What is Decision Intelligence?

What is Decision Intelligence?

Decision intelligence describes the systematic and data-based optimization of decisions using AI, analytical modeling, and operations research. According to Gartner, DI is a practical discipline that explicitly designs decisions and continuously improves their results through feedback. It replaces pure gut feeling in operational and tactical decisions with traceable, well-founded, and reproducible decision-making processes.

 DI differs significantly from related concepts such as business intelligence (BI) and process mining. While BI mainly visualizes data and analyzes historical patterns, DI also provides concrete recommendations for action and integrates optimization and AI directly into decision-making processes. Process mining , on the other hand, focuses on process analysis, whereas DI actively shapes decisions based on these analyses.

Background and Context

Decision Intelligence combines several sciences: data science, operations research (OR), cognitive psychology, and AI research. Gartner cites DI as one of the strategic technology trends because it takes data-driven approaches to a new level. Originally used in the context of BI and analytics, DI is increasingly evolving into a holistic method that automates or supports complex operational decisions.

 The increasing complexity of global markets, the rapid growth in data volumes, and growing compliance and transparency requirements have increased the demand for efficient decision-making methods. Companies are faced with the challenge of having to make strategic decisions in ever shorter time frames in order to remain competitive. By using OR methods such as network analysis and linear programming, as well as AI technologies such as machine learning, DI is able to quickly and systematically analyze even the most extensive decision spaces and find optimal solutions.

 Historically, decision intelligence is a natural evolution of methods such as operations research and traditional data analytics. OR was already being used over 50 years ago to solve logistical challenges. Today, DI expands on this approach by using modern AI, which evaluates large amounts of data in real time and significantly increases the quality of decisions.

How does decision intelligence work?

Decision Intelligence follows a clearly structured process:

• Decision modeling: Definition of goals, KPIs, conditions, and options for action in a clear model, often supported by visual decision tables and flowcharts.
• Data integration: Aggregation and analysis of data from ERP, CRM, SCM systems, and external sources, including the application of advanced data management techniques.
• AI-supported analysis: Application of machine learning, optimization algorithms (e.g., genetic algorithms, heuristic searches), and simulations to solve complex decision-making problems. Various scenarios are simulated to assess risks and identify opportunities.
• Human-machine interaction: People review, validate, and supplement automated decisions using interactive dashboards and reporting tools.
• Monitoring & feedback: Results are monitored and fed back into the system for continuous improvement, ensuring a permanent learning and optimization process.

Application examples at INFORM

• ADD*ONE – Inventory optimization: Optimizes inventory levels through predictive sales planning and automated scheduling, reduces storage costs, and improves delivery capability.
• Syncrotess – Transport logistics: Optimizes transport and handling processes in real time, minimizes waiting times, and maximizes resource efficiency in industry and trade.
• Terminal Logistics – Port logistics: Automated planning of container handling, optimizes storage space and crane movements, increases terminal throughput and transparency.
• Vehicle Logistics – Vehicle distribution: Plans optimized transport routes and resource allocation in the automotive industry, reduces downtime, and improves flexibility.
• RiskShield – Risk & Fraud Prevention: Identifies and minimizes risks in financial transactions through real-time analysis and AI-powered fraud detection.
• GroundStar – Airport Operations: Optimizes airport ground handling processes such as personnel and resource planning, improves punctuality, and reduces operating costs.
• WorkforcePlus – Workforce Planning: AI-based shift planning that takes individual preferences and operational requirements into account, increasing productivity and employee satisfaction.

FAQ About Decision Intelligence

What distinguishes Decision Intelligence from traditional BI solutions?

Decision Intelligence goes beyond pure data analysis and integrates AI and operations research to actively shape concrete decision options.

For which industries is Decision Intelligence particularly suitable?

It is particularly suitable for complex industries such as logistics, manufacturing, financial services, and aviation, which have to make many data-driven decisions.

To what extent does DI automate decision-making processes?

Automation varies depending on the use case; often, it involves a combination of automated suggestions and human validation.

What role do humans play in decision intelligence?

Humans validate and control decisions, define parameters, and intervene where human expertise and contextual knowledge are crucial.

What technologies are used in DI?

DI uses technologies such as machine learning, simulations, heuristic search methods, and optimization algorithms from the field of operations research.

Is decision intelligence only useful for large companies?

No, DI is also attractive for medium-sized companies, as it helps them make complex decisions in a data-driven and resource-efficient manner.

Conclusion

Decision intelligence revolutionizes decision-making by intelligently combining AI, data analysis, and human expertise. Companies that use DI benefit from more accurate decisions, increased efficiency, and enhanced competitiveness. Learn more about our solutions and contact us for a personalized consultation.