Struggling to wring value from AI? You need DI

Welcome to the Intelligence Era.

 

It’s a new Industrial Revolution: an age where 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 service 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 they’ll no longer be competitive.

 

The hurdles facing commercial AI

 

But for the moment, AI remains almost exclusively the domain of today’s largest businesses. The majority have yet to tap into the potential of AI. Most agree it is the future, but very few AI models are actually being productionised and most businesses struggle to quantify a return on that investment. This is down to a number of 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 – it’s surprising how frequently one function’s data fails to connect with that relied on by another. There is significant resource 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. Yet it is a crucial first step in the journey to AI-readiness.

 

There are further challenges too. Data science teams are often slowed by the complex technical requirements of combining data, building and deploying AI-powered solutions. Turning predictive models into something actionable by commercial teams can also be challenging – this is one of the primary reasons 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 so 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 productionise AI or quantify a return on their data strategy. By working backwards from an objective, businesses can build needed solutions and unlock value from AI consistently.

 

The biggest tech movement for a generation

 

We’re 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 optimise 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 organisation. For that, every business will need its own unique intelligence, capable of driving tangible results and delivering on objectives at an organisational level, not simply within one or two departments.

 

A company is the sum total of every decision it has made. The 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. 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 for a generation.

 

Producing AI with DI

 

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

1. All of your data made ready for AI.
2. An intelligence customised to the business built using their 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.

 

This is why Peak’s DI platform is designed as one centralised platform with a suite of features that enable customers to 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 the product strategy of a company. Decisions are always interconnected, and therefore a single centralised intelligence ensures that no one function is optimised 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 operates under significant uncertainty and complexity but still needs to make consistent and fast data-driven decisions.

 

DI can also impact one of the biggest issues of our time – sustainability. It’s a vast, complex and interconnected challenge for all businesses but one that’s more important than ever to tackle. And it’s exactly the kind of challenge that DI is uniquely suited to address; difficult decisions need to be made across vast quantities of organisation-wide data. As sustainability reporting frameworks come into play, a centralised 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.

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Find out how Peak can change how your business works by booking a demo.

 

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by Tom New, Head of Category at Peak