In many companies, IT procurement is overwhelmed by day-to-day operational tasks. As a result, it often fails to carve out enough space for large, business-critical procurement projects. This article explores how digitalization can reduce the workload of operational procurement and how artificial intelligence (AI) can further contribute. We’ll present practical use cases where AI significantly reduces the effort required from procurement staff. Finally, we’ll take a brief look at the skills that need to be developed to support this transformation.
Typical Starting Point
Even today, IT procurement in many organizations remains highly reactive. A significant portion of procurement requests from business units arrive unpredictably and must be processed quickly. There’s often little time for market research, competitive offers, or negotiations. The latter is especially difficult when business stakeholders have already spoken with their preferred vendor and intend to proceed with the purchase.
In this situation, IT procurement should aim to drastically reduce and better plan the effort required for frequently occurring purchases of low-cost IT products and services. We present two improvement approaches that can be effectively combined:
- Digitalization
- Use of AI
Improvement Through Digitalization
For frequently requested internal IT products, standardized product baskets can be defined to cover most business unit needs. This typically requires collaboration with the IT department and management. IT knows its internal customers and can define standards for such products. Special requests should only be approved through a separate authorization process. Management must actively support this standardization and ensure that exceptions remain rare.
For products in these standardized baskets, IT procurement can issue long-term framework agreements. The resulting higher purchase volumes form the basis for more attractive pricing.
To truly relieve procurement after the framework agreement is in place, the process for individual orders from the basket must be digitalized. This involves setting up a catalog-based ordering system and automating the ordering process. Ideally, IT users can request catalog items via a service portal, triggering the order directly in the ERP system. Upon delivery, the invoice is automatically verified and paid.
AI in IT Procurement
If digitalization is implemented thoroughly, the effort required for procuring standard basket items becomes minimal. Procurement ensures smooth ordering, maintains the basket, and handles the rare special requests.
AI can support IT procurement across various tasks and reduce staffing needs. The following table outlines key application areas:
| Application Area | AI Capabilities |
|---|---|
| Data Analysis | Analyzing large procurement datasets – detecting patterns, trends, anomalies |
| Specification Development | Describing needs, IT products, and services – input for RFQs and tenders |
| Supplier Management | Analyzing and categorizing supplier data – selecting suitable vendors |
| Contract Management | Automated contract analysis – identifying risks and providing risk alerts |
| Price Optimization | Monitoring price trends – recommending optimal pricing strategies |
| Communication | Automatically drafting emails, inquiries, and orders |
| Negotiation Support | Analyzing past negotiation data and market prices – suggesting negotiation tactics |
| Spend Analysis | Classifying payment data – making spending structures transparent |
Three Practical Use Cases for AI in IT Procurement
1. Master Data Management
AI systems can review large datasets to identify and correct inconsistencies and errors in master data—such as duplicate products or suppliers, or inconsistent payment terms. Improved data quality simplifies analysis and reporting.
2. Request for Quotation and Ordering
This refers to a typical request from a business unit for a low-cost IT service, usually requiring three vendor quotes. AI can assist in every step:
- Translating business requirements into technical specifications
- Identifying similar past purchases to refine specs
- Pre-selecting vendors based on performance and reliability
- Drafting inquiries and emails
- Supporting negotiations with rule-based suggestions
- Comparing offers based on price, quality, delivery time, and service
- Reviewing terms and conditions for risks and proposing contract changes
3. Spend Analysis for Cost Reduction
AI can analyze and categorize payment data to make spending more transparent. For example, it may reveal multiple orders with one supplier under different contracts—highlighting bundling opportunities for cost savings.
Conclusion: Digitalization and AI in IT Procurement
Both digitalization—especially in operational areas like automated catalog ordering—and the use of AI can significantly relieve IT procurement staff. This creates space for careful planning, execution, and negotiation of major procurement initiatives, such as implementing a new CRM system or outsourcing IT services. Digitalization and AI can also support these larger projects.
When introducing AI into IT procurement, it’s essential to consider employee readiness. If staff lack AI experience, initial training is strongly recommended. Employees should develop a basic understanding of AI, including data analysis, formulating queries for generative AI, and interpreting AI-generated results.
It’s crucial to critically assess AI outputs and not rely on them blindly. Human review and interpretation are always necessary to ensure results align with business goals. In this context, procurement expertise and knowledge of relevant IT markets become even more important.
