Examples:
Dynamic Pricing in Retail
+600% revenue uplift through AI pricing of unprioritized products.
For a comparable application, the following points need to be clarified:
- Problem: In large assortments, prices are not reviewed regularly against the relevant business objectives.
- Approach: Pricing rules and test groups are bounded before recommendations or automated adjustments are introduced in stages.
- Required data: Sales history, prices, margin, inventory, and defined pricing and assortment constraints.
- Measurable outcome type: Revenue, margin, unit sales, and price stability compared with a defined baseline.
- Human control: Accountable teams set rules, exceptions, and permitted ranges, and monitor deviations.
Customer Service Automation
40% workload reduction by AI-driven second-level support.
For a comparable application, the following points need to be clarified:
- Problem: Repetitive requests occupy specialists even though resolution knowledge already exists in documentation or previous cases.
- Approach: Suitable request types are defined, and responses are first introduced as suggestions within the existing support process.
- Required data: Categorized requests, approved knowledge sources, handling history, and escalation rules.
- Measurable outcome type: Handling time, resolution rate, escalation rate, response quality, and team workload.
- Human control: Specialists review sensitive or uncertain cases, and their feedback updates the knowledge base and rules.
Quality Management
Automated analysis of thousands of product test reports for REWE.
For a comparable application, the following points need to be clarified:
- Problem: Large volumes of unstructured reports are slow and inconsistent to evaluate manually.
- Approach: Reports are processed against a subject-matter schema so that anomalies and recurring patterns can be grouped and reviewed.
- Required data: Accessible reports, consistent quality categories, relevant metadata, and expert-reviewed examples.
- Measurable outcome type: Coverage, processing time, and agreement with expert assessments.
- Human control: Quality owners confirm critical findings and adjust categories when necessary.
Logistics Optimization
Predictive stocking and service planning for technicians.
For a comparable application, the following points need to be clarified:
- Problem: Demand, inventory, and field service are planned separately, which can lead to shortages or unnecessary stock.
- Approach: Consumption and service patterns are combined to derive demand estimates and planning suggestions for defined periods.
- Required data: Historical service jobs, parts consumption, inventory, lead times, and regional assignments.
- Measurable outcome type: Availability, stockouts, inventory coverage, journeys, or time to successful service delivery.
- Human control: Dispatch and service leads review exceptions, capacity, and safety-critical minimum stock.
Scientific Collaboration
AI-supported research document analysis with DOI integration.
For a comparable application, the following points need to be clarified:
- Problem: Relevant publications and their relationships are difficult to capture consistently across large document collections.
- Approach: Documents and DOI metadata are structured, while search, matching, or summarization functions are tested against known examples.
- Required data: Lawfully usable full text or metadata, DOI records, search criteria, and expert-curated examples.
- Measurable outcome type: Retrieval quality, coverage, source traceability, and research time.
- Human control: Researchers verify sources, conclusions, and relevance; the system does not make independent scientific judgments.
See our services for the capabilities behind these initiatives. The Business AI Framework explains the path from selection to controlled implementation.