AI Positioning & Implementation Framework BST / FinOffice
1. Context and Objectives
The rapid advancement of artificial intelligence is transforming how software is developed, operated, and used—particularly in data-intensive domains such as market data management.
This document outlines BST’s positioning, guiding principles, and concrete measures for the use of AI.
2. BST’s Core Position on AI
2.1 AI as a Productivity Enabler
BST views artificial intelligence primarily as a tool to enhance productivity—not as a replacement for expertise.
- AI supports employees in analysis, processing, and decision-making
- Domain expertise and professional judgment remain essential
- Outputs generated by AI systems are critically reviewed
- The objective is an “augmented workforce,” not an automated black box
2.2 Protecting Expertise and Quality
Uncontrolled use of AI carries the risk of gradually eroding professional expertise.
BST addresses this risk proactively:
- AI is deployed in a controlled and purpose-driven manner
- Employees are trained to interpret and validate AI-generated results
- Critical processes remain subject to expert validation
This approach ensures both quality assurance and sustained customer trust.
3. Data Strategy as a Differentiator
A key differentiating factor of BST is its consistent approach to data storage and processing within Switzerland.
3.1 Core Principles
- No transfer of sensitive data outside Switzerland
- No use of customer data for external model training
- Processing within controlled Swiss infrastructure
3.2 Implications for AI
These principles directly shape BST’s AI strategy:
- Preference for locally deployable or controllable models
- Careful trade-off between innovation speed and data sovereignty
- Deliberate avoidance of certain hyperscaler-based approaches
Comment:
This may reduce short-term speed but strengthens long-term trust, compliance, and differentiation.
4. Internal AI Use Cases
BST clearly distinguishes between internal efficiency gains and customer-facing features.
4.1 Support and Knowledge Management
- AI-powered chatbot for first- and second-level support
- Context-aware assistance within FinOffice
- Automated structuring and analysis of support cases
Objective: Scale high-quality support from a high-cost location.
4.2 Training and Enablement
- Automated generation of training materials
- Creation of interactive training content (e.g., video formats)
- Personalized learning paths for users
4.3 Software Development
- Use of AI to accelerate development processes
- Support for testing, code generation, and documentation
- Economical delivery of customer-specific customizations
Key point:
Customizations become economically viable even at smaller volumes.
4.4 Marketing and Communication
- Efficient creation of expert content (whitepapers, posts, documentation)
- Target audience-specific content adaptation
- Support for translation and internationalization
5. AI-Based Product Features (FinOffice Roadmap)
5.1 AI-Supported Invoice Processing
- Automated extraction and classification of invoice data
- Assistance with account assignment and validation
- Reduction of manual effort while maintaining control
5.2 AI-Based Contract Data Extraction
- Extraction of structured information from contracts
- Support for contract analysis
Limitations:
- High validation requirements
- Limited efficiency gains in current scenarios
- Significant risk in case of misinterpretation
Interpretation:
This feature remains exploratory and will only be pursued under clearly defined conditions.
5.3 Additional Potential Features
- Anomaly detection in market data (quality assurance)
- Intelligent data classification and mapping
- Forecasting models for data volume and usage
Conclusion
BST follows a deliberately differentiated approach to AI:
- Focus on productivity rather than substitution
- Strong prioritization of data sovereignty and quality
- Selective, economically driven implementation
This positions BST not as a short-term technology frontrunner, but as a sustainable and trustworthy provider in the field of market data management.