January 23, 2026Agentic AIRetailCPG

S&OP Agent for planning team

From static S&OP cycles to agent-led planning

Ankur Verma

Ankur Verma

CEO, TrueGradient

S&OP Agent for planning team

In Consumer Packaged Goods (CPG) and Retail, planning complexity compounds fast.

  • > Demand fluctuates across SKUs, channels, and regions
  • > Promotions distort signals
  • > Supply constraints surface late
  • > Inventory swings between excess and stock-outs

Yet most S&OP processes still operate as monthly rituals—driven by spreadsheets, manual overrides, and last-minute consensus.

TrueGradient changes this paradigm with an AI-native S&OP Agent—built to support CPG planners to become thought partner in enriching forecast.

Planner Agent
Planner Agent

Instead of navigating dashboards or running static reports, planners start with intent.

  • “Calculate the YoY change in forecast for Furniture Category”
  • “Identify excess stock risk for the next 90 days”
  • “Simulate supply impact if demand increases by 10%”

The S&OP Agent translates natural-language questions into:

  • Forecast analysis
  • Inventory diagnostics
  • Scenario simulations

This shifts planning from tool-driven workflows to decision-driven conversations.


Using Agents to arrive at the most optimal forecast while demand planning

Demand Planning Agent
Demand Planning Agent

In CPG & Retail, forecast accuracy isn’t enough. Planners need context.

The S&OP Agent continuously evaluates:

  • Forecast vs actuals at item-store level
  • YoY growth trends
  • Short-term and medium-term sales momentum

Instead of just showing numbers, the agent explains:

  • Where demand is structurally changing
  • Where recent sales diverge from plan
  • Which SKUs and locations need attention

This creates a shared demand narrative across planning, sales, and leadership.

Ankur Verma

Ankur Verma

CEO, TrueGradient

Ankur Verma is CEO at TrueGradient, the AI-Native Planning OS for Consumer Brands and retail. He is passionate about solving the toughest business challenges through the application of Machine Learning, Deep Learning & Reinforcement Learning. In the past, he has worked at Amazon and Walmart, solving optimization on a massive scale and dealing with billions of time series (Product/Location combinations). His academic papers can be accessed here .

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