In the fast-evolving world of procurement, artificial intelligence (AI) and more specifically, generative AI (Gen AI), promise to change how organizations manage supplier relationships, forecast demand, and optimize sourcing. However, there’s a crucial prerequisite to reaping the benefits of these advanced technologies—quality supplier data. Without a solid data foundation, the promises of AI remain out of reach.
In this blog I’ll be sharing insights from the recent DPW Summit in New York City where we had a session with CPOs and other procurement professionals to understand the challenges they’re facing when it comes to data quality and why a solid data foundation is critical to success with AI in procurement.
Common data challenges in procurement
Building a trusted supplier data foundation
The path to leveraging Gen AI in procurement
At the recent ISM conference in Las Vegas, Patrick Marlow, Staff Engineer from the Vertex Applied AI Incubator at Google stated, “In order to properly leverage Gen AI tools like Gemini, you need good data posture. Procurement teams have data but it’s everywhere. There seems to be no data foundation. Without it, they will miss out on the exponential benefits of Gen AI.”
This sums up the hurdles procurement pros deal with today. Juggling different systems, processes out of sync, and duplicate records are just a few of the obstacles organizations need to tackle for a solid data foundation.
Common data challenges in procurement
At DPW, my team spoke to procurement leaders across industries such as pharmaceuticals, airline, consumer packaged goods, financial services, and more, and they shared some of the challenges their teams are facing when it comes to data quality. Here are a few that stood out:
- Disparate systems:
Problem: Many organizations house supplier data in multiple systems that don’t communicate with each other.
“We have all these different systems with suppliers in each of them, and they don’t connect up,” said the Head of Procurement Digital & IT at a large biopharmaceutical company. This lack of integration hampers the ability to leverage vendor data effectively.
- Misaligned processes and data owners:
Problem: Different departments control different aspects of supplier data, leading to inconsistencies and inaccuracies .
“Finance and procurement control it, but the business inputs it. The business doesn’t want to touch a procurement system,” said the Director Advanced Analytics Lead at another large pharmaceutical company. This scenario often results in compliance issues and data inaccuracies.
- Duplicate records:
Problem: Multiple entries for the same supplier across various systems.
“We have three different systems… For a Deloitte or a PWC, there could be 70 entries in the system,” reported the Head of Procurement Americas at a leading investment bank. This redundancy creates inefficiencies and confusion.
Building a trusted supplier data foundation
To solve common data quality challenges like these and to be able to leverage Gen AI, it’s critical for organizations to have a supplier data foundation that includes:
AI use cases in procurement
Once a supplier data foundation is in place, organizations will be better equipped to take advantage of AI in procurement. In speaking to the CPOs at DPW, we learned that organizations are keen on leveraging AI for use cases such as:
Supplier recommendations
AI can enhance the supplier selection process by analyzing a multitude of supplier attributes such as pricing, quality, delivery performance, and compliance history. By evaluating these factors against specific organizational criteria, AI can recommend the best fit suppliers, ensuring alignment with business goals. This not only saves time but also enhances the accuracy and reliability of supplier selection, ultimately leading to stronger supplier relationships and better procurement outcomes.
Increased operational efficiency
Imagine generating detailed Statement of Work (SOW) analyses in mere minutes. Gen AI can automatically extract key data points, organize them into a clear, actionable format, and provide insights that might have been missed. This means faster reporting, fewer errors, and more time for procurement teams to focus on strategic initiatives.
Enhanced decision-making
AI’s ability to process vast amounts of data in real-time provides procurement teams with actionable insights that are critical for informed decision-making. By analyzing trends, predicting market changes, and identifying risks and opportunities, AI supports procurement professionals in making decisions that are data-driven and strategically sound. Whether it’s optimizing inventory levels, negotiating better contracts, or improving supplier performance, AI empowers procurement teams to make decisions that enhance efficiency, reduce costs, and drive value across the supply chain.
The role of Gen AI
Gen AI, like Google’s Gemini, requires high-quality data to function effectively. At DPW, we demonstrated a Gen AI application that integrates with Gemini to provide enhanced supplier information, recommendations, and comparisons. This application relies on TealBook’s trusted data, supplemented by additional sources like Google’s search engine.
You can learn more about the application here:
The path to leveraging Gen AI in procurement
Achieving a solid data foundation is critical for successful AI and Gen AI implementations. Organizations must address the challenges of disparate systems, misaligned processes, and duplicate records to unlock the full potential of AI technologies.
Focusing on quality data enables procurement professionals to make smarter, faster, and more effective decisions, driving their organizations toward greater success. Ensuring a robust data foundation is the first step in unlocking the full potential of AI in procurement.
Stephany Lapierre is the Founder and CEO of Tealbook. A lifelong entrepreneur, Stephany spent 10 years building a successful strategic sourcing and procurement consulting firm and it sparked her desire to solve the supplier data problem. Stephany is passionate about establishing the gold standard for supplier data and making it accessible to every large organization, which is why TealBook’s Supplier Data Platform automates the collection, verification, and enrichment of supplier data across any data lake or enterprise system. TealBook is a leader in procurement technology and has been chosen by Spend Matters as one of their "50 Vendors to Know," named a top 100 solution by ProcureTech, and recognized as a "Cool Vendor" by Gartner. A highly-coveted supply chain thought leader, Stephany has been recognized as one of the Top 100 Most Influential Women in Supply Chain. Outside of work, you can find Stephany spending time with her husband and three daughters, and keeping active on the ski hill or in a pilates studio.
