Artificial intelligence is increasingly becoming a strategic issue for technical procurement. For CPOs, the question is no longer whether AI will become relevant, but rather where it can make a real contribution. With the white paper “AI in Technical Procurement: A Guide for CPOs,” PartSpace outlines the conditions companies must establish to use AI applications productively and cost-effectively.

In this white paper, PartSpace addresses a key challenge faced by many industrial companies. Although technical drawings, 3D models, ERP data, and historical procurement knowledge are available in large quantities, they often remain insufficiently linked within technical procurement and are therefore of limited use. As a result, decisions regarding costs, suppliers, and savings potential are frequently based on an incomplete picture. This is precisely where artificial intelligence comes into play - provided it is understood not as a generic promise of automation, but as a specialized approach to analyzing technical and commercial data.

Technical Procurement Requires More Than Generic AI

The white paper makes it clear that general-purpose AI applications are generally insufficient for these requirements. When dealing with drawing-based components, complex assemblies, and a high degree of variant diversity, decisions must take into account not only prices and delivery times, but also geometries, materials, tolerances, manufacturing processes, and manufacturability. Added value is only realized when design, manufacturing, and procurement data are systematically consolidated and interpreted within their context. PartSpace therefore describes AI in technical procurement not as a mere IT project, but as a strategic lever for greater transparency, more informed supplier decisions, and robust business cases.

The guide is primarily aimed at CPOs and procurement managers in industrial companies who want to systematically prepare for the use of AI in technical procurement. It provides guidance across key areas of action - from evaluating the existing data landscape and addressing security and compliance issues to identifying initial use cases with measurable economic potential. At the same time, it demonstrates how initial pilot projects can be developed into a scalable approach for productive use. “Many industrial companies already have relevant data for AI in technical procurement, but have not yet used it as a reliable basis for decision-making,” says Sebastian Freund, co-founder and COO of PartSpace. “This is exactly where our white paper comes in: It outlines a five-step process showing how companies can progress from a realistic assessment of their data infrastructure, through security considerations and initial quick wins, to scalable implementation.”

The white paper is available for download here: “AI in Technical Procurement: A Guide for CPOs”