Decision Intelligence (DI): A Smarter Decision-Making Training for Managers and Entrepreneurs

This year, company managers are training in a new discipline – decision intelligence, DI (engl. decision intelligence). It is a trend in management and a field that encompasses a range of methods for easier and better decision-making. Decision intelligence is based on machine learning algorithms and is tailored to organizational decision-making.

It combines various decision-making techniques that unite artificial intelligence (AI), automation, business intelligence, and creativity in decision-making (the latter refers to human potentials) to achieve the best business success through advanced decisions based on effective (selected) data.

Discovering the Perfect Profile

For companies that want to overcome outdated reports, decision intelligence enables the processing of large amounts of data through a sophisticated combination of tools such as artificial intelligence and machine learning to transform data dashboards and business analytics into more comprehensive decision-support platforms. Decision intelligence systems help companies look to the future with greater ease and confidence, and DI platforms can identify risks and provide concrete recommendations for action to avoid harmful consequences.

For example, if something disrupts the supply chain, a DI platform can be used to successfully resolve issues in real-time. By using decision intelligence, all stakeholders in the business process can be managed with more predictable success. DI technology excels at predicting, for example, when products will become more sought after, whether the margin has reached or not the point where changing inventory makes sense, and synchronizing supply with consumer demand in other ways.

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Decision intelligence eliminates the need for lengthy discussions, and with the help of a DI platform, the management team is connected and has access to data they can trust because it is unbiased, accurate, and reliable, enabling faster action and reducing disagreement within the team. Additionally, digital intelligence solutions such as machine learning can execute algorithms on client data collected in the form of datasets.

This way, the perfect user profile is revealed and compared with third-party data to create a target list of potential customers that will serve marketing for campaign implementation. The strategy helps identify specific customer preferences and complement their needs.

Deep Understanding

However, this is not as simple as it seems because successful decision-making strategies require an understanding of how organizational decisions are made, as well as a commitment to evaluating outcomes and managing and improving decision-making with feedback.

– It is not a technology, but a discipline that consists of many different technologies – says​ Gartner analyst Erick Brethenoux.

According to this consulting firm, decision intelligence is one of the main strategic technology trends this year, and it is expected that more than a third of large organizations will practice this discipline by 2023. The trend is developing at a time when organizations must decide faster than ever, and at scales that have not been seen before.

According to Brethenoux, decision intelligence helps ensure automated decision-making, which can help companies remain competitive and meet market demands. But for that, one needs to deeply understand decision-making processes, the risks and rewards that each decision brings, and acceptable error margins and be able to understand how confident one should be in any decision offered by automated decision-making processes.

Introducing decision intelligence into corporate practice means starting a process that is well-defined, low-risk, and has a large collection of examples. It is not a one-time process, which is why the approach must be continuously adjusted based on feedback.

More Accurate Risk Assessments

Many companies already have such processes, but not all are fully automated yet.

– Companies that are too busy with daily operations may not notice that they are missing these opportunities. Later, they will wonder why competitors are doing better, but it will be too late. Even when the process is already automated, adding more factors to the decision-making mechanism can improve accuracy – says Ray Wang, chief analyst and founder of Constellation Research.

For example, a risk assessment decision can be improved by considering the time of day or the user’s location. The more frequently the process is repeated and the clearer the results, the more opportunities the company will have to improve it.

For instance, LexisNexis uses its product ThreatMetrix to make 300 million decisions a day related to fraud, and although they are not entirely perfect, they offer enormous value to customers as they are correct in 99 percent of cases. LexisNexis applies machine learning algorithms to sort transactions into behavior profiles to predict whether a transaction is fraudulent or suspicious.

Automation and Performance Measurement

In traditional decision-making, risk scoring involves a series of decisions ‘if – then’, but machine learning systems, in addition to being able to quickly make decisions based on datasets, can mitigate or completely eliminate risk. To make the best possible decision in this complex process, special attention should be paid to the data collected.

If the stages in decision-making are poorly defined, the results of decisions are less clear, or there are greater risks of wrong decisions because intelligent systems cannot completely replace humans in decision-making, but they can significantly increase the quality of the decision.

– The automation of decision intelligence can occur during the data collection phase in decision-making, but it does not have to make final conclusions; it can be used to create reports or generate trends and correlations. The old way, manual data collection and report creation, is not a good idea today; it is more useful to have automatically collected and processed data in real-time to make the final decision – says Wang.

It is essential to use large datasets for decision intelligence to be truly effective because it can be very challenging to determine whether a decision was good based on smaller datasets.

– The quality of outcomes and the quality of decisions are not the same. Sometimes you have a great hand of cards and make the right decisions, but still lose. Unfortunately, when it comes to complicated and rare decisions, companies often do not have established mechanisms to measure their performance. But solving this problem is not technology – the first step is to formalize decision-making in the company and then think about adding software to support that process. However, it is challenging to collect the results of those decisions and link them to the decision-making process. Currently, marketers are the most skilled at this – says Amaresh Tripathy, global head of analytics at Genpact.

Business Secret

Companies are increasingly adopting artificial intelligence technologies to address existing and new problems in the business ecosystem, meet changing market demands, and achieve better business results, revenue growth, and market impact. However, one of the problems in decision intelligence can be data bias because decisions are only as good as the data they are based on.

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People produce data that is often based on bias, and those who decide will seek data that supports their bias. For example, if a manager thinks that the company needs more people in a sector, they can easily find data that supports that, which can lead to wrong conclusions. The solution is to manage bias by learning from past mistakes.

Innovations, including artificial intelligence, computer vision, decision intelligence, and machine learning, will have a significant transformative impact on the market in the coming years, which is why decision intelligence will become a necessary mechanism in the business process.

However, in Croatian companies, they are reluctant to talk about the application of decision intelligence in decision-making. Questions about what basis they make decisions and what processes they apply were directed to large companies: some responded that it is a business secret, others that they use artificial intelligence to a lesser extent, and others still do not think about decision-making based on the mechanisms and tools that DI offers.