AI hype has become intense, and understandably so. The technology has become part of everyday life, whether it’s Netflix predicting what shows we might like based on previous choices, Google’s search results consistently improving based on millions of people’s clicks, or conversational AI systems like Amazon’s Alexa getting to know you. Meanwhile, virtually invisible to mainstream consumers, the business world is agog over harnessing AI to solve a range of problems.

It’s human nature to latch onto the next big thing, and we’ve seen it many times before with other tech buzzwords of the moment: big data, the cloud, software as a service (SaaS), mobile, Web 2.0—the list goes on and on.

But overuse—or flat-out misuse—of the term “AI” in the startup world right now is especially rampant. Barely a day goes by when I don’t come across a company that is molding its marketing messages to the hype and pitching itself as an AI company, without a genuine AI story to back it up.

In a typical scenario, these artificial artificial intelligence companies are in actuality doing basic data analysis. Their technology sifts through data, and the results are used to drive certain outcomes—say, identifying the best time to send marketing emails based on pre-programmed rules.

Such companies may provide value by making data contextually relevant, but that’s not AI. Here’s the crucial difference: AI systems are iterative—they get smarter with the more data they analyze and become increasingly capable and autonomous as they go. Think of Tesla’s Autopilot improving with every mile that its fleet spends on the road. Authentic AI capability is what enables true market disruption.

A number of SaaS and automation companies out there are positioning themselves under the AI banner, even though all they really do is use data analytics to orchestrate applications and workflows. The technology doesn’t get more intelligent over time, and it never reaches the level of autonomy of bona fide AI.

For these companies, AI incorrectly has become a catchall phrase for anything that has to do with data or workflow. They also tend to liberally throw around “algorithm,” a word often associated with AI. But just because a system has algorithms that drive certain outcomes doesn’t necessarily mean it is AI.

Here’s what we look for before we invest in a company making an AI play: Are they doing more than basic data analysis? Are they creating their own data exhaust—a large trail of proprietary data that they collect from interesting sources? Do they use this data to create systems that constantly get smarter and in turn create their own data exhausts? Do they have iterative technology (machine learning or deep learning) that reduces the need for humans in the loop?

If those boxes can be checked off, we look for the following: founders with deep technical understanding of machine learning models, a unique approach for applying those models to a very large data set, and the strong possibility of a successful business model from all of it.

We should ask the following questions about the heads of companies that claim to use AI: Do the people claiming to be AI experts have experience taking on huge AI challenges, to the point that they have an extreme advantage over competitors? Do they understand the intricate technical details of what it takes to build an autonomous system? Are they attracting the talent to attack the market?

Authentic AI can provide groundbreaking solutions to real problems. The companies that can deliver in the field deserve all of the hype coming their way.

Arif Janmohamed is a partner at Lightspeed Venture Partners.