I recently sat on a panel with Amy Hu of H&R Block and Andrea McCullough of Dunkin’ Donuts about how machine learning impacts martech. The moderator asked, “Has artificial intelligence actually been achieved, or is what we’re seeing in the market right now machine learning?” Amy was adamant in her response, “No, it doesn’t exist,” to which I followed with an emphatic “Yes, it does.”

With the World Economic Forum now predicting that machines will do more workplace tasks than humans by 2025, it’s difficult to deny that artificial intelligence (AI) exists. But it’s also often difficult to recognize AI, even when it’s right in front of you.

When the first artificial intelligence business solutions began emerging a few years ago, I remember someone saying, “People expect artificial intelligence to feel magical, like a laser show or something.” This isn’t usually the case, of course. Currently, most early business adopters are using machine learning and AI to address legacy issues that their industries haven’t been able to.

In marketing and retail, these legacy issues stem from a convoluted digital landscape that continues to evolve faster than brands and marketers can keep up with (e.g., too many channels, too many devices, too much data and too little transparency).

Additionally, there are internal issues that create unnecessary complexities. Brands’ tech stacks are often convoluted and unmanageable. They have disparate data sources across departments, channels and vendors. They’re often working with multiple vendors who aren’t interacting with one another. And because processes are so manual, it’s difficult to scale them significantly without scaling resources.

Against this backdrop, artificial intelligence’s inevitable first job with any company is to act as a fixer. From there, its next job is to drastically scale the efforts the company had put in place pre-AI.

Amy Hu of H&R Block described their use of machine learning as “fast-paced technology, but old-fashioned marketing.”

Andrea McCullough shared how Dunkin’ is using machine learning to recommend products to guests, increase sales and build out their loyalty program. A customer stepping into a Dunkin’ Donuts, for instance, might receive a notification asking, “Would you like a blueberry donut with that coffee?”

In other words, Dunkin’ Donuts is using some of the most cutting-edge technology in the world to perfect the age-old art of the upsell. This is something that goes back to 19th-century retail when proprietors knew their customers and could predict what they might like. The better the prediction, the better their business. This fundamental philosophy hasn’t changed; it’s just taken on a different format and must be deployed at scale.

Businesses are using AI and machine learning to scale the things they’ve always done at a level they never imagined possible (say, running 20,000 campaigns simultaneously). From an outside view, this might not look cutting edge. It seems like more of the same. A lot more.

For brands like H&R Block and Dunkin’ Donuts, advanced technologies are enabling a necessary course correction. In the process of solving problems that humans aren’t equipped to address manually, AI is returning brands to their human roots.

For marketers and retailers, this means a return to strategy — to dreaming up campaigns or experiences for new audiences the AI has discovered. It’s ramping up creative output, enhancing consumers’ engagement with their brands, exploring new ideas and, generally, unearthing themselves from the manual, data-centric and technical matters that had them acting more like robots than humans for the last decade.

Machines aren’t visionaries, after all. CMOs, CEOs, marketing people — they come up with the vision. They come up with the experience. The AI platform then does the work. This is where the AI stops and the human starts.

As AI games researcher Mike Cook said in response to human gamers’ recent victory over an AI when playing eSports game Dota 2, “The bots are still very good at moment-to-moment, but they seem bad at macro-level decisions.”

It’s important to note, however, that man and machine are not at odds. Humans will continue to lead vision. AI will step in to fix the things that humans broke while trying to execute on that vision alone. And they’ll do it in a way that’s both highly complex and totally simple, giving their human counterparts the luxury of not having to do the work of machines.

In a way, relying on machines to be more human is as cutting edge as it gets. More cutting edge, even, than a laser show.

Article previously featured in Forbes, Machines Are Fixers; Humans Are Visionaries, October 26, 2018