Conagra’s latest supply chain initiative shows how AI is shifting from a point solution to a core driver of structural redesign, powering long-term margin recovery after years of inflation and outsourcing drag.
In Brief:
From Cost Pressure to Systemic Change
Conagra Brands is taking a different approach to cost pressure. After five years of elevated inflation and third-party production strain, the company is redesigning how its operations work, using AI as a central enabler.
“We are kicking off an ambitious initiative to reengineer our core work processes, leveraging technology, including AI, to accelerate growth and lower costs,” said CEO Sean Connolly at the company’s latest earnings call.
The company’s AI strategy isn’t focused on surface-level improvements. Rather than layering AI on top of legacy systems, Conagra is embedding intelligent decision-making into the process architecture itself, starting with plant-floor automation, throughput modeling, and connected data environments.
CFO Dave Marberger pointed to specific productivity levers, including:
1. Connected shop floor modernization: Using data-driven monitoring to improve throughput and yields.
2. Manufacturing automation: Replacing manual tasks in production environments historically slow to digitize.
3. AI-enabled sourcing and tariff mitigation: Identifying supplier alternatives and cost engineering opportunities in response to new 2025 tariffs.
The company expects these efforts to drive over 5% productivity gains in FY26, up from 4% in FY25, and has explicitly classified tariff mitigation savings as a form of AI-driven cost productivity.
Repatriation With a Digital Backbone
A key area of focus is frozen foods, where supply constraints tied to outsourced chicken processing caused service disruptions and margin drag. Conagra is now investing in internal fried and baked chicken capacity, and using AI to avoid manual overhead as volume shifts back in-house.
“The advancement of our supply chain resiliency investments, including our chicken plant, will enable us to repatriate outsourced production at lower costs,” Connolly noted.
AI plays a central role in managing this transition. As production reshoring increases asset intensity, Conagra is deploying:
a. Scenario modeling for product design, helping the company reduce dependence on tariff-affected materials like tinplate steel.
b. Throughput simulation models to manage line capacity during facility upgrades.
c. Yield optimization algorithms to offset volatility in protein input costs (projected to inflate double digits in FY26).
By using AI to augment, not replace, labor and planning, Conagra aims to regain control while improving cost flexibility and execution precision.
AI as Structural Lever, Not Speed Booster
While the company hasn’t disclosed full implementation details yet, the framing suggests that AI will be integrated across planning, sourcing, manufacturing, and logistics, not just forecasting or analytics.
The shift reflects a broader inflection point. As Connolly put it, FY26 is a “transitory year,” one in which Conagra is willing to take margin pressure in order to reset for fiscal 2027 and beyond. That reset isn’t powered by price or headcount, it’s powered by rethinking how costs are created in the first place.
Looking Ahead: Will Cost Productivity Be Enough Without Demand AI?
Conagra’s focus on structural efficiency is timely, but it’s only half the equation. With frozen and snacks positioned as growth engines, the next test will be integrating AI more deeply into demand-side levers, personalized promotions, shelf-level targeting, and dynamic trade spend. If AI can simultaneously redesign the cost base and revive volume responsiveness, Conagra’s supply chain may become not just leaner, but sharper.