New AI built to make better decisions

From content recommendations on your Netflix dashboard to interactions with Amazon voice assistants to AirBnB, Uber, and Google—all couldn’t do what they are doing without AI.

But these are some of the world’s most successful companies. What about the rest?

This might be the intelligence era, but the vast majority of companies have yet to tap into its potential. And it’s not that they’re doing anything wrong. Big tech companies were data-first from the start. Smaller-scale companies with more traditional roots just aren’t built with the capability to harness AI in their day-to-day operations. And until very recently, such a capability remained far out of reach.

What’s changing the intelligence game for businesses is a new AI category that’s built for commercial settings: Decision Intelligence (DI).

This exciting technology is helping companies in sectors outside of tech to layer in AI-informed decision-making through every vertical of the business—from supply chain to marketing. DI is set to help a much broader spectrum of businesses harness data to make better decisions. Gartner predicts that over a third of large organizations will be using it within the next two years.

It makes sense that the commercial application of AI should be focused on decision-making. The value of a business is the sum of its decisions: A product positioning or logistics approach that cuts ahead of the competition, grows revenue, and funnels back into the value chain.

We can look at DI as the leap from hoping we’re making a decision that will create value for a business—to knowing we are. In the computing age, we’d use historical data to make a guess at good forecasting, pricing, or marketing decisions. In the age of DI, real-time data becomes endemic to the decision-making process, so we can be confident in the outcome every time.

In this new business reality, data teams are no longer hidden away in a back office, building models that never see the light of day. They’re in constant communication with the commercial side of the business, absorbing data from every department, and translating it into immediately actionable recommendations.

Suddenly, we’re seeing workforces where every employee—from the process level to the C-suite—is empowered to use AI in their everyday decision-making.

This is what the very near future could look like. But what’s the path to adoption for companies who want to start embedding DI? I typically break this down into three key requirements:

For the majority of companies, though, building all of that is a tall order. That’s why I think we can expect a growing demand for off-the-shelf DI platforms in the next couple of years—a trajectory similar to what we’ve seen with CRMs. In the early 2000s, 80% of companies were building CRMs in-house. Today, we’d never dream of it. Companies are accelerating time to value by investing in ready-made solutions—and DI is ripe for the same kind of innovation.

Out in the wild, only 10% of all machine learning models are actually being put into production with an organization. As companies begin to adopt DI, particularly through a ready-to-use platform model, we’ll see that number increase exponentially.

It’s interesting to think about the impact of this broader-scale adoption on macro issues like sustainability. For many businesses, reducing supply chain emissions is the next frontier for corporate climate action. We can start to picture how DI could help companies assess the environmental impact of a decision across production, distributions, and consumption—and choose the best outcome for their business and the planet. In fact, I’ve already seen a major CPG company use DI to reduce haulage emissions by an impressive 147 tons of CO2.

Most exciting though is the fact that much of what DI is capable of will be discovered in practice. There may be breakthrough applications across healthcare, accessibility, DEI, and more that we can’t yet conceive of. And shifts in how we as individuals approach our daily work that we’d never have imagined.

Richard Potter is the CEO of Peak.