AI is projected to contribute $15.7 trillion to the global economy by 2030. Ninety-four percent of business leaders believe AI will be critical to their organization's success in the next five years.
Yet most companies are struggling to bridge the gap between AI's promise and its practical implementation.
To understand where we're going, it helps to understand how we got here.
What Is AI, Really?
At its core, AI isn't magic—it's math, data, and algorithms working together to mimic aspects of human cognition. Strip away the hype, and you find systems that recognize patterns, make predictions, and generate outputs based on what they've learned from vast amounts of data.
When deployed correctly, these systems can augment human abilities, automate repetitive tasks, and uncover insights hidden in complexity. But "deployed correctly" is doing a lot of work in that sentence—and it's where most organizations stumble.
The Long Road to Today
The Beginning (1950s)
The journey began in 1950 when Alan Turing published "Computing Machinery and Intelligence" and posed a deceptively simple question: Can machines think? He proposed what we now call the Turing Test—a benchmark for machine intelligence that we're still debating today.
By 1956, the term "Artificial Intelligence" was officially born at the Dartmouth Conference, where researchers optimistically predicted that machines would match human intelligence within a generation.
The Winters (1970s-1990s)
They were wrong. The path wasn't smooth. We've seen two "AI winters"—extended periods where progress stalled, funding dried up, and the field retreated from ambitious promises.
The first winter came when early AI systems couldn't scale beyond toy problems. The second arrived when expert systems—the hot AI approach of the 1980s—proved too brittle and expensive to maintain.
Each winter taught the field humility. Grand promises gave way to incremental progress.
The Quiet Revolution (2000s-2010s)
While AI winters dominated the headlines, three developments were quietly setting the stage for everything that followed:
The data explosion. The internet, mobile devices, and social media generated unprecedented amounts of data. AI systems are only as good as their training data, and suddenly there was more training data than anyone knew what to do with.
Computational power. GPUs, originally designed for video games, turned out to be perfect for the parallel processing AI algorithms require. What once took months could now be done in hours.
Deep learning breakthroughs. In 2012, a neural network called AlexNet won an image recognition competition by such a dramatic margin that it reignited interest in approaches that had been dismissed for decades.
The Generative Era (2020s)
Then came ChatGPT in November 2022, and everything changed.
Not because the underlying technology was fundamentally new—large language models had been developing for years. What changed was accessibility. Suddenly, anyone could interact with AI through natural conversation. No programming required. No technical expertise needed.
The interface breakthrough mattered as much as the capability breakthrough. AI went from something specialists used to something everyone could explore.
The Gap That Remains
Despite this progress, most organizations are still stuck between AI's potential and its practical implementation. The technology is ready. The business case is clear. But successful deployment remains elusive.
The reasons are rarely technical:
- Unclear use cases — Organizations know AI is important but don't know where to start
- Skills gaps — Teams lack the knowledge to evaluate, implement, and maintain AI solutions
- Integration challenges — AI tools don't easily connect to existing workflows and systems
- Change resistance — Employees fear disruption; leaders underestimate the human side of transformation
What Comes Next
Closing the adoption gap requires more than better technology. It requires a systematic approach to identifying opportunities, implementing solutions, and driving adoption—what we call AI Operations.
The companies that figure this out won't just use AI. They'll be transformed by it. The rest will watch from the sidelines as the $15.7 trillion opportunity passes them by.