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 execution orchestrates your decision flows end-to-end, covering development, testing, and production environments. Whether operating in real time or batch mode, it ensures your decision processes perform consistently and scale to meet operational demands. For enterprises managing high volumes of complex decisions, this means fewer bottlenecks, greater reliability and the confidence to deploy decision intelligence platforms.

Decision modeling provides the "blueprint" design for decision intelligence. Before a platform can provide insights, a human with subject matter expertise must define the logic. The decision logic includes what inputs are important, how they relate, what patterns indicate, and the outputs that trigger an action.

Decision modeling helps set-up decision intelligence processes by:

• Structuring expert knowledge. To apply expert knowledge, the subject matter experts encode what they know about a domain — patterns, scores into models for predicting risk or opportunities.
• Enabling Explainability. The reason the decision logic is trusted and explainable is due to it being modeled rather than inferred by a black-box algorithm. This helps analysts and decision makers trace event findings back to the source. This functionality is critical for high-consequence decisions.
• Supporting continuous intelligence. Once a decision model is deployed, it can run continuously against live data streams, enabling the proactive, real-time risk and opportunity assessment that separates decision intelligence from traditional reporting or batch analytics.
• Bridges human expertise and artificial intelligence. Decision modeling is where the human expert judgment gets operationalized — the model reflects how a skilled analyst thinks, but it scales far beyond what any individual could provide coverage for. Thereby, risks are lowered, and opportunities are maximized by enabling expert models within decision intelligence platforms.

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 struggle with complex, high-volume data environments and decision-making challenges. Siloed systems and manual analysis lead to delays, information gaps, and biased insights. Decision intelligence platforms address these issues by leveraging Expert AI and machine learning to analyze data, uncover hidden patterns, and provide actionable insights. By automating tasks, reducing bias, and enabling collaboration, decision intelligence helps organizations make faster, informed decisions and shift from reactive to proactive operations.

While some analytics solutions can be helpful, an industry-specific approach to decision intelligence is more effective. By tailoring Expert AI-powered solutions to specific needs, organizations maximize benefits and achieve better results. Here are industry-specific examples of how decision intelligence optimizes key business operations:

• Healthcare - Continuous risk assessment identifies patient safety concerns and operational inefficiencies before they escalate, enabling proactive intervention and improved care outcomes.
• Finance - Advanced behavioral analytics detect fraud patterns and compliance risks in real-time, reducing losses while maintaining audit trails for regulatory requirements.
• Defense - Integrated intelligence platforms provide situational awareness across dynamic threat environments, shrinking the haystack to surface critical risks that demand immediate attention.
• Logistics - Predictive analytics optimize supply chain operations by identifying disruptions early, reducing waste, and improving asset utilization across complex networks.

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 — where the stakes are meaningful but the decision window is tight. In these scenarios, decision augmentation surfaces the most relevant options, ranks them by risk and impact, and equips analysts and business leaders with the context needed to act with confidence. Rather than replacing human judgment, decision augmentation strengthens it — ensuring that the right people have the right information at the right moment to choose the most effective course of action.

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 (DIP) serve diverse organizations facing complex, high-consequence decisions. Enterprise-scale companies use these platforms to manage risk and optimize operations, while government agencies deploy them for intelligence analysis and threat assessment. Expert AI-powered decision intelligence helps teams across different roles analyze data, assess risk, and make informed decisions in dynamic environments.

DIP helps multiple user types within organizations:

• Executive Leadership - CIOs, CTOs, and CISOs monitor key performance indicators and organizational risk through integrated dashboards, enabling strategic decisions based on real-time situational awareness rather than delayed reports.
• Analysts and Subject Matter Experts - Threat analysts, business analysts, and domain experts use no-code behavioral analytics to build models that detect patterns, investigate events, and provide actionable intelligence.
• Data Scientists and Engineers - Technical teams leverage the platform's open architecture and Expert AI capabilities to develop custom machine learning models, integrate data sources, and configure analytics that address specific requirements.
• Decision Support Teams - Case managers, investigators, and collaborative teams use integrated workflows to evaluate high-consequence risks, coordinate investigations, and provide auditable recommendations to decision-makers.
• Operations and Security Personnel - Fusion centers, security operations teams, and field units deploy the platform for continuous monitoring, real-time threat detection, and coordinated response across environments.

Insider threat investigations demand speed, precision, and defensible findings — yet most security teams are working against a tide of fragmented data, manual processes, and alert overload. Decision Intelligence platforms give insider threat analysts the tools to cut through the noise, connect the dots across data sources, and drive investigations to resolution faster than legacy approaches allow.

