What is a decision support system in artificial intelligence (AI)?

By Rachel Lee - September 17, 2021

Most companies today have no trouble gathering data, but knowing what to do with that data is the tricky—and time-consuming—part. Even brilliant organizations are challenged with making smart business decisions after thorough data analysis. Bridging the gap between data analytics and decisions is where a decision support system (DSS) is incredibly useful.

When decision-making processes aren’t working, a DSS can help companies navigate their data landscape and feel confident about their decisions. A DSS is used to gather and analyze data and then incorporate it into comprehensive reports. The decision support system methodology can be completely managed by artificial intelligence (AI), humans decision makers, or a blend of both.

Artificial intelligence techniques for decision-making, or augmented analytics, are a powerful way for businesses to use data to make smart business decisions confidently.

Examples of AI-powered decision support systems

Decision support systems can take many forms, whether it’s a robo-advisor helping young people invest in the stock market or an online streaming service making TV show recommendations.

Here are a few real-world applications for intelligent decision support systems (IDSS), which are DSS powered by AI.


Robo-advisors are a great example of decision support systems in financial technology. Financial robo-advisors run economic models to provide online investment portfolio management with little to no human involvement, aside from an initial self-assessment process to choose between investment options.

This form of DSS uses AI to give advice based on prior outcomes, outsourcing investment recommendations to appeal to the consumer.


Radiologists use clinical decision support systems in the form of AI-powered image processing software to help in cancer detection. Likewise, DSS can be used to manage health informatics such as maintaining research information about specific protocols, preventative care, and diagnosing illnesses.

More broadly speaking, DSS can help healthcare companies analyze patient data to improve overall patient outcomes and reduce healthcare costs.


Marketers use AI-powered decision support systems to model buyer personas by analyzing how users interact with different elements of a brand or across multiple brands.


For e-commerce sites, DSS is used to make recommendations for users based on metrics such as previous purchases or browsing data. A decision support system can help companies give tailored suggestions based on consumer insights and consumption patterns. For example, many e-commerce sites will use your browsing history to recommend similar products or suggest items that other users ultimately bought.


GPS is one common example of a decision support system that’s used today. A GPS will help plan the fastest route between two points by analyzing all possible options. Most GPS systems can also monitor traffic in real-time, which helps drivers navigate around traffic jams.


Another great use for DSS is in real-estate: Companies use decision support tools to gather data including neighborhood comparison prices, future planning, and acreage.


Even farmers use DSS tools to determine when to plant their crops, when to fertilize, and when to harvest.

Decision support systems & artificial intelligence

Artificial intelligence is the backbone behind effective decision support systems. A decision support system helps facilitate decision-making for a team or business based on data. AI takes that one step further and automates decision-making for companies, also known as an expert system.

A DSS will take companies’ data sets and streamline the decision-making process by forecasting outcomes based on large amounts of data. Several types of decision support systems help companies based on their source of information, as compiled by University of Northern Iowa professor Daniel Power.

    1. Data-driven: This is the most common form of DSS, and includes all management reporting systems. Data-driven DSS will make recommendations based on large datasets of both internal and external data.
    2. Model-driven: This less data-intensive approach can include accounting/financial models, optimization models, or representational models.
    3. Document-driven: These decision support systems help retrieve and analyze documents. Search engines are an example.
    4. Knowledge-driven: These DSS specifically recommend actions to managers and provide problem-solving based on a specific issue or topic. For example, a knowledge-driven DSS tool could help a manager better plan or predict intended outcomes.
    5. Communication-driven: This type of DSS is about streamlining communication and collaboration to help people who are working together

AI works by creating artificial neural networks—a series of algorithms that identify relationships and patterns in data—that can mimic the way the human brain operates. AI systems can then create methods of optimization to help businesses make informed decisions.

AI’s predictive functionality is what makes it a great decision support tool, taking raw data and turning it into actionable advice.

Find your DSS with Sisu

Sisu makes it far easier to bridge the gap between your data and decision-making. We help businesses make better decisions with expert systems and machine learning, leaning on AI to take big data and transform it into clear insights. Schedule a demo with Sisu to streamline your analytics and find facts that might be buried in your data.


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