Skip to main content

Breaking down decision intelligence - and why it's not the same as artificial intelligence

artificial intelligence
(Image credit: Image source: Shutterstock/PHOTOCREO Michal Bednarek)

Despite Artificial Intelligence (AI) being long-heralded as the next evolution in dealing with business data, it is used as a catch-phrase or buzzword so often that the underlying meaning and value that it can bring has been lost. Yet the reality is, it’s a technology with the possibility of going beyond human capability, able to read and analyze vast quantities of data, even creating forecasting models for future predictions. The key element that has been lacking in recent years is developing the understanding of how AI technology feeds into commercial decision-making and business growth. This is where the term decision intelligence comes into play.

Decision intelligence is the commercial application of AI to drive profit and growth for businesses. It allows enterprises to make faster, more accurate, more consistent decisions, all the time. It does this by leveraging a business’ number one asset – its data – and unlocking the value within it, using AI technology.

For many executives not close to the technology, the real-world application of AI is increasingly difficult to understand as the term becomes broadened and over-saturated. However, the idea of making quick and effective data-driven decisions is something all business leaders can understand the value of.

To get started, here are the key best practices and steps to success that any business should pay attention to if it is considering decision intelligence as the next game-changing adoption.

Understanding the end goal 

Decision intelligence combines complex and often disparate data and makes predictions from it, on a scale and at a pace that’s beyond human capability. As the name suggests, it is used to make better decisions and solve complex business problems. However, as with any type of business transformation project, a clear understanding of what is attempting to be achieved with decision intelligence is vital before it is implemented.

Key areas to consider will be on whether the goal is to; improve marketing campaign effectiveness, deliver the right stock at a store or branch level, or improving efficiency of logistics operations reducing emissions and carbon footprint. These are all tangible options with decision intelligence, but setting measurable business outcomes should remain at the top of the agenda to really feel the benefit of the technology.

Data is the north star 

Businesses know they need to be doing more with their data, are interested in the concept of using AI, and understand the benefits that it can bring. However, years of working in siloed systems means that the data tends to be messy and scattered around; leading to the belief that a large amount of time is needed to be spent in getting it up to scratch before it is used. This often isn’t the case. It is important to not let fear over the state of the data be a barrier to getting started – it is good enough, AI has done this before, and decision intelligence is able to drive value from it.

Becoming decision intelligence-driven enables businesses to break down organizational and data silos – leveraging data from anywhere, regardless of the state it’s in, and eliminating all those troublesome silos between systems and functions within your business at the same time. From here, applying AI to new and improved data sources can create a holistic, predictive view of products, customers and the supply chain. This in itself is a big task and one where a combination of data science skills and the right platforms to help you build and test AI models is vital. However, when done right, it goes much further than simply exploring past data and instead provides a window into the future.

In using this intelligence, enterprises can make faster, more consistent decisions over complex data, all the time – transforming decision-making across the whole organization.

A decision intelligence system should also have the capability of powering the technology across the entire value chain, without replacing elements such as existing marketing automation tools, ERP, CRM and logistics systems. The AI should form a centralized system that connects with other business systems, leverages standard data models and solutions that can be tailored to business needs, powering outcomes across the whole business.

How is decision intelligence different to AI? 

Sometimes businesses will be a bit hesitant to introduce AI into their decision-making because they don’t fully understand how the technology works and what it can do. Or in recent transformation discussions they may start to believe it is perhaps too much of a black hole to begin delving into. AI in this instance serves a greater purpose, to produce outcomes. Many AI projects never get put to use in day-to-day operations - the explicit end goal is to help businesses win by driving growth, profit and efficiency, not just to be a bit smarter.

The purpose of decision intelligence is also to keep ‘explainability’ at its core, with a clear focus on straightforward understanding and accessible meaning to every business. As its name suggests, decision intelligence is heavily action-orientated, going beyond data insights and making suggestions and recommendations on what to do next. It's the decisions that therefore become paramount, not just the surfacing of insight, going that one step further. This is the crux of the long dreamed of ability for technology to finally be able to ‘make decisions with us'.

Businesses no longer need to be a tech giant to leverage the benefits of data-driven decision-making. Accurate, successful and most importantly data-driven commercial decisions are now being made accessible to every enterprise.

Empowering teams across the business 

The more data points taken into account in decision-making, the better the decision. Time is a precious commodity and the length spent analyzing decision-making processes is a certified time-sucker. AI alleviates teams from time-intensive data tasks and empowers them to focus on strategy or to explore creative avenues.

The core of decision intelligence is that it gives teams the ability to work on the outcomes of a decision, rather than trawling through spreadsheets in a bid to make the best decision themselves in the first place. It’s a real game-changer in empowering teams to do more.

With all this said and done, the true business case for implementing decision Intelligence is that everybody benefits from it. The commercial application of technology is often hindered by an organization, with financial stakeholders worried about costs, business leaders worried about return on investment and team members confused at how it works. By putting commercial success at the center of what AI technology does, the business will begin to feel a positive impact across all of its departments, teams and decision-makers.

Tom New, Head of Product Marketing, Peak

Tom New, Head of Product Marketing, Peak.