What is Decision Intelligence?
With the rapid advancements in AI technologies, we see that every application, business process, and workflow will become more intelligent and automated in the coming years. Decision Intelligence (DI) will play a critical role in applying AI and infusing intelligence at critical decision points across all industries, and domains to unleash the business value, improve productivity, and make the businesses more agile.
Gartner included Decision Intelligence as one of the Top Strategic Technology Trends for 2022 and defines it as "Decision intelligence is a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor and tune decision models and processes.
Those disciplines include decision management (including advanced nondeterministic techniques such as agent-based systems) and decision support and techniques like descriptive, diagnostics and predictive analytics.
Decision Intelligence encompasses:
- A well-defined decision-making process includes decision models documenting decision requirements, dependencies, sub-decisions, and knowledge or information needs.
- A technology platform to support, augment, and automate decisions. Decision Intelligence applies different AI techniques, optimizations, and advanced analytics, such as predictive and prescriptive analytics.
- Various tools, methods, and practices that enable the decision-makers to design, orchestrate, execute, and monitor the decision flows efficiently and consistently.
- An Orchestration system to track, measure, and evaluate the decision outcomes and improve the decision-making process and algorithms through feedback.
Dr. Lorien Pratt, who coined the term Decision Intelligence and the co-author of the book "Decision Intelligence Handbook: Practical Steps for Evidence-Based Decisions in a Complex World", outlines five key processes that include Decision Requirements, Decision Modeling, Decision Reasoning, Decision Action, and Decision Review.
How is DI different from areas of BI, BRMS, and RPA?
Over the last two decades, many companies have implemented various decision support and automation technologies, primarily Business Intelligence (BI), Business Rules Management (BRMS), and Robotic Process Automation (RPA). While each has tangible benefits, they fail to drive quality decisions and executable actions. Decision Intelligence offers a more dynamic, data-driven approach that adapts to complexity and change, providing enhanced decision-making capabilities in today's data-rich business environments. We will share high-level differences to contrast Decision Intelligence from the current approaches.
Difference Between Business Intelligence vs. Decision Intelligence
Business Intelligence (BI) primarily focuses on descriptive analytics, which involves summarizing historical data to provide insights into what happened in the past. Decision AInalyst, using AI-powered analytics, encompasses a broader range of analytics, including descriptive, predictive, and prescriptive analytics, enabling organizations to forecast future outcomes and recommend actions to optimize results.
BI is commonly used for generating regular operational reports, tracking key performance indicators (KPIs), and producing standard management dashboards. Decision AInalyst is suitable for cases requiring predictive capabilities, such as demand forecasting, fraud detection, recommendation systems, and natural language processing. Decision AInalyst is also used for prescriptive analytics, recommending specific actions to optimize outcomes.
BRMS vs. Decision Intelligence
Business Rules are specific, actionable directives used to guide business operations. They are explicit statements that define business behavior and decisions, often in an "if-then" format. Decision Intelligence is a broader concept encompassing data, analytics, machine learning, and decision theory to enhance decision-making processes. It's not just about setting rules but understanding and analyzing complex decision environments to make informed decisions.
Generally, business rules are simpler and more static. They are suitable for straightforward, repeatable decisions but might struggle with complex scenarios involving multiple variables and changing conditions. DI handles complexity better, integrating data from various sources and applying advanced analytics and machine learning to adapt to changing circumstances and complex decision scenarios.
Changes to rules require manual intervention, while DI is more dynamic and can learn and adapt over time using AI and machine learning techniques.
RPA vs. Decision Intelligence
Decision Automation and Robotic Process Automation (RPA) are related concepts in the field of automation, but they serve different purposes and operate at different levels of an organization's processes. Decision automation focuses on automating complex decision-making processes, while RPA specializes in automating repetitive, rule-based tasks.
Decision Automation using Decision Intelligence can handle complex decision-making scenarios by analyzing data, considering multiple factors, and applying sophisticated AI and machine learning algorithms to enhance decision quality. While RPA can handle data, its primary focus is on data entry, retrieval, and transfer between systems.
