is dramatically accelerated, and unfortunately accompanied by an avalanche of clickbait and poor guidance.

Predicting technology adoption and scaling remains inherently challenging. The McKinsey Intel blunder with microprocessors serves as a perfect reminder: even seasoned experts can dramatically misjudge a technology's trajectory. More than half a century later, this cautionary tale still appears in innovation textbooks and business courses.

Whatever your approach to AI, success depends on cutting through the noise to find reliable information and identifying genuinely viable use cases. Those use cases do not have to be limited to solving today’s problems.

Microprocessors. The most successful adoptions combined solving immediate problems while simultaneously exploring new possibilities.

While the early years focused on problem-solving, the real shift happened when companies started asking "What new things can we do?" rather than just "What problems can we solve?" This mindset led to:

• Personal computers (IBM, Apple, and others saw the opportunity to create entirely new markets)

• Consumer electronics with digital features that weren't solving existing problems but creating new capabilities

• Industrial automation that went beyond replacing manual processes to enabling new manufacturing approaches

Key Pattern: Companies that thrived long-term were those that made the mental shift from viewing microprocessors as problem-solvers to seeing them as opportunity enablers. Those who remained purely reactive often found themselves playing catch-up as more visionary competitors reshaped entire industries.

Several major companies missed significant opportunities by focusing too narrowly on solving existing problems rather than exploring what microprocessors could enable:

Digital Equipment Corporation (DEC) DEC dominated the minicomputer market and viewed microprocessors as toys that couldn't handle "real" computing. They were solving enterprise computing problems brilliantly but couldn't see that microprocessors would eventually become powerful enough to disrupt their entire market. Ken Olsen famously said in 1977 there was "no reason for any individual to have a computer in his home."

Wang Laboratories Wang was incredibly successful with dedicated word processors and saw microprocessors mainly as a way to make their existing products cheaper. They missed that general-purpose PCs could do word processing plus everything else, making dedicated word processors obsolete.

Traditional Watch Companies (Swiss) When digital watches emerged, most Swiss watchmakers saw microprocessors as a threat to mechanical craftsmanship rather than an opportunity to create new categories of timepieces. They focused on solving traditional timekeeping problems while companies like Casio and later Seiko created entirely new markets with calculator watches, sports watches, and eventually smartwatches.

Established Calculator Companies Companies like Monroe, Friden, and Burroughs were focused on making better mechanical and electronic calculators. They missed that microprocessors would enable personal computers that could do calculations plus word processing, games, and business applications.

Traditional Automotive Electronics Suppliers Many focused on solving specific automotive problems (better radios, simpler wiring) but missed the opportunity to become platform providers for what would become comprehensive vehicle management systems.

The pattern: These companies had deep expertise in their domains but focused only on solving problems. They couldn't envision how microprocessors might create entirely new product categories or business models.

Insurance's Microprocessor Moment. Starting with AI to solve existing problems such as faster claims processing, improving data intake, better underwriting or improved fraud detection is a natural and necessary entry point, just as it was with microprocessors. However, the real winners won't be those who simply digitize yesterday's processes. The transformation happens when companies begin asking "What new things can we do?" AI could enable continuous risk monitoring that prevents losses before they occur, dynamic coverage that adapts in real-time, and entirely new ways of thinking about risk transfer. The companies that thrive long-term will be those who make the mental shift from viewing AI as just a better way to do insurance, to seeing it as an opportunity to redefine what insurance could become.