Himanshu Jain says CommerceIQ’s AllyAI is moving retail software from dashboards toward agents that can carry out selected work. The company’s head of products described how brands can use agents across sales, advertising, content and inventory while people provide context and review consequential decisions.
CommerceIQ combines sales, retail media, digital shelf, content, inventory and competitive data in one platform. The company says its system covers more than 1,500 retailers in 85 countries. Its AllyAI tools analyze retail signals, recommend actions, automate reporting and optimize advertising bids and budgets.
How Himanshu Jain Describes Retail Agents
Himanshu Jain distinguishes agentic retail from software that only helps a person work faster. He said an agent can learn a company’s processes and internal guidelines, then act toward a goal. A person supplies context and audits the result instead of completing every step.
He also separates agentic retail, where brands use agents to manage selling, from agentic commerce, where shoppers use them to find and buy products. Shoppers can state what they need while an agent reviews product pages, claims and reviews. The shopper still chooses, but the agent can handle comparison work first.
That change puts more weight on product page details, Himanshu Jain said. Shopping tools such as Rufus, Sparky and Alexa Shopping use product information to support purchases. General-purpose tools such as ChatGPT and Perplexity serve more like early research, where third-party coverage and citations can influence what shoppers encounter.
For brands, Himanshu Jain said that product content must serve both traditional search and agents that can read a full page. He described CommerceIQ’s AllyAI as an orchestrator that coordinates specialized agents around sales growth. It weighs opportunities and risks, then assigns tasks to agents working on media, content and pricing.
Without shared context, separate systems could work against one another. Himanshu Jain gave the example of advertising an item that is running out of stock. He said CommerceIQ built Ally to share information across its agents and avoid that kind of isolated optimization.
Himanshu Jain Favors Gradual Automation
Himanshu Jain recommends starting with work that requires little judgment and has limited impact. He pointed to long-tail products and retailers that receive less attention. People can keep strategic oversight of high-impact decisions and use corrections to train agents.
He said teams may begin with about 10 percent automation and 90 percent human approval. That ratio can change as feedback builds trust, first for lower-impact work and later for decisions with greater consequences. He cautioned that poor data can make autonomy riskier because an agent may act on an error at scale.
Himanshu Jain described structured data, with clear meaning and relationships, as much of the work behind reliable agents. A system built on inconsistent information may produce unreliable answers, he said. Giving an agent more freedom without improving its context can let a mistake spread before a person spots it.
Himanshu Jain also urged brands to act on practical advertising signals instead of waiting for perfect measurement. He said teams should avoid extra bids when a product already ranks high organically, and consider advertising when it ranks lower or reaches shoppers unfamiliar with it. The guidance favors faster, directionally useful decisions.
His expected model still keeps people involved on both sides of a transaction. Shopper agents may compare and recommend products, while people make the final purchase decision. On the brand side, agents can execute tasks as people set strategy and approve important choices.
Readers interested in related company-building questions can explore how a founder views AI agents, founder-led business strategy and product pricing decisions. Jain said the context and feedback a brand gives its agents will shape how well they can support its work.
In the interview, Jain said brands that give agents useful business context and feedback can build toward more trusted decisions. He said those that skip that work risk becoming invisible to AI-driven purchasing systems.