Over the past few years, AI has dominated news cycles and captured the imagination of entrepreneurs, investors, and consumers alike. We can see the potential: self-driving transportation on-demand, robotic assistants in the home, and Amazon Echo version 14.0 to do things the human mind could never even contemplate. That future isn’t far off — a decade or so, maybe.

But as much as we talk and read about AI, many of us still think about it in the wrong way. People compare artificial intelligence to human intelligence too much and often see human intellect as the end goal for AI. Human intelligence is familiar, and it is natural to want to use it as the bar. But here’s the thing: Human intelligence is closer to the bottom end of the scale.

Human intelligence is not the bar

To many, the goal of AI is to create technology that can think like humans. But it’s oversimplifying to suggest that any intelligence — human or artificial — can be rated on such a simple scale as “better or worse.” Some people excel at memorization, logic, or emotional IQ, while others excel at the visual or auditory. Similarly, an AI may have strengths and weaknesses. Furthermore, why have a goal of just matching human capability, when beating it is within reach?

Think about all the dimensions where AI seems to have already surpassed human intellect. Can a human translate a passage into any one of 300 languages in a fraction of a second? How about instantly determining the optimal driving route to avoid all traffic? Machines already outperform us in many tasks — specifically, those that involve the processing of big data.

What should we expect from AI?

Don’t get me wrong. I am excited about the deeper AI that begins to mimic a human’s ability to learn purely by observing and interacting with the world. This is known as artificial general intelligence, or AGI, and it requires no training data beyond direct experience.

Certainly, the movie industry is intrigued by AGI — machines taking human form, complete with five full senses and the capability to comprehend and communicate with the world. It is surreal and fun to imagine coexisting with machines that are indistinguishable from humans in form and in intellect, but it is not a useful benchmark for understanding the current environment and trajectory of AI, and how AI will actually affect most products and industries. AI should not necessarily be judged by how human it is.

On the contrary, AI’s largest effects over the next 10 years are likely to be in the realm of domain-specific use cases. To achieve this, AI needs data and lots of it. These new awe-inspiring forms of intelligence are born from speedy algorithms that can process increasingly massive amounts of data.

Domain-focused AI and data-driven software are on the brink of causing widespread industrial disruption. For example, at Applied Semantics and later Google, we built machine learning systems to choose the best ad out of a pool of millions, all in milliseconds. Each time we serve an ad that is not clicked, it is an extra data point to train the AI — a small opportunity for the system to learn and, more importantly, make new conclusions about the world. With trillions of data points, the systems become eerily effective, certainly far beyond anything within human capability.

The symbiosis of data and AI

We are witnessing an exponentially increasing demand for data. Nearly every industry and aspect of the business world has moved toward a digital reinvention: from brick-and-mortar shopping to ecommerce, from TV ads to mobile marketing, from cash to crypto, and so on. These new normals require software, AI, and data — massive amounts of data.

That’s exactly why I founded Factual: to provide the highest-quality location data to power digital innovation, including AI. Data companies have an incredible opportunity to help businesses develop new products, acquire customers, and understand patterns of use within a real-world context. In order to build an engine for producing such data, we’ve had to build our own AI, which is in turn fed by even more data from our partners — a terrific feedback loop.

The effectiveness of our proprietary AI can’t be easily compared to a human scale because the capabilities lie within a different dimension of intelligence, such as processing trillions of data points to derive meaning. Many of the most promising applications of AI aren’t the ones that seem most like us, but rather, the ones that can do things we never even contemplated.

This story originally appeared on Medium. Copyright 2017.

Gil Elbaz is the founder and chief executive office of Factual, a neutral data company making data accessible to everyone.