Successful AI implementation starts with a relevant problem and consistent data, not with choosing a tool.
What to expect
- 01
Start with the business problem
Choose use cases that are both feasible and relevant to procurement. AI should solve a concrete problem rather than become an end in itself.
- 02
Build a reliable data foundation
Digital infrastructure is only part of the challenge. Maintaining high-quality, consistently structured data is essential for useful analysis.
- 03
Identify practical applications
Supplier and risk management, procure-to-pay and spend management offer starting points for AI-supported analysis and automation.
- 04
Standardise before benchmarking
The APALYTICS example harmonises historical proposals and seniority levels before comparing prices with ecosystem data and exploring predictions.
About the author
As Product Manager at apadua, I work on turning data and technology into practical support for procurement teams. In this article, I explore how teams choose relevant AI use cases and build the data foundation they need.
Topics
- AI in Procurement
- Procurement Automation
- Spend Analysis
- Data Quality
- Use-Case Prioritisation