Decision Intelligence Academy

Decision intelligence (DI) uses large language models (LLMs), along with data fusion, visualization and collaboration tools to augment and improve decision making. The objective of DI is to accelerate and improve the accuracy of decisions and not to replace humans in decision making. Decision intelligence platforms provide users a holistic view of their organization’s data to get actionable insights that manual analysis would struggle to obtain.

Decision intelligence (DI) has emerged as a critical capability for organizations navigating today's complex and rapidly evolving landscape. DI is the application of AI to enhance decision-making. Depending on the use case, the level at which the machine intervenes may vary depending on the complexity and risk of the situation. Below are the three different categories of decision assistance:

• Decision Automation - Low complexity and minimal consequences – Ideal for situations where the chance of error is slimmer, and the consequences from potential errors are not drastic.
• Decision Augmentation - Medium complexity and consequence. The aim is for machines to provide what you need for your own decisions to be swifter, more informed.
• Decision Support - Highest complexity and greatest consequence. Machine intervention should be at its least intrusive. Humans need to be making the decisions in such cases, both to ensure accountability and minimize the possibility of misjudgment.

Applying the correct level of machine intervention can help organizations face increasing volumes of data and sophisticated threats, and it enables them to maintain an agile approach while ensuring that critical decisions receive human oversight. 

Many analytical tools fall short by providing summaries and insights that may not be actionable. Decision intelligence addresses this challenge by democratizing data and insights, making them accessible to a wider range of users. By providing analysts better insights, investigators and decision-makers can identify emerging threats and respond effectively to challenges.

Market Research from Gartner shows:

According to the 2024 Gartner Chief Data and Analytics Officer (CDAO) Agenda Survey, one-third of organizations have implemented decision intelligence (DI).

By 2026, 75% of Global 500 companies will apply decision intelligence practices, including the logging of decisions for subsequent analysis.  

By 2028, 25% of CDAO vision statements will become “decision-centric,” surpassing “data-driven” slogans, with human decision-making behaviors explicitly addressed to improve D&A value.

Other Market Research has forecasted that growth and adoption is expected:

Grand View Research: "The global decision intelligence market size was estimated at USD 15.22 billion in 2024 and is projected to reach USD 36.34 billion by 2030, growing at a CAGR of 15.4% from 2025 to 2030." They attribute this growth to "the increased demand for data-driven decision-making solutions."

MarketsandMarkets: "The global decision intelligence market is projected to grow from USD 13.3 billion in 2024 to USD 50.1 billion in 2030, at a CAGR of 24.7% during the forecast period." This indicates a very aggressive growth trajectory.  

Fortune Business Insights: "The market is projected to grow from USD 19.38 billion in 2025 to USD 57.75 billion by 2032, exhibiting a CAGR of 16.9% during the forecast period."

Decision intelligence enables enterprises to make timely data-driven decisions that improve efficiency, mitigate risks, provide improved services, and governance. By utilizing advanced analytics, such as machine learning, and AI, organizations can gain valuable insights from large volumes of data, identify patterns, predict trends, and optimize the allocation of resources. This provides more informed decisions, streamlines operations, and delivers tailored services to customers. In addition, decision intelligence helps assess and mitigate risks and promotes transparency and accountability in enterprises. With this technology, enterprises can better serve customer’s, improve competitiveness and agiliy address the challenges of the future.

Many organizations today grapple with vast amounts of data and complex decision-making challenges. Siloed systems and manual analysis often lead to delays, information gaps, and biased insights. Decision intelligence offers a solution by leveraging AI and machine learning to analyze data, uncover hidden patterns, and provide actionable insights. By automating tasks, reducing bias, and enabling collaboration, decision intelligence empowers organizations to make faster, more informed decisions. While some analytics solutions can be helpful, an industry-specific approach to decision intelligence is often more effective. By tailoring solutions to specific needs, organizations can maximize the benefits of decision intelligence and achieve better results.

Decision Automation is the application of AI technology to execute routine or complex decisions without human intervention, ensuring speed, consistency, and efficiency. Within the Decision Intelligence process, it serves as the critical step where decision models can be configured into automatic actions. After Decision Intelligence has identified the best course of action and designed the decision logic, Decision Automation deploys the automations at scale and transforms insights directly into operational outcomes like automatically adjusting inventory, approving credit applications, or routing customer inquiries. Decision automation is suitable for use cases with low complexity and little consequences – in other words, situations where the chance of error is slimmer, and the consequences of potential errors are not drastic.