• Analyze large volumes of structured and unstructured data in near real-time to surface behavioral indicators that warrant investigation
• Identify anomalous user activity using Expert AI behavioral analytics that model complex threat patterns hierarchically — reducing false positives and sharpening investigative focus
• Automatically prioritize cases by risk score so analysts concentrate effort where consequences are highest
• Streamline investigative workflows through a single pane of glass — reducing context switching, supporting team collaboration, and accelerating time-to-finding
• Automate routine analytical tasks, freeing investigators to focus on higher-order judgment and decision support
• Produce auditable, explainable findings that answer questions essential for high-consequence insider threat cases requiring executive or legal review

Fraud, waste, and abuse (FWA) represent some of the most costly and persistent challenges facing enterprise organizations today. Detecting and preventing FWA requires more than reactive audits — it demands continuous visibility across complex, high-volume operational environments. A Decision Intelligence Platform (DIP) provides exactly that: a real-time, comprehensive view of organizational operations that enables proactive detection before losses compound.

By applying AI and advanced analytics to financial transactions, employee behavior, vendor relationships, and operational metrics, a DIP identifies anomalies and suspicious patterns that human analysts would otherwise miss. Connecting disparate data sources exposes hidden relationships between individuals, organizations, and transactions — surfacing sophisticated fraud schemes and wasteful spending patterns that siloed systems cannot detect. For enterprise leaders, this translates into measurable cost savings, improved operational efficiency, and stronger regulatory compliance.

A Decision Intelligence Platform can strengthen FWA prevention across several critical operational processes:

• Claims Processing and Adjudication
• Provider Network Monitoring
• Beneficiary Identity and Eligibility Verification
• Investigative Workflow and Case Prioritization

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.

Insider risk management programs face a relentless challenge: too much data, too little time, and consequences that are too high to get wrong. Decision Intelligence platforms give security and risk teams the analytical power to keep pace with dynamic threat environments — shifting organizations from reactive response to proactive risk management.

• Continuously analyze large volumes of structured and unstructured data to surface anomalous user behavior before it escalates
• Detect insider threats with greater precision using Expert AI behavioral analytics that model risk hierarchically — the way analysts actually think
• Prioritize investigations automatically so case managers focus on the highest-consequence risks first
• Automate routine tasks and streamline workflows to reduce analyst burden and accelerate time-to-decision
• Enable cross-team collaboration with a single pane of glass that supports auditable, explainable findings

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.

Machine learning is a fundamental component of decision intelligence, enabling it to extract valuable insights from large datasets. By deploying machine learning algorithms, decision intelligence systems can identify complex patterns, trends, and relationships, empowering organizations to make more accurate predictions, optimize decision-making processes, and achieve better outcomes. These systems can predict future trends, recommend optimal courses of action, identify anomalies, automate insight generation, and provide personalized recommendations. By leveraging machine learning, decision intelligence helps organizations make data-driven decisions that drive growth, efficiency, and innovation.

Expert Systems contribute to decision intelligence by providing a structured and automated way to incorporate domain-specific knowledge and rules into the analysis and decision-making process. They can be used to standardize best practices, identify potential risk indicators or opportunities based on predefined guidelines, and offer recommendations based on expert-level reasoning. When businesses integrate these systems, they can enhance the consistency and quality of their decisions and augment human analysts' capabilities by automating the application of complex rules set-up to emulate expertise. The systems can free up analysts' time allowing them to focus on more strategic and critical aspects of decision making.

Forrester Research: Generative AI Investment:

A May 2024 Forrester survey revealed that "67% of AI decision-makers plan to increase investment in generative AI within the next year." Given Forrester's view that "it's decision intelligence that gives AI its direction," this increased investment in AI directly translates to a greater need for and adoption of Decision Intelligence.

Hierarchical Complex Event Processing (HCEP) plays a crucial role within decision intelligence by enabling real-time analysis of intricate patterns and relationships hidden within vast streams of enterprise data. HCEP acts as a powerful engine for identifying significant events and trends as they unfold, going beyond simple data aggregation to detect complex sequences and correlations that signify emerging opportunities or potential risks. HCEP provides real-time contextualized insights from data streaming that allows businesses to make faster, more informed decisions and optimize operational responses. Organizations can proactively adapt strategies within their overarching decision intelligence framework to gain improved agility and competitive advantage.

Business Intelligence (BI) primarily focuses on understanding past and present performance through data analysis and reporting. Decision Intelligence (DI) is a newer approach to improve future decision-making. BI provides insights into what happened and why and often through static dashboards and historical data. DI leverages AI, machine learning, and a broader range of data to not only understand the past but to predict potential outcomes and recommend or automate the best course of action. DI goes beyond providing information, it actively advises and optimizes organizational decisions for a more agile and impactful future.