The role of AI in intelligent procurement decision-making

The third blog exploring Beroe’s latest report from its Future Visions series, “Artificial Intelligence: Real Decisions”, moves beyond the buzz to unpack how organizations can apply AI purposefully, matching the right type of intelligence to the right moment in the procurement lifecycle.  

As AI becomes part of everyday workflows, from drafting emails to surfacing insights, its value to procurement teams lies not in novelty, but in its ability to sharpen decision-making. But what does it truly take to enable intelligence-driven decision-making, moving beyond the hype around new tools to focus on the structural capabilities that really matter? 

The role of AI in intelligent procurement decision-making 

Whether it is employees using Microsoft Copilot to help them draft emails, or ChatGPT to curate information, people are already using AI every day. So, when it comes to the procurement team, the value of AI lies not in its novelty, but in the precision it can bring to their work.  

Yet, too often, organizations approach AI as a monolith, deploying one-size-fits-all tools that overpromise and underdeliver. The reality is more nuanced: AI is a set of technologies, not a single solution. Furthermore, in a hype cycle such as that characterizes AI today, organizations can be prone to adopting technology without fully thinking through all the dimensions of deployment. So, while knowing which type of AI to use, and where, is critical to maximizing its benefits and unlocking practical, scalable impact, understanding when and whether to use AI are equally important considerations.  

Matching the right AI to the right problem  

The key is to anchor AI efforts to specific decision moments across the procurement lifecycle, such as selecting suppliers, assessing risk, forecasting prices, or negotiating terms, and choose the AI method best suited to the complexity, ambiguity and data context of each task.  

Getting this right requires both internal and external collaboration. Business priorities must be aligned with data capabilities, and the AI type that best matches both the complexity of the specific task and the maturity of the organization. There are multiple approaches to designing AI systems, each with their strengths, limitations, and ideal use cases. Getting this right requires both internal collaboration between procurement and tech teams; and external collaboration with vendors who are using AI technology and for whom this is their core capability.  

Choosing the right AI is not just a technical decision, it’s a strategic enabler. Different use cases require different capabilities and, when applied correctly, AI can compress procurement cycle times, reduce risk exposure, enhance decision quality, and free up human capacity for higher-value work. For ‘deterministic’ tasks, such as invoice matching or enforcing procurement policies, a rules-based AI is the correct choice. In contrast, for ‘probabilistic’ tasks, such as where the procurement team needs to mitigate risk by understanding the impact of potential price changes, tariffs, or geopolitical disturbance on the supply chain, a predictive AI is required.  

Procurement teams don’t have to become experts in all types of AI – but they do need to be able to ask the right questions of their solution providers to see what is real or will have what impact. This is because each AI type offers something different in practice. 

“Artificial Intelligence: Real Decisions” explores how leading procurement teams can use AI today to strengthen decision-making across key domains spanning insight generation and risk forecasting to supplier engagement and strategic sourcing.