Decision Augmentation refers to the use of AI and advanced analytics to enhance human decision-making by providing timely insights, recommendations, and data relevant to human end users. It provides business leaders and employees with tools that clarify complex situations, highlight potential risks or opportunities, and suggest the best course of action. Decision augmentation allows humans to make more informed, efficient, and higher-quality judgments. It can be beneficial within the Decision Intelligence process when requiring decisions to be swifter, more informed, and more accurate. The concept of decision augmentation is suitable for use cases of medium complexity and consequence.

Decision support refers to technology systems and analytical tools that assist decision-makers by providing relevant data, insights, and modeling capabilities to enhance the speed and quality of business decisions. In the decision intelligence process, decision support becomes particularly beneficial when decision-makers need to navigate complex scenarios with multiple variables, high stakes, and time constraints—such as strategic planning, risk assessment, market expansion, or crisis response. For situations of the highest complexity and greatest consequence, machine intervention should be at its least intrusive. Humans need to make decisions in such cases, both to ensure accountability and minimize the possibility of misjudgment.

Decision intelligence platforms are valuable tools for many types of organizations. Large companies can leverage these platforms to optimize business processes, while government agencies can use them for investigations, intelligence analysis, and risk assessment. Senior decision-makers can benefit from decision intelligence to understand strategic trends and make informed decisions, while data scientists and engineers can use them to develop tailored machine learning models. The platforms can be deployed in dedicated fusion centers or for organization-wide use, empowering teams to make data-driven decisions and achieve better outcomes.

Decision intelligence accelerates insider threat investigations and intelligence analysis by providing effective tools to analyze large volumes of data to identify potential threats. By leveraging analytics and machine learning, decision intelligence platforms can identify anomalous user behavior, detect insider threats, prioritize investigations, automate routine tasks, and facilitate collaboration among teams. Organizations can benefit from proactively addressing insider threats, reducing risk, and improving security posture.

Decision intelligence platforms (DIP) are designed to combat fraud, waste, and abuse by applying a comprehensive, real-time view of organizational operations that enables proactive detection. DIP leverages AI and machine learning to analyze vast amounts of data from multiple sources—financial transactions, employee behavior, vendor relationships, and operational metrics—identifying anomalies and suspicious patterns that human analysts would miss. By connecting disparate data sources, the DIP reveals hidden relationships between individuals, organizations, and transactions, uncovering sophisticated fraud schemes and wasteful spending patterns. For enterprises, this translates into substantial cost savings through reduced losses, improved operational efficiency, and enhanced regulatory compliance.

Decision intelligence allows organizations to significantly enhance their risk management capabilities. By leveraging advanced analytics and machine learning, decision intelligence software can identify patterns, anomalies, and emerging risks, enabling organizations to proactively address potential threats and mitigate risks before they escalate. For example, in the financial services industry, decision intelligence can be used to detect fraud, assess credit risk, and manage operational risk. With decision intelligence, organizations can make more informed decisions, reduce risk exposure, and improve overall performance.

Decision intelligence uses predictive analytics to forecast future outcomes based on historical data. By employing statistical algorithms and machine learning techniques, decision intelligence empowers organizations to make more informed and proactive decisions. For example, in law enforcement, predictive analytics can help identify potential crime hotspots, predict the likelihood of recidivism, and anticipate emerging threats.

While decision intelligence is a practical application of AI, it differs in its focus on specific decision-making processes. AI, on the other hand, encompasses a broader range of technologies and techniques, including machine learning, natural language processing, and computer vision. Decision intelligence leverages these AI technologies to analyze data, identify patterns, and generate actionable insights.

By combining AI and data visualization, decision intelligence platforms provide a valuable tool for analysts. These platforms can extract intelligence from various data sources, including text, audio, images, and videos. AI-powered engines can perform tasks like face recognition, object tagging, and transcription, enabling deeper analysis and faster insights.

An industry approach to AI, tailored to specific industries and use cases, is crucial for achieving optimal results. By leveraging domain-specific knowledge and data, organizations can develop more effective and efficient decision intelligence solutions.

Decision intelligence accelerates insider risk management and case management by utilizing platforms to analyze large amounts of data and identify potential threats. By leveraging advanced analytics and machine learning, decision intelligence platforms can identify anomalous user behavior, detect insider threats, prioritize investigations, automate routine tasks, and facilitate collaboration. By streamlining investigations and providing actionable insights, organizations can utilize decision intelligence to proactively address insider threats, reduce risk, and improve security posture.

Decision Intelligence platforms provide data science teams with a unified pool of all relevant data sources to tailor and run machine learning models that are customized to the organization’s unique data sources, system requirements and use cases.

Decision Intelligence platforms incorporates the functionality of multi-persona Data Science and Machine Learning (DSML) platforms which provide the tools and data sandboxes needed by expert data science teams for their specialized work, while also providing a low-code/no-code user experience to non-technical users that have a limited background in data science but have significant subject expertise and require the ability to analyze data and finding insights.