Struggling to Wring Value from AI? You Need DI.

Peak is a Business Reporter client.

We’re entering the Intelligence Era, yet most businesses are struggling to implement AI. Is Decision Intelligence the missing piece of the puzzle?

Welcome to the Intelligence Era. It’s a new Industrial Revolution—an age in which artificial intelligence (AI) will transform how we live, work and play.

Change is already happening, and how companies build products and interact with their customers is evolving. From streaming content recommendations to smart speakers in your kitchen keeping track of your shopping list, AI is helping many of the world’s biggest brands do what they do.

And where they lead, others will follow. AI has the ability to expand our cognitive abilities, improve customer experience and provide insight and predictions in real time. As the Intelligence Era progresses and AI becomes the norm, businesses that have failed to adopt it will fall so far behind the pack that they’ll no longer be competitive.

The hurdles facing commercial AI

For the moment, AI remains almost exclusively the domain of today’s largest businesses, although the majority have yet to tap into the potential of AI. Most agree it is the future, but very few AI models have been implemented, and most businesses struggle to quantify a return on that investment. This comes down to several factors.

A lack of AI-ready data is the first challenge. Data fuels AI, and data fragmentation within companies is common. Different teams often use different data sources and systems, and it’s surprising how frequently one function’s data fails to connect with data relied on by another. Significant resources are required to bring a business to a stage of data maturity in which one data source is widely supported as a single source of truth—a crucial first step in the journey to AI-readiness.

There are other challenges, too. Data science teams are often slowed by the complex technical requirements of combining data and building and deploying AI-powered solutions. Turning predictive models into something actionable by commercial teams can also be challenging; this is a primary reason why AI projects fail to provide tangible results.

Decision intelligence (DI) is the commercial application of AI to the decision-making process. It is outcome-focused and requires models to be built with a business objective in mind. As such, it addresses many of the pain points for businesses that are currently struggling to productionize AI or quantify a return on their data strategy. By working backwards from an objective, businesses can build needed solutions and consistently unlock value from AI.

The biggest tech movement in a generation

It’s still relatively early in the Intelligence Era, in a stage of “narrow AI” where machine-learning predictions are used for specific purposes. A business may use an AI solution to optimize advertising campaigns, for example, or to suggest products to online shoppers. But to have a meaningful, long-term impact on operations, AI will need to solve the biggest challenges facing that business in a way that takes a more holistic view of how decisions are made and how they impact the wider organization. Every business will need its own unique intelligence capable of driving tangible results and delivering on objectives at an organizational level, not simply within one or two departments.

A company is the sum total of every decision it has made. Companies that make consistently great decisions are the ones that come to dominate their sector. DI—outcome-focused AI grounded in commercial decision-making—is therefore how the majority of businesses will adopt AI.

Imagine running a business without the internet or a CRM process. In 10 years’ time, the thought of running a business without a DI platform will be just as unfathomable. DI is the biggest tech movement in a generation.

Producing AI with DI

What does the path to widespread commercial DI adoption look like? Each business will need to meet three requirements:

1. All data made ready for AI.
2. An intelligence customized to the business, built using its data.
3. An interface in which line-of-business users can engage with a model.

This third step is crucial, and one that existing AI and ML (machine learning) platforms fail to address. If commercial users have no way to engage with AI to make decisions, then even the most accurate and complex predictive models cannot drive value alone—a step most AI strategies are missing. Additionally, commercial teams need to understand and influence these solutions to buy in and be comfortable using them. Of the barriers businesses face in successfully implementing AI, culture is a significant hurdle that will only be overcome by uniting technical and line-of-business teams.

Peak’s DI platform is designed as a single centralized platform, with a suite of features that enable customers to easily build and integrate AI-powered solutions across multiple business functions, all in one place.

The importance of one intelligence that sits across the entire business and supports multiple departments cannot be overstated. An ML-driven product-recommendation engine on a website is not valuable if it keeps recommending products that are out of stock or do not fit within a company’s product strategy. Decisions are always interconnected and, therefore, a single, centralized intelligence ensures that no one function is optimized at the expense of another.

Businesses in any industry can make AI-powered decisions about marketing, sales, demand forecasting, diagnostics, supply chains and more. That’s because we now live in a world where every function of a business generates large amounts of data, and although businesses operate under significant uncertainty and complexity, they still need to make consistent and fast data-driven decisions.

DI can also impact one of the biggest issues of our time: sustainability—a vast, complex and interconnected challenge for all businesses that’s more important than ever to tackle. And it’s exactly the kind of challenge that DI is uniquely suited to address, as difficult decisions need to be made based on vast quantities of organization-wide data. As sustainability reporting frameworks come into play, a centralized intelligence can empower companies to make more informed decisions about their impact on the environment.

DI will change the way the world works, just as meaningfully as the industrial revolutions that preceded it.

— Tom New, Head of Category at Peak

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This article originally appeared on Business Reporter. Image credit: iStock id1159761614