New analysis reveals the trends that could affect agricultural trading and the capabilities traders will need to navigate commodity market volatility 

New analysis released by McKinsey & Company (McKinsey) finds that agriculture merchants and processors will need to find ways of improving the agility of their trading teams and the analytics these teams use to inform their decisions as they look to navigate commodity markets that are more complex and rapidly changing than ever before. 

McKinsey's How agility and AI could rewire agriculture trading details how the increased frequency and intensity of weather outliers, vacillating trade policies, price volatility, and other external factors have made it increasingly challenging for leading commodity merchants to anticipate shifts in supply, demand, and trade flows.  

With the increase of unpredictable events likely to continue, players that do not invest in these capabilities risk being structurally disadvantaged as new digitally sophisticated market entrants pay to close information asymmetries.  

To become more nimble and ready for the future, processors and traders need to consider several avenues for transformation including: 

• Shifting from regional to global value chain optimization – Most agricultural players optimize decisions at the level of the individual business unit or regional operating company. The trouble is that these groups may hold conflicting views of what is best for the broader enterprise since global and regional leaders lack unified, standardized, and transparent decision-making processes. To enable decision-making to be more efficient, companies can pursue operational value chain transformations to reduce friction. 
• Designing an agile operating model to move quickly – An agile operating model can help differentiate companies by introducing shorter, more frequent planning cycles to enhance responsiveness to market shifts. 
• Empowering efficient collaboration by improving data quality and transparency – Trading organizations of all sizes are often hindered by poor data quality that slows decision-making and increases the cost of collaboration. Improving data quality and enabling end-to-end profit and loss (P&L) visibility for all traders across a value chain can mitigate conflict and allow traders to prioritize trades that optimize P&L. 
• Building nimble analytics that scale by interconnecting across a common domain – Ag traders can best be served by investing in a diversified portfolio of interoperable, nimble analytics that can evolve and progress over time. 

Furthermore, according to the analysis, the benefits of implementing nimble analytics portfolios are noticeable. For example, leading commodity traders that have invested in predictive analytics and value chain optimization have uplifted their profitability by 200 to 500 basis points, and deploying agentic AI in post-trade operations (for example, trade booking, reconciliation, and settlement) is expected to improve productivity by 30 to 60 percent over the next two to four years. 

Avinash Goyal, Senior Partner at McKinsey, said: "Global forces and algorithmic price discovery are reshaping agriculture trading. Traders and processors with agile, analytics‑driven organizations could define the next competitive frontier." 

Xavier Veillard, Partner at McKinsey, added: "The pace of change in agricultural markets is accelerating, and the gap between leaders and laggards is widening. The next edge won't come from more dashboards—it will come from reimagined workflows, powered by AI agents that interface with predictive and optimization models that use well-established machine learning methods." 

To read McKinsey's analysis in detail, click here.