An economist’s take on the power of predictive AI, and how it’ll transform entire industries

Our lives are all about making decisions. Will you take an umbrella on your walk or not? How about if there’s a 25% chance of rain? How about if you’re wearing something you really don’t want to get wet? 

According to Ajay Agrawal, economist, professor, and co-author of Prediction Machines: The Simple Economics of Artificial Intelligence, we use a combination of prediction and judgment to make every decision, big or small. The power of AI comes from its ability to take care of the prediction part of that equation — to tell you the chance of rain. Ultimately, you’ll use human judgment to decide whether you should bring an umbrella based on how much you dislike getting wet versus how much you dislike carrying an umbrella when it doesn’t rain.

Artificial intelligence models can process massive amounts of data to identify patterns and generate accurate predictions. There’s no doubt AI is enhancing how individuals work, helping them be more productive, improving how they collaborate, and upleveling their skills. And when companies use AI for predictive analytics, it can transform how leaders make decisions, helping them better serve customers, allocate resources, and create new and improved processes.

Ajay recently joined our Work Evolved webinar series to talk about the power of AI and predictive analytics. We sat down with Ajay to continue the conversation on predictive AI — here’s what he had to say about its potential for changing entire industries and which employees have the most to gain from using predictive AI tools.

Some people view AI as smart machines, robots capable of talking or thinking like humans. As an economist, how do you think about AI?

Artificial intelligence helps us with prediction. Prediction is using information you do have to generate information you don’t have. 

That’s what generative AI models like ChatGPT are doing — using prediction to generate human-sounding language. Generative AI models predict the next token, or word, in a sequence to create a human-sounding message. Another example would be a bank using AI for fraud detection, processing data from past transactions and user habits to accurately predict whether a purchase is fraudulent.

A basic principle of economics is that when something becomes cheaper, we use more of that thing. The rise of AI represents a drop in the cost of prediction. And as prediction gets cheaper, we’ll use more of it. 

Let’s talk more about prediction. What is predictive AI and what are some real-world examples?

Predictive AI uses input data to generate output. As a traditional example, we can use 20 years of historical sales data to predict third-quarter sales for next year. The historical data is the input, and the sales prediction is the output. 

Less traditionally, we can use the pixels in a medical image to predict the label on a tumor as malignant or benign. The pixel data is the input, and the label on the tumor is the output. That’s also a prediction. 

One interesting feature of AI is that, unlike prior statistical techniques, it can utilize multimodal data (e.g., pictures, video, language), not just numbers, as input data, and it can produce predictions in the form of pictures, video, and language.

Predictive analytics is core to many types of innovation. We can see the real-world applications of using AI for prediction in things like driving, translation, fraud detection, particle size distribution, email replies, and inspection.

In industries where predictive AI is becoming a dominant force, what effect will AI have on jobs? People are worried that this AI revolution could eliminate their positions. Is that a valid fear?

Before we had washing machines, it took two people all day to do the laundry. Now, one person can spend a fraction of their day washing clothes. But nobody’s complaining that those machines took their jobs. 

Similarly, we’ll be able to offload some aspects of our work to machines. When we think about our jobs, a lot of it is based on making decisions. And every decision has two elements: prediction and judgment. If we can offload predictive analytics to the machines, we’re left with human judgment, and that skill is what people need to focus on growing. 

AI can use prediction to draft an email or map a route from point A to point B — but it takes a human to decide whether that email message achieves the goal, or whether the route makes sense to take. AI is the copilot, the assistant, but it can’t make the decision.

What effect does AI have on employees’ productivity and the workforce in general? 

When computers were introduced, it made highly skilled people disproportionately more productive, and that led to a lot of income inequality. AI seems to be having the opposite effect. Lower-skilled workers have the most to gain from using AI assistants or copilots.

Take, for example, call centers. They began introducing AI tools that would give employees recommendations to help the customers they were speaking with. Researchers at MIT and Stanford discovered that AI had a limited impact on the high performers because they already knew what to say. But employees who previously hadn’t performed as well were now brought up almost to the level of highly skilled workers with the help of AI tools. 

Another example we’re all familiar with is how navigational AI has made it so that anyone can drive in any city as well as a pro. That enabled innovative companies like Uber. Before Uber, there were 200,000 people who were professional drivers. Now, there are 3-4 million people who drive for Uber. We’re sitting at the precipice of a moment in history where this sort of system-level shift is starting to happen across different industries. 

What advancements are you most excited about with AI? If you could take out your crystal ball, what do you see being the next big innovation and how will it affect our world?

I predict that in two to three years, we’ll see AI coming off the screen and into the physical world. Right now, we have models to predict a series of words on a screen, and the next stage is to have robots predict a series of actions to accomplish a task like making a coffee or parking your car.

But the real profound shift will be in terms of scientific discovery. We’ll have AI models that can generate a hypothesis and test it, trigger robots to run the experiment, and feed the results of that experiment back to the AI, which updates the hypothesis and runs another experiment. We’re already seeing labs doing a version of this.

Continue the conversation on predictive AI

For even more of Ajay’s insights into where the next wave of AI innovation will take us, check out his on-demand Work Evolved webinar. You’ll get a deep dive into predictive AI and how it’s helping companies save time and create new and more effective ways of working.

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