Artificial intelligence (AI) is reshaping procurement, particularly in spend classification and spend analysis tools. The recent study AI Meets Spend Classification: A New Frontier in Information Processing by Guida, Caniato, and Moretto (2025) explores the impact of AI on spend classification, using the Organizational Information Processing Theory (OIPT) to analyze how AI meets the increasing complexity of spend data in modern organizations.
This article summarizes the key takeaways from the report, with a particular focus on the summary of approaches by defined spend analysis tool providers. We’ll also provide reflections on these findings to help buyers-in-training better understand the evolving landscape of spend analytics.
Table of Contents
Key Insights from the Report
AI’s Role in Spend Classification
Spend classification is a foundational activity in procurement, enabling organizations to analyze spending patterns, optimize supplier relationships, and improve cost control. The report highlights how AI enhances this process by:
✅ Automating data cleaning and classification
✅ Identifying spending trends and patterns
✅ Detecting anomalies such as maverick spending
✅ Enhancing category tree design through Natural Language Processing (NLP)
✅ Providing predictive insights for future procurement needs
Despite its potential, AI adoption faces several challenges:
⚠️ Data quality issues – Many firms struggle with fragmented, inaccurate, or incomplete spend data.
⚠️ Lack of internal analytical skills – Procurement teams often lack AI expertise, leading to mistrust of AI-generated recommendations.
⚠️ Regulatory and compliance hurdles – Changing legislation adds complexity to AI implementation.
The study emphasizes a hybrid intelligence approach, combining AI’s analytical power with human expertise for validation and strategic decision-making.
Deep Dive into the Findings on Annex B: Cross Case analyses
Overview of Spend Analysis Providers’ Approaches
The report analyzes nine AI-based spend analysis tool providers, examining their capabilities and challenges in AI deployment. Here are the key dimensions evaluated:
| Dimension | Findings from the Study |
|---|---|
| Data Integration | Most providers integrate with ERP systems, external data sources, and supplier databases. Some struggle with fragmented procurement systems. |
| Classification Methodologies | AI-driven classification using machine learning, NLP, and deep learning to structure spend data. However, accuracy depends on data quality. |
| Customization & Flexibility | Some providers offer highly customizable solutions tailored to industry-specific needs, while others focus on standardized, plug-and-play tools. |
| Adoption Challenges | Many providers report skepticism among buyers, requiring extensive change management and training to drive adoption. |
| Regulatory Compliance | AI solutions must adapt to industry-specific regulations, creating complexity for global implementation. |
| Supplier Collaboration | Some solutions incorporate supplier data for better visibility, but trust and data-sharing reluctance remain obstacles. |
Reflections on These Findings in spend analysis tools
- Data Integration Remains a Hurdle
- AI is only as good as the data it processes. The lack of standardized procurement taxonomies and data silos across business units pose challenges. Procurement teams must prioritize data governance strategies to improve AI accuracy.
- Human Oversight is Essential
- AI’s ability to classify spend is impressive, but human intervention is necessary to validate AI-generated insights and make strategic decisions. This aligns with the report’s advocacy for a hybrid intelligence approach, ensuring AI enhances – rather than replaces – procurement expertise.
- Customization vs. Standardization
- Some firms require highly tailored AI solutions to fit industry-specific needs, while others prefer ready-to-use applications. Buyers should assess their internal procurement maturity before selecting a provider.
- Change Management is Key to Adoption
- The report highlights buyer skepticism as a major barrier to AI adoption. Procurement teams should invest in training programs to build AI literacy and demonstrate tangible ROI to stakeholders.
- Regulatory Compliance Adds Complexity
- AI-powered spend classification must navigate complex compliance requirements. Procurement teams should collaborate with legal experts to ensure regulatory alignment when implementing AI tools.
- Supplier Engagement Matters
- Some AI providers incorporate supplier data into their spend classification models. However, supplier reluctance to share data remains a challenge. Companies can foster trust and transparency by clearly communicating the benefits of AI-powered spend analysis.
Final Thoughts: What This Means for Buyers
For procurement professionals and buyers-in-training, AI-driven spend analysis presents both opportunities and challenges. To leverage AI effectively:
🔹 Improve data governance – Invest in data cleaning and standardization before deploying AI.
🔹 Adopt a hybrid intelligence approach – Use AI for automation but retain human oversight for strategic decision-making.
🔹 Assess customization needs – Choose AI solutions that align with your procurement complexity.
🔹 Prioritize change management – Educate stakeholders to drive AI adoption and trust.
🔹 Ensure regulatory compliance – Work with legal teams to navigate evolving procurement regulations.
🔹 Enhance supplier collaboration – Encourage suppliers to share data via Supplier Portal and onboarding functions hence increase the data quality and potentially optimize AI’s value in procurement.
The study provides a comprehensive roadmap for procurement teams looking to integrate AI into their spend classification processes. By understanding where AI can add value and how to address adoption challenges, procurement professionals can make smarter, data-driven purchasing decisions.
Sources: Guida, M., Caniato, F., & Moretto, A. (2025). AI Meets Spend Classification: A New Frontier in Information Processing. Journal of Purchasing and Supply Management. DOI: 10.1016/j.pursup.2025.100993
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Note: Illustration of the post “AI and Spend Analytics: A New Era for Procurement” about Spend analysis tool was created with Chat-GPT on February 13, 2025.