Over the last several weeks, Ardent Partners has gone back in time with its “State of Procurement (A Retrospective)” series, featuring executive insights from its previous State of Procurement reports dating back to 2006. It’s now time to shift gears into the present, with a close examination of this year’s report, highlighting the most significant trends and issues facing today’s CPOs.
Our new weekly series brings industry insights around Ardent’s CPO Rising 2025: Navigating Global Uncertainty & Unlocking an AI-Driven Future report. Don’t miss our coverage on all things procurement, including operational strategies, technology adoption, artificial intelligence trends, and supply chain challenges.
In Part Three this week, we explore the wave of uncertainty CPOs face as well as AI’s emergence in procurement.
eSourcing Is Leading the AI Charge
AI is steadily gaining traction across various facets of the procurement organization, with use cases growing in number as organizations try harnessing the technology’s potential to enhance efficiency, insight, and decision-making. Currently, the highest levels of usage/support from AI (described as “heavy” or “good” in the survey) appear in the sourcing process and, therefore, within eSourcing technology. More specifically, AI usage is most prominent in RFP development, where 18% of procurement teams are leveraging AI to automate and streamline the creation of requests for proposal. This is followed closely by supplier discovery (17%), where AI is used to help identify new potential suppliers based on certain criteria and expanded search and analysis capabilities. Similarly, AI is being applied to bid analysis (17%), where procurement leaders and teams are using AI to more effectively assess and contrast supplier bids.
Other use cases with over 15% heavy or good usage and support include ePayables (AP automation/invoice processing), where 17% of organizations are leveraging AI to optimize front-end invoice receipt as well as processing. Moreover, AI-driven eSourcing is now something 16% of teams are utilizing.
When expanded to include “some” usage and support from AI, the integration of generative AI (GenAI) and system chatbots are emerging within procurement operations. It appears that roughly one-third of teams (34%) are starting to use GenAI technology for contract analysis and summary creation. However, ranking highest in procurement AI usage is source-to-pay (S2P) system chatbots, with 36% of procurement organizations benefitting from this AI capability. Spend analysis, a technology perfectly suited for AI, is an area that 33% report as having some AI usage and support. While this may be an accurate percentage, a lack of deep AI knowledge could mean that this number is understated.
Internal Challenges a Barrier to AI Integration
The primary obstacles preventing CPOs from unlocking greater value from AI adoption are largely internal. Key challenges include a lack of budget to invest in more support/resources (53%), data quality and access (47%), and employees lacking skills to use AI properly (36%). Moreover, CPOs maintain strong confidence in the capabilities of AI solutions themselves as only 17% (not shown) cite vendor capabilities/solutions not performing as expected as being a major barrier to achieving better AI outcomes. This indicates that while artificial intelligence is promising, organizational readiness remains the biggest hurdle to fully realizing the technology’s potential.
SIDEBAR
An AI Primer
Artificial intelligence (AI) adoption within procurement operations is still in its early stages, with most organizations applying it in targeted, tactical ways rather than as a fully-integrated capability. The path of AI within procurement over the next few years will not be linear, but the direction is clear. AI will become a core part of procurement operations, so it is important to start investigating it, and using it with controls.
The ABC’s of AI
AI is a complex technology that can be looked at from different perspectives and classifications, such as general, narrow, or application AI (e.g., computer vision, speech recognition, robotics, expert systems, etc.). Another way of classifying AI, which has more relevance to this report, is by AI technique. Within procurement technology, machine learning, deep learning, and natural language processing (NLP) are perhaps the most important today.
- Machine Learning is a type of AI that learns from data to make predictions or decisions without being explicitly programmed. In procurement, it can be used to spot savings opportunities, flag unusual spending, or predict supplier risks based on historical trends.
- Deep Learning is a more advanced form of machine learning that finds complex patterns in large datasets using layered algorithms. It is often used behind the scenes in tools that handle tasks like forecasting demand, analyzing supplier performance notes, or generating insights from unstructured data.
- Natural Language Processing (NLP) helps computers understand and work with human language — both written and spoken. In procurement, NLP can be used to extract terms from contracts, analyze supplier emails, or summarize lengthy documents quickly and accurately.
The newer AI innovations, such as generative AI (GenAI) and Agentic AI, build on these technologies.
Generative AI. Generative AI is a type of artificial intelligence that creates new content like text, images, or data based on patterns it has learned from large datasets. In procurement, it can help draft RFPs, summarize contracts, write supplier communications, or generate category insights. A common form of generative AI is the Large Language Model (LLM), which focuses on language tasks, such as summarizing documents, answering questions, or generating text. These tools respond to written prompts, so knowing how to give clear instructions is key to getting useful results. However, GenAI can sometimes produce information that sounds right but is not when the system lacks relevant training data or misinterprets the prompt. For procurement teams, this means results should be reviewed carefully, especially when accuracy matters.
Agentic AI. Agentic AI refers to a more advanced type of artificial intelligence that can take action and make decisions on its own to achieve a goal. Unlike traditional tools that wait for instructions, these AI “agents” can plan, adjust, and use other systems to get tasks done with minimal human input. In a procurement context, Agentic AI could eventually help automate complex, multi-step processes, such as running a sourcing event from start to finish, identifying and onboarding new suppliers, or continuously monitoring contracts for risk. It combines several advanced technologies, including generative AI, to operate more independently.
