In its latest report, How agility and AI could rewire agriculture trading, the consultancy argues that merchants and processors must fundamentally rethink how trading organisations operate, moving toward more agile structures supported by advanced analytics and emerging forms of AI.
The findings reinforce a broader shift highlighted previously by AgTechNavigator, where agentic AI is beginning to push the industry from human-led, experience-based decision-making toward autonomous systems capable of translating complex data into rapid, actionable insights.
Volatility exposes limits of traditional trading models
McKinsey’s analysis paints a picture of agricultural markets that are becoming increasingly difficult to navigate. A combination of more frequent and severe weather events, fluctuating trade policies, price swings, and geopolitical uncertainty is making it harder for traders to anticipate shifts in supply, demand and trade flows.
This rising unpredictability is exposing the limitations of traditional operating models, where decision-making is often fragmented across regional teams and reliant on human judgement shaped by experience rather than real-time data.
As a result, companies that fail to modernise risk becoming structurally disadvantaged, particularly as new digitally sophisticated entrants invest heavily in closing information asymmetries that have historically underpinned trading margins.
From fragmented decision-making to global optimisation
A key theme in the McKinsey report is the need to move beyond regionally siloed decision-making processes toward a more integrated, enterprise-wide approach.
Today, many commodity players optimise performance at the level of individual business units or geographies, often leading to conflicting priorities and suboptimal outcomes at a global level. McKinsey argues that operational value chain transformation – supported by unified and transparent decision-making frameworks – will be critical to reducing friction and improving overall performance.
This shift aligns closely with the emergence of agentic AI systems, which are designed to operate across datasets and decision environments, enabling a more holistic view of value chains and optimising outcomes at scale.
Agility becomes a competitive differentiator
Beyond structural changes, McKinsey highlights the importance of adopting agile operating models. Shorter, more frequent planning cycles can enable trading organisations to respond more quickly to market shifts, providing a crucial edge in fast-moving and uncertain environments.
Here, the role of AI extends beyond analytics into workflow design. Rather than simply supporting decisions, AI – particularly in its more autonomous, agentic form – can help orchestrate decision cycles, continuously updating forecasts, running optimisation models and recommending or executing trades in near real time.
As Xavier Veillard, partner at McKinsey, puts it: “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.”
Data quality and transparency remain bottlenecks
Despite the promise of AI-driven transformation, the report emphasises that many trading organisations are still constrained by poor data quality and limited transparency.
Fragmented datasets slow down decision-making, increase the cost of collaboration, and can create internal conflict between trading desks. McKinsey argues that improving data quality and enabling end-to-end profit and loss visibility across the value chain will be critical to unlocking faster and more aligned decision-making.
This mirrors a key tension identified in AgTechNavigator’s earlier coverage of agentic AI: while the technology has the potential to transform decision intelligence, its effectiveness depends heavily on the quality, governance and accessibility of underlying data.
Building interoperable, scalable analytics
Another priority identified by McKinsey is the need to develop “nimble analytics” that can scale and evolve over time. Rather than relying on monolithic systems, trading organisations should invest in interoperable tools that can interact across a common data domain.
This modular approach creates the foundation for more advanced AI capabilities, including agentic systems that can coordinate across multiple analytical models, continuously learning and adapting as market conditions change.
The potential upside is significant. McKinsey notes that leading commodity traders that have already invested in predictive analytics and value chain optimisation have achieved profitability uplifts of between 200 and 500 basis points.
Agentic AI moves into the trading stack
While much of the current focus remains on predictive analytics, McKinsey’s analysis points to a growing role for agentic AI within trading operations themselves.
In post-trade processes such as trade booking, reconciliation and settlement, the deployment of AI agents is expected to deliver productivity gains of between 30% and 60% over the next two to four years.
More fundamentally, these systems signal a shift toward increasingly autonomous trading environments, where AI agents do not just support decision-making but actively participate in – or even execute – key workflows.
Avinash Goyal, senior partner at McKinsey, describes this as a broader transformation driven by “global forces and algorithmic price discovery.” He added: “Traders and processors with agile, analytics‑driven organizations could define the next competitive frontier.”
Reshaping power dynamics in agricultural markets
McKinsey’s findings reinforce the argument that the adoption of AI – particularly in its more agentic form – is not just a technological upgrade, but a structural shift in how agricultural markets function.
The pace of change in agricultural markets is accelerating,” Veillard said. “The gap between leaders and laggards is widening.”
