How AI extends the application of business intelligence – and why controlling and management benefit from it.
After the initial wave of attention surrounding generative AI, more concrete questions are arising in many companies today: Which expectations are realistic? Where does actual added value emerge? And how can AI be meaningfully and reliably integrated into existing processes and systems—particularly in business intelligence and controlling?
Business intelligence has long been established in many companies. Metrics are evaluated, deviations analyzed, reports automatically generated. For controlling and management, BI is a central instrument of corporate management. At the same time, demand is growing: decisions should be made faster, be better informed, and address causes rather than symptoms.
This is where artificial intelligence comes into focus. However, the sensible use of AI in controlling is less a question of additional technology than a question of consistent further development of business intelligence.
Proven controlling methods and their analytical limits
Controlling today has a broad toolkit of proven analytical methods. Contribution margin flow calculations, driver analyses, variance analyses, as well as structural and time series analyses provide well-founded insights into financial developments and economic relationships. These methods are powerful and form the backbone of modern corporate management.
At the same time, they are limited in their analytical scope. They presuppose structured data, explicit models, and known causal relationships. However, many questions that controlling and management face today arise outside this framework: in customer interactions, service processes, sales activities, employee conversations, or market reactions.
Such information exists in the company but often in unstructured form—as texts, comments, conversation notes, or reviews. With number-centric BI, these sources can only be systematically evaluated and linked with metrics to a limited extent. Underlying causes, patterns, and early indicators thus remain analytically invisible—not because they’re unknown, but because they lie methodologically outside the framework of BI.
AI extends the analytical scope of business intelligence
This is where AI unfolds its added value. It extends proven controlling methods with additional analytical perspectives. AI makes it possible to systematically link quantitative metrics with qualitative knowledge, thereby making connections visible that were previously inaccessible to BI.
Large Language Models (LLM) in the BI context bridge the gap between metrics and meaning. They help interpret causes, present relationships comprehensibly, and put analytical results into a decision-relevant context. Business intelligence thus evolves from a pure information system toward active decision support.
Explainable BI: explainability as a prerequisite for trust
Particularly in controlling and management, trust in analytical results is crucial. AI-supported analyses must be traceable, verifiable, and reproducible. What matters is not only the explainability of individual AI models, but the explainability of BI results overall—from the data foundation through calculation logic to action recommendations.
Explainable AI thus becomes Explainable BI. AI supports analysis; responsibility and decision authority remain with humans. Human-in-the-Loop is not a technical safety net, but a conscious leadership principle.
Agentic BI: from analysis to continuous decision support
A further development step lies in agent-based BI workflows. BI systems no longer just respond to specific queries but work proactively. They regularly analyze developments, detect anomalies, explain causes, and provide recommendations for action.
The decisive difference lies in the shift from reactive to proactive operation, rather than mere automation of individual evaluations. Analysis is evolving from a one-time event to an ongoing process that supports corporate management. Controlling and management are alerted to relevant developments at an early stage, instead of having to react to deviations retrospectively.
Particularly in complex organizations, from medium-sized businesses to international corporations, tangible benefits emerge: more orientation, more consistent decisions, and significant relief in daily operations, without giving up control or responsibility.
Conclusion: greater impact from data
Controlling 2026 means achieving greater impact from existing data. Artificial intelligence helps advance business intelligence and embed analytical insights more deeply into decision-making processes. This gives companies of all sizes a stronger foundation for corporate management and enables closer, more effective collaboration between controlling and management.
