Innovation for innovation’s sake is one of the most pernicious issues I see in the tech world right now. Last year, venture capital investments in the AI industry skyrocketed to over $12 billion, almost half of which was invested in the US AI market, according to a recent Drake Star Partners report.

But despite industry growth and all the companies that talk about AI, we have yet to realize anything that comes close to the lofty goal of true artificial intelligence. More often than not, when businesses claim to be working or improving their product with AI, it is nothing more than the sound implementation of well-established machine learning algorithms against specific use cases.

These overblown claims have at best turned “AI” into the marketing buzzword du jour. At worst, the technology won’t be taken seriously as a result of industry failure and false promises.

That’s why in 2019, I’m calling for marketers to embrace a resolution: Stop the AI-hype cycle.

VCs and CEOs are enthralled by the possibility of capturing a piece of the revenue pie that AI seems to promise — perhaps with good reason, since adding artificial intelligence capabilities can be an attractive portfolio-add for potential investors and boards alike.

But such an enchantment with technology doesn’t mean it’s truly valuable from a business standpoint. Many companies don’t need automation or machine learning-based solutions, and those that do can generally satisfy their needs with tried and true techniques.

The fact of the matter is this: CEOs are being pitched big-picture ideas when the technology, and more importantly the data, isn’t ready, leaving developers caught in the middle attempting to build something that can’t be put into practice. At the same time, traditional marketing surrounds these products-to-be and confuses customers who struggle to decipher all of the AI talk floating around.

And this is where the descent down the slippery slope of innovation for innovation’s sake begins. We’re overlooking the very real, and proven power of machine learning or automation, in favor of this decade’s technological white whale: artificial intelligence.  

Only a few companies have the resources and infrastructure to invest in the pursuit of AI, and even when the technology is possible, practitioners struggle to fully grasp the implications. We’ve seen how the development of new tools or software can be greatly compromised by biased data, even if involuntary. Who could have predicted that we would eventually face deep fakes, or that Twitter dialogues would induce a bot to lean toward outright racism within less than 24 hours?

The current benefits of machine learning are undeniable. These awesome tools dictate how we search, recommend what to buy, and tell us when our car will arrive. But just because we are scratching the surface of how we can use them in a practical way, they are not suddenly what researchers, data scientists or the general public should consider as artificial intelligence.

If we continue to overplay our current AI hand, consumers are never going to take the technology seriously, much less understand the potential that it holds.

We are still  far way away from AI becoming a reality. Many people realize this cold truth, but many more don’t — or worse, fail to acknowledge it. Instead of contributing to the AI-hype cycle, we should highlight the very real impact that machine learning has had on our lives so far.

The path towards artificial intelligence means confronting our own prejudices, and understanding how they impact technological development. In celebrating the current state and true potential of machine learning, we can appeal to a wider and more diverse audience — an audience who will innovate and create products that can improve the makeup of our society and provide real solutions to day-to-day problems.