Introducing Decision Intelligence (DI)

What Is Decision Intelligence?

Let’s start by defining decisions carefully. According to the Cambridge Dictionary, “Decisions are choices that are made after considering several options”, such as previous experience, prejudice, feelings, desires, and intuition. Our decisions can also be influenced by stereotypes, misunderstandings, and ultimately subjective perceptions of reality. This is how the human brain processes a combination of external and internal factors to make choices. It, therefore, does not consider all influential factors and the whole picture cannot be imagined.

But when it comes to AI-based decision-making machines, it becomes a game-changer. Artificial intelligence systems process and analyze large amounts of data in real-time, make smart predictions based on historical data, and suggest the best possible decisions based on datasets and initially specified parameters. Therefore, there are two main differences between human and AI decision-making. The AI considers all available information while humans consider only limited data. Artificial intelligence is ultimately objective and ignores emotional factors.

How Does an Intelligent Decision Model Work?

There are many technologies and algorithms that enhance the decision-making machine.

  • Machine learning (ML). Machine learning algorithms process a certain amount of structured data and make suggestions or decisions according to specified parameters. The fraud prevention system used by banks is the simplest example. For example, if a user accesses a banking app from a suspicious IP, the system will determine if additional user authentication is required.
  • Deep learning. Deep learning is the next step in the evolution of machine learning. In this case, the decision machine considers decisions made and the results of each new proposal.
  • Visual decision modeling. AI decisions serve as a reliable starting point, but decisions are still made by entrepreneurs and their employees. Visual decision modeling is a feature of decision intelligence software that presents the options and results available to human decision-makers.
  • Complex system modeling. One of the benefits of decision intelligence is the ability to quickly build complex business logic based on available data and ultimate goals.
  • Predictive analytics. The decisions made by the AI ​​system are based on fairly accurate predictions. The simplest example is price forecasting and automatic optimization in the retail industry. In this case, Decision Intelligence’s proposals are based on myriad insights into current and past price fluctuations, forecast demand, future trends, and customer behavior.

Decision Intelligence Benefits for Enterprises

Below are the four key benefits of decision intelligence solutions that enterprises can expect.

Data-driven decision-making. 91% of companies believe that data-driven decision-making can drive business growth, but only 57% rely on data. To gain a competitive advantage, you need to properly analyze the available data, make some predictions, and select the best option. AI can better examine the data array to find invisible patterns and possible anomalies that can have a significant impact on the results.

Make decisions quickly. According to a McKinsey survey, only 20% are happy with the speed of decision-making. Others admit that it takes too long to make the right choice, but that is not always the case. AI decision-making systems can process large amounts of data almost instantly, making the process as fast as possible.

Multiple problem-solving options. AI-based decision algorithms are also very flexible, allowing you to emphasize multiple outcomes of a particular decision when one of the parameters changes. This feature helps companies make the best choice from a variety of options, given their current goals and growth strategies.

Elimination of errors and distortions. There are at least five types of bias that can directly affect the outcome of business decisions. Well-programmed algorithms objectively examine the available data, allowing decision intelligence to avoid all of them.

But do intelligent systems always make better decisions than humans? Guided by large amounts of input data, there is no tendency for cognitive bias, but a human review is still needed, especially if decision-making can lead to conflicts of interest and values.

Decision Intelligence Use Cases Across All Industries

Industry-wide organizations use decision intelligence solutions to increase data-driven sustainability, resilience, rationalization, and cost-effectiveness.

Banking and Finance

Morgan Stanley is a financial advisory company that helps clients invest smarter, backed by in-house financial advisors and intelligent decision-making models. The wealth management platform is based on decision intelligence. Based on the client’s goals (such as investing in real estate or starting to save children’s tuition), the AI ​​system proposes a winning strategy. This is also verified by a human advisor before being offered to the client.

Lloyds Banking Group also uses AI decision-making solutions in most business processes. With that help, they analyze customer behavior, anticipate their needs and weaknesses, adapt their products and services, and make better decisions about when to start deep, personalized communication.


Being able to predict better prices for a particular category of goods based on external factors, customer demand, trends, and the sentiment is the simplest yet most effective decision-making information for retailers and distributors. This is one of the usage examples. For example, Remi AI helps retailers make better pricing, adapt pricing policies to their customers’ solvency and expectations, thereby optimizing their supply chains and making sales volumes more predictable.


Enlitic Cure is a data analysis and decision-making platform designed to combine artificial intelligence with the skills of human physicians. Decision intelligence solutions enable clinicians to analyze medical imaging reports more quickly, suggest diagnoses, and enable clinicians to prioritize cases and improve medical outcomes.


As far as the use cases of decision intelligence in the energy sector are concerned, AthenaAI software is worth mentioning. This system helps users manage their energy resources better and make automatic decisions about energy and cost savings. It also predicts solar power and optimizes battery capacity accordingly.

Another case study worth mentioning is Infopulse, which has developed artificial intelligence decision-making software for Ellevio, one of Sweden’s largest distribution operators. With the help of a decision engine, Ellevio can collect, structure, and analyze business data from seven different sources, generate insightful reports, and make optimized decisions.


Environmental problems, climate change, and the natural disasters that result from them are global problems, but at the micro-level, they represent companies with significant risks. One of the benefits of decision intelligence is the ability to anticipate and leverage potential risks based on the past. One concern is the AI ​​decision platform that enables businesses to analyze and keep clear about the potential risks of environmental disasters. Ultimate climate data analysis also allows them to make better business strategy decisions. For example, hospitality companies can consider not only weather conditions, but also market conditions, COVID-19 conditions, and customer demand to determine a safer place to build a new hotel.


AI-supported business intelligence solutions enable enterprises to make better and faster decisions in critical business processes. In this way, enterprises not only unlock the ultimate benefits of data-driven but also consider as much relevant information as possible when deciding on the next step.

Contact us to access our in-depth expertise so that you can fine tune your decision-making platform with long-term forecasts to perfectly match your current business needs.