Hi,

When we look at the technological shifts happening around us today, it is easy to feel as though artificial intelligence arrived overnight.

It seems that one day, AI was a science-fiction concept, and the next, it started to write reports, summarize documents, generate imagery, and operate with increasing autonomy.

That being said, artificial intelligence has been ninety years in the making.

Understanding this long trajectory is not just a lesson in technological history; it also gives us a practical lesson to reflect on.

The history of AI teaches us that rigid systems inevitably break, while adaptive systems redefine the future.

To understand where artificial intelligence is heading into the era of agentic workflows and advanced autonomy, let's take a look back at how far we have come.

The earliest substantial work in the field of artificial intelligence was done in the mid-twentieth century by the British logician and computer pioneer Alan Turing.

In 1936, Turing described an abstract computing machine consisting of limitless memory and a scanner that moves back and forth through memory, reading and writing symbols.

This became known as the universal Turing machine, which forms the core foundation of all modern digital computers.

During World War II, while working as a leading cryptanalyst, Turing gave considerable thought to how computers could learn from experience and adapt to solve new problems through practical heuristics.

In 1948, he introduced many central concepts of artificial intelligence in an unpublished report, including the original idea of training a network of artificial neurons to perform specific tasks.

He also proposed the Turing Test in 1950 as a gold standard for measuring machine intelligence, suggesting that if a human communicating via a keyboard could not tell whether they were talking to another person or a machine, the system was deemed intelligent.

Turing also used chess as a theoretical model for machine intelligence, recognizing that a computer could not exhaustively search all moves and would need quick rules to adapt its choices dynamically.

By 1956, the term artificial intelligence was officially coined by John McCarthy during the Dartmouth Summer Research Project.

In the decades that followed, pioneers programmed systems using specialized languages like Lisp, created by McCarthy in 1958 for list processing and recursion, and Prolog, developed in 1972 for logic programming.

Back then, making a system smarter meant going back in and manually writing more code.

Because these early systems were meticulously programmed rather than learning from data, they lacked the ability to adapt on their own.

Early milestones included programs like the Logic Theorist in 1956, which could prove mathematical theorems, and the General Problem Solver in 1957, which tackled various logic puzzles.

In the 1980s, the business world experienced a massive wave of excitement around expert systems, which were designed to solve specialized problems requiring human expertise.

Companies expected them to revolutionize decision-making across entire industries.

Instead, these systems proved brittle and failed because they could not adapt quickly to changing conditions, leading to a period of deep disappointment known as an AI winter.

Yet, beneath the hype, critical milestones were quietly laying the groundwork for true machine learning.

In 1997, IBM built Deep Blue, which defeated world chess champion Garry Kasparov, proving that computation could match human strategy, planning, and creativity.

In 2011, IBM created Watson, which triumphed on the game show Jeopardy!, tackling the vagaries of human language, such as idioms, puns, and natural nuances, against all-time human champions.

Then, around 2022, the generative AI inflection point arrived.

With foundation models and advanced neural networks, AI shifted from static programming to dynamic pattern matching on a massive scale, allowing technology to finally adapt to broad human communication.

Today, we are witnessing the rise of agentic AI, where systems are given greater autonomy to adapt and operate on their own towards specific goals.

There is a powerful parallel here between technological evolution and business strategy.

In business, just as in technology, we often experience long periods of foundational work that feel like a snail's pace, followed by a sudden inflection point where everything accelerates.

The temptation in leadership is to cling to rigid methods until disruption forces our hand, or conversely, to expect immediate miracles before foundational processes are mature.

True strategic foresight requires us to continually adapt our approach as the environment around us changes.

So what does this mean for your organization today?

It means that the tools at your disposal are no longer just static databases, as they are now capable of learning and executing complex, autonomous workflows.

As we navigate this new frontier together, the question is not just what AI can do, but how we adapt our operations to build better enterprises, stronger communities, and improved ecosystems.

Unless you adapt your leadership and strategy to leverage these learning systems, you may overlook major opportunities.

What has been your experience with adapting your workflows or business strategy to AI?

Are you seeing successful opportunities for adaptation with AI?

Thank you for taking the time to read and reflect.

Until next time,​
​Alex Rășcanu​
​

P.S. If you'd like to read the past e-newsletters focused on life lessons, you can find them here.

P.P.S. See you at one of the upcoming monthly ExperienceTO historical tours, such as the University of Toronto one on Saturday, October 10.