In a highly competitive retail environment where speed is of the essence, simply spotting a problem isn’t enough – the key to conversion is in the fix, says Rahul Shah, CommerceIQ CEO.
With marketplaces recalculating prices and rankings by the hour, even CPG brands that detect a problem within minutes can still lose the sale if they can’t shorten the time to execution.Adding more AI-generated recommendations doesn’t solve the problem when those insights enter the same workflow, where a team member still has to review the suggestion and manually make the change on the marketplace.
That execution gap is why enterprise software is shifting from tools that surface reports to systems that can act on them. Gartner expects agentic AI to put $234 billion in enterprise software spending at risk by 2030 as buyers reconsider capabilities that stop short at recommendations. Brands need agentic AI that carries the recommendation through to execution, making updates before a competitor takes the sale.
Why brands can’t execute fast enough on their insights
The advantage no longer goes to the brand that spots a problem first. All brands use technology that surfaces stockouts or ranking drops, so what gives one brand an edge over another is how quickly they can publish a fix. That speed used to depend on review cycles and manual approvals designed for a marketplace that, not long ago, changed much more slowly.
If a listing lost share of search on a high-volume keyword, the dashboard would flag it within the hour, and AI-generated recommendations would identify a fix. Content teams would refresh listings a few times a year during periodic resets, so the fix would wait until the next seasonal update. It would then pass between the content owner and the media owner, who worked in separate tools. That handoff could take weeks, and by the time the listing was finally updated, the shoppers searching that keyword could already have bought from a competitor