AI for Corporate Support and Decision-Making
- Identify AI automation opportunities in finance, HR, procurement and administrative support processes.
- Design an AI-assisted workflow for a support service such as invoice processing or HR inquiries.
- Use AI-supported forecasting and scenario analysis to compare options for a management decision.
- Build a decision brief that combines AI analysis with risks, assumptions and human judgement.
- Define controls for accuracy, confidentiality and accountability in AI-supported corporate services.
- AI in Corporate Support Functions
- Typical support processes and pain points
- Automation versus augmentation options
- Shared services and AI
- Automating Support Services
- Intelligent document processing
- HR, procurement and IT service assistants
- Workflow automation and integration
- AI for Management Information
- Automated reporting and narrative summaries
- Decision dashboards and alerts
- Data quality and single source of truth
- AI-Supported Decision-Making
- Forecasting and scenario analysis
- Structured decision frameworks
- Combining AI insight with human judgement
- Decision Briefs and Communication
- Writing evidence-based decision briefs
- Presenting options, risks and assumptions
- Avoiding bias in AI-informed decisions
- Controls and Adoption
- Accuracy checks and approval steps
- Confidentiality and data protection
- Measuring service and decision quality gains
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Program Professional Framework
AI for Corporate Support and Decision-Making
Program Overview
Corporate support functions such as finance, human resources, procurement, legal and administration handle large volumes of routine work and supply information for leadership decisions. This course shows how AI can automate and improve these services and support better decisions: intelligent document processing, HR and procurement assistants, forecasting and scenario analysis, automated management reporting and decision dashboards. Participants also learn structured decision-making methods that combine AI insight with human judgement, and the controls needed for accuracy, confidentiality and accountability in corporate services.
Learning Outcomes
- ✓Identify AI automation opportunities in finance, HR, procurement and administrative support processes.
- ✓Design an AI-assisted workflow for a support service such as invoice processing or HR inquiries.
- ✓Use AI-supported forecasting and scenario analysis to compare options for a management decision.
- ✓Build a decision brief that combines AI analysis with risks, assumptions and human judgement.
- ✓Define controls for accuracy, confidentiality and accountability in AI-supported corporate services.
Target Audience
- Managers of finance, HR, procurement and shared services
- Business partners and analysts supporting leadership
- Strategy and performance management officers
- Executive office and decision support staff
Detailed Program Modules
01AI in Corporate Support Functions
- Typical support processes and pain points
- Automation versus augmentation options
- Shared services and AI
02Automating Support Services
- Intelligent document processing
- HR, procurement and IT service assistants
- Workflow automation and integration
03AI for Management Information
- Automated reporting and narrative summaries
- Decision dashboards and alerts
- Data quality and single source of truth
04AI-Supported Decision-Making
- Forecasting and scenario analysis
- Structured decision frameworks
- Combining AI insight with human judgement
05Decision Briefs and Communication
- Writing evidence-based decision briefs
- Presenting options, risks and assumptions
- Avoiding bias in AI-informed decisions
06Controls and Adoption
- Accuracy checks and approval steps
- Confidentiality and data protection
- Measuring service and decision quality gains
Training Methodology
Applied Project & Assessment
Applied Project
Participants select one support process and one recurring management decision in their organization, design an AI-assisted solution for each, and present a decision brief demonstrating the combined use of AI analysis and human judgement.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.