Decision Automation and RPA can complement each other within an organization's automation strategy, with Decision Automation guiding processes as part of Intelligent Automation and RPA executing tasks efficiently.
Why Decision Intelligence?
Decision Intelligence delivers significant benefits to companies by improving the quality of decisions, increasing the efficiency and consistency of decision-making, and optimizing decision outcomes through continuous learning. While every aspect of business can be improved, here a few of the areas where DI have been proven to deliver an immediate and tangible ROI:
- Increase sales: Decision Intelligence helps companies increase sales by improving product selection, pricing, cross-selling, upselling, and customer retention.
- Reduce costs: Decision Intelligence helps to automate processes, streamline operations, improve productivity, and allocate resources more efficiently.
- Enhance Customer Experience: Decision intelligence can enhance many recurring customer support and service decisions to improve customer satisfaction and retention.
- Improve quality: Advanced analytics and Decision Intelligence drive product quality improvements by detecting emerging issues early, decreasing the detection to correction cycle time, and mitigating the risk of costly recalls or retrofits.
- Streamline Supply Chains: Decision Intelligence can help optimize sourcing, pricing, logistics, distribution, and risk mitigation.
Compared to the existing decision support approaches, Decision Intelligence offers many advantages:
- Enhanced Flexibility and Adaptability: DI systems can adapt to changing business environments and learn from new data, providing more flexibility.
- Improved Decision Quality: By leveraging data and advanced analytics, DI can lead to more informed and accurate decisions, particularly in complex scenarios where multiple factors must be considered.
- Greater Scalability: DI can handle a larger variety and volume of decisions, scaling as the organization grows and the decision-making environment becomes more complex.
- Predictive and Prescriptive Capabilities: Unlike BI, which is typically descriptive and prescriptive, DI can predict future outcomes and prescribe actions to achieve desired results.
- Integration of Various Data Sources: DI can integrate and analyze data from a wide range of sources, including unstructured data, large volumes of knowledge, videos, audio, and images, providing a more holistic view for decision-making.
- Real-time Decision Making: DI can facilitate real-time decision-making by analyzing live data, which is particularly important in fast-paced business environments.
- Better Risk Management: By understanding the potential outcomes and their probabilities, DI can help businesses better manage risks.
How to get started on Decision Intelligence?
We want to share a few recommendations for getting started with DI to realize the benefits immediately:
- Focus on Impactful Decisions: Identify recurring operational decisions that can significantly impact on business outcomes, productivity improvements, and customer experience.
- Vertical Use Cases: Focus on industry and domain-specific use cases where the quality of decisions will help achieve the desired outcomes rather than a generic technology and platform approach.
- Decision-First Approach: Take a Decision-first approach to let the decisions drive the knowledge and data required rather than taking broad data-centric approaches such as data lakes that do not yield immediate benefits.
- Natural and Conversational UX: Leverage Natural Language Processing (NLP) capabilities are possible now with the power of LLMs and Generative AI to make decision intelligence more accessible and explainable to knowledge workers at all levels in the organization.
- Scaling to Autonomous Business: Approach decision intelligence at three levels of Support/Analysis, Augmentation, and Automation to realize incremental value over time.
- Choose the Right DI Partner: Select a partner who can help deliver on your Decision Intelligence use cases by leveraging the different AI techniques, Domain expertise, and packaged solutions.
Circuitry.ai - Your Partner in Decision Intelligence
Circuitry.ai stands at the forefront of this revolution, offering Enterprise AI as a Service applications tailored to specific industries and domains. By leveraging DI, Circuitry.ai helps businesses optimize the outcomes effectively and efficiently.
Embark on Your DI Journey
To harness the full potential of Decision Intelligence and transform your business decision-making processes, contact us today. Discover how our unique approach and tailored solutions can catapult your business into a new era of intelligent decision-making.
