In our previous article, AI strategy in AgriFood: Building a roadmap for smarter market intelligence, we explored why agrifood organizations need a structured AI roadmap and how to align AI investments with business goals. Strategy, however, is only the first step. 

The natural next question is: How do procurement and supply chain leaders put that AI strategy into action? 

Procurement functions today face relentless volatility: commodity price swings, inflationary pressures, supplier risk and changing regulatory landscape including tariffs and trade barriers. In an industry where margins are razor-thin, intuition and static spreadsheets are no longer enough. 

This is where artificial intelligence in procurement is delivering real-world impact-automating processes, sharpening forecasts and enabling sourcing strategies that balance cost, risk, and resilience. 

 

How Can AI Help in Procurement?  

AI in procurement refers to the use of GenAI, advanced algorithms and machine learning models to streamline processes, generate insights and support smarter decision-making. 

Unlike traditional tools, AI doesn’t just analyze historical averages, it continuously ingests live market signals, supplier performance data, trade flows and even weather events to predict outcomes. 

With the advent of GenAI, the possibilities are endless and the barrier to entry for advanced workflow automation and innovation is unparalleled. The result? Procurement teams shift from being reactive cost controllers to strategic value creators. 

 

Types of AI in Procurement 

AI is not one-size-fits-all. Within procurement, the most impactful applications include: 

• Predictive Forecasting – Anticipating commodity price movements, demand shifts and supply availability. 
• Automation Tools – Managing routine workflows such as invoice reconciliation, purchase order processing and supplier onboarding. 
• GenAI – Reviewing contracts at scale, flagging risk clauses, help with market research and category strategy by acting as a smart “copilot”. 
• Recommendations – Proposing supplier diversification strategies or alternative sourcing options in response to market dynamics. 
• Anomaly Detection – Identifying irregular price trends, delivery delays or supplier reliability issues before they escalate. 

 

Key Benefits of AI in Procurement 

Businesses can unlock five key, significant advantages by integrating AI into their procurement function:

1: Automation & Operational Efficiency. AI reduces the manual burden of repetitive procurement tasks, freeing professionals to focus on negotiations and supplier collaboration.

2: Forecasting Accuracy. Machine learning models trained on commodity-specific datasets deliver sharper forecasts, reducing exposure to demand and price volatility. Paired with SMEs, proprietary and curated partner data, this delivers real, actionable intelligence to users. 

3: Cost Reduction. Optimized sourcing strategies, backed by AI insights, lead to better contracts, fewer stockouts and improved margins. AI allows you to broaden your view to perform wholistic review of your entire product line, taking you from a category manager view to a true strategic planner.

4: Supplier Management & Risk Monitoring. AI systems track supplier performance, ESG compliance and geopolitical exposures, providing early-warning signals and resilience safeguards. Imagine an automated system that helps you get ahead of disruptions and allows you to focus on what’s important.

5: Strategic Value Creation. With efficiency gains in place, procurement leaders can focus on long-term priorities: building resilient supply ecosystems, advancing sustainability goals and aligning procurement with corporate strategy. 

 

Challenges and Considerations for AI Adoption

AI adoption is not plug-and-play. Key challenges include: 

• Data Quality & Integration – Fragmented or inconsistent data undermines AI accuracy.
• Change Management & Buy-in – Teams need to see AI as an enabler, not a threat. 
• Interpreting AI Outputs – Leaders require explainable AI to build trust in recommendations. 
• Domain-Specific Tuning – Generic AI (GenAI or traditional models) fails in agrifood; models must be tuned to commodity nuances and market realities. 

Success starts with a clear AI strategy and roadmap. Procurement is where that strategy becomes execution. 

 

How Expana Supports Procurement Teams with AI 

At Expana, we’ve built AI-powered market intelligence tools specifically for agrifood procurement leaders with Expana IQ.

Our platform offers: 

Commodity Price Forecasts – Highly accurate, model-tuned predictions for individual commodities. Using sophisticated algorithms and advanced AI technologies that work alongside our experts, you get the most up to date and accurate forecasts.  

Market Intelligence Aggregation – Bringing together time series, forecasts and global news into one intuitive hub. AI helps you find the right high-quality data and news, both when you search and through smart suggestions. 

Cost Model Development – Develop sophisticated Cost Models that use high quality time series to suggest cost and weight percentage models on the fly. An agentic and highly tuned AI workflow helps build Cost Models with high accuracy. 

As laid out in our previous article, our approach reflects the principle of augmented intelligence where AI amplifies human expertise, rather than replacing it.  

 

AI is transforming procurement by driving efficiency, cutting costs and building more resilient supply chains. Organizations that embrace predictive analytics and AI-powered tools are better positioned to make smarter sourcing decisions and stay ahead in supplier management, sustainability and risk mitigation.  

Those businesses that want to thrive in this challenging environment need to ensure they are using the full range of tools and strategies available to stay ahead of the competition.  

 

Image source: Getty