By: Richard Potter – Peak’s Co-founder and CEO
For centuries, humans have been supported by technology that amplifies our potential. Steam power was the breakthrough that spurred the first industrial revolution in the 1700s. One century on, and the discovery of electricity changed the face of life — with computing quick to follow. Now, we’ve reached what many are calling a fourth industrial revolution: the intelligence era.
Change in this revolution is happening quicker than any that’s gone before it, but what really separates this era, is technology’s new capacity to think alongside us. Artificial intelligence has expanded our cognitive capabilities, allowing us to make decisions and predictions we could never make alone.
In the business world, it’s sparked huge shifts in how companies build products and interact with their customers. It’s already easy to think of popular AI applications within today’s top brands, from content recommendations on your Netflix dashboard, to interactions with Amazon voice assistants. AirBnB, Uber, Google — all the companies that we think of as dominators in their sector — couldn’t do what they are doing without AI.
But that’s the very top of the most successful companies we’re talking about. 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.
Decision Intelligence and the New Business Reality
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 layer in AI-informed decision making through every vertical of the business — from supply chain to marketing. With Gartner predicting that over a third of large organizations will be using it within the next two years, DI is set to help a much broader spectrum of businesses harness data to make better decisions.
The idea that the commercial application of AI should be focused on decision making makes a lot of sense. 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 a good forecasting, pricing or marketing decision. 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.
The Path to DI Adoption
So, 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: an AI-ready data set, an intelligence customized to your specific business, and an interface available to teams company-wide, so that non-technical teams can engage with a model and its outputs.
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 tonnes 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, D&I 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.