The future of service procurement: using AI for greater efficiency and innovation

Classification and special features

• Differences from direct material purchasing.
• Changing specifications, changing contact persons, complex service packages.
• Typical pain points: Maverick buying, decentralized procurement, lack of transparency.

Collaboration with specialist departments

• Roles, responsibilities, cross-functional teams instead of silo thinking.
• Stakeholder alignment and internal governance.

Systematization and control

• Product group strategy, lead buyer concepts.
• E-procurement for spend management, measures against maverick buying.
• Supplier management: segmentation, qualification, performance management.

Tendering, awarding contracts, drafting contracts

• Specifications, performance description, delimitations.
• SLAs and KPIs: definition, measurement, bonus/penalty.
• Offer analysis, price comparison, evaluation matrix, award documentation.
• Types of contracts, Terms and conditions, property rights, delivery/installation/acceptance.
• Payments, advance payments, guarantees, default and warranty provisions.

Controlling and risk management

• Key figures, reporting processes, risk analyses.
• Verification and communication of value contributions.

Fundamentals and classification

• What modern AI can do: machine learning, assistance, predictive analytics.
• Benefits vs. buzzwords: real added value and limitations, typical pitfalls.

Guided buying for services

• Requirements specification, supplier search, automated RFx creation.
• Qualitative and quantitative bid evaluation, decision support.
• Differences from traditional material groups and dealing with dynamic requirements.

Processes, organization, and maturity level

• Integration of AI into existing purchasing processes and systems.
• Interfaces to specialist departments and suppliers.
• Maturity model: from manual to AI-supported and self-learning.
• Reporting and controlling setups for measuring success.

Tool and system evaluation

• Selection criteria: maturity, integration, governance, security, measurable benefits.
• Checklist: "Does the tool deliver what it promises?"
• Success factors from implementations (lessons learned).

Interactive practical exercises / case studies

• Analysis of typical service processes and identification of AI potential.
• Simulation of guided buying processes.
• Evaluation of AI tools based on specific tender and bid examples.

Strategy, roadmap, and outlook

• Efficiency, quality, and agility levers; risk reduction, cost transparency.
• Implementation steps: Data base, process design, change & enablement.
• Trends: self-learning systems, predictive sourcing, automation of complex contract processes.