AI for Supplier Discovery

Rethinking Supplier Discovery in a Digital Era

As CPOs seek to increase the strategic value of procurement, many have adopted digital platforms to improve process efficiency, data visibility and governance. These tools have delivered significant benefits across sourcing, contracting and supplier management. However, supplier discovery remains a persistent challenge. It often depends on fragmented information, contextual nuance and manual effort, making it difficult to automate or scale effectively.

Why Traditional Supplier Search Falls Short

Traditional approaches such as supplier marketplaces, curated databases or outsourced research can offer value but also have limitations. These include high costs, limited data coverage and the need for manual customisation to align with specific business requirements. Often, I find clients would prefer to stick to the suppliers they know rather than invest in robust supplier discovery, as the effort of manual research is perceived to be too great to justify. This often leads to missed opportunities to innovate, improve resilience and save money by limiting visibility of the supply market. 

AI Tools That Do the Heavy Lifting in Supplier Discovery

AI is increasingly well placed to address the challenges of supplier discovery by automating slow, manual research tasks and improving access to relevant, high-quality supplier LLMs, such as ChatGPT, are perfectly positioned to support early supplier research and discovery as they are able to synthesise large amounts of publicly available information to generate tailored supplier longlists or summaries from natural language prompts. However, browser-based generic LLMs are limited in their output formats, prone to hallucination and generally only draw from publicly available information. Some companies use offline LLMs, but browser-based versions lack data security, so sensitive information cannot be shared, limiting their effectiveness.

Procurement-specific AI applications offer more varied functionality, including:

• Maintaining enriched supplier databases with ESG, financial, and risk data
• Producing category-specific market intelligence
• Matching complex sourcing requirements to supplier profiles

• Creating dashboards to compare and evaluate supplier performance

They often also integrate with internal systems like ERP and vendor records, as well as external market intelligence platforms, improving the accuracy and relevance of insights, while embedding AI-driven discovery into existing procurement workflows. This can significantly improve the efficiency of supplier discovery by providing not just raw data, but also summaries and insights to help inform decision AI applications aren’t a cure-all however. Current AI-powered supplier discovery tools can fall short when data is incomplete, sourcing needs are complex, or the tools are not properly implemented. Without a well-thought-out AI strategy and strong user adoption, organisations risk underutilising the technology and missing out on its full potential to drive better, faster decisions.

H&Z’s View: AI in Supplier Discovery Is No Longer Optional

From our experience of the market, there are already numerous AI applications that address the slow, manual aspects of supplier discovery — particularly around research, data gathering, and analysis. However, human input is still needed to validate insights and make final decisions. AI is improving quickly, and we believe it will soon be able to fully automate discovery for low-risk products, even feeding shortlists directly into sourcing events with minimal oversight. What is clear is that procurement teams not using AI for supplier discovery are already falling behind. It reduces admin time and enhances decision-making, allowing fewer people to make better choices — faster and with more data.