You’ve probably heard the old saying, “when everything is a priority, nothing is a priority.” I submit to you, dear reader, that this is also true of artificial intelligence. When everything is AI, nothing is AI.

Just ask the person who is literally the Michael Jordan of machine learning. We haven’t truly created artificial intelligence in the traditional sense of the term. We’ve built some really cool tools that process information, identify patterns, and make connections that would take humans years to realize. But even the most modern innovations are an augmentation of human intelligence and decision-making, rather than a full replacement.

Nonetheless, AI has become a discipline shrouded in hype and mystique. Conceptually, it has been around for more than 60 years, but it took until the 21^st century for the term to be applied to products and services ranging from advanced analytics, to robotic process automation, to machine learning. It was all “AI,” even though none of it lived up to the expectations set by sci-fi movies and technology futurists.

So why the disconnect?

When we talk about AI, we tend to comingle two distinct concepts: artificial narrow intelligence, and artificial general intelligence.

• Artificial narrow intelligence is built to solve one specific problem. Analyze a giant data set for patterns. Watch for signs of fraudulent activity among banking transactions. Create an automated, yet human-feeling customer support interaction. This is the type of AI we see right now, every day.
• Artificial general intelligence basically amounts to reverse engineering the human brain and letting it learn. That’s not where we are – and it’s not where we need to be for “AI” as we know it to provide value.

And it’s here where the technology adoption curve fails us.

If you’re not familiar, Everett Rogers introduced a theory of how people adopt new ideas and technology in his seminal work, Diffusion of Innovations. It describes adoption along the lines of a bell curve, with the less risk-averse categories of innovators and early adopters acting as agents of change that help a new idea spread, while benefiting from the early access they obtain by opting in quickly. Modern takes on the concept tend to favor those ahead of the curve, using terms such as “visionaries” and “thought leaders,” whereas later adopters are considered “reactives” and “skeptics”.

So who are the early adopters of AI?

Well, it took decades for AI to move from an academic concept to market ubiquity – so “early” here is a relative term. Nonetheless, most would agree that those who jumped in with both feet at the turn of this century were ahead of the curve. In the interim 20+ years, we’ve witnessed organizations of all sizes build AI operations and deploy solutions to tackle their biggest challenges. But we’ve also watched AI make huge advances in the size and scale of the problems it can solve. Increases in both storage capacity and processing speed make AI much “smarter,” even if we’re not quite yet near artificial general intelligence.

This means that you can still be an early adopter, 20 years later. And companies that have adopted already can adopt early again. There is a whole marketplace of products now that use AI to solve specific problems; you don’t need a team of data scientists and machine learning engineers to get started. The adoption cycle becomes less of a “curve” and more of a hockey stick – and it’s not a coincidence that this resembles Moore’s law, the concept that raw computing power doubles roughly every 10 years.

Related Reading: AI… We Can’t Be Too Late

With AI, the bias that typically favors the traditional idea of an “early adopter” fades away. No matter when and where you jump in, you’re always early. You get the best of both worlds; you can get in on the ground floor of innovations that will give you a competitive edge, while enjoying the benefits of a proven, mature technology.

So let’s peel back the mystique and aura. Let’s talk about “AI” not in terms of the novelty of the technology, for better or worse, but rather the problems it can solve.

In my next blog, I’ll talk about how organizations can combine the promise of AI and advanced analytics with the power of human ingenuity to create amazing experiences for their constituents, customers, users, and employees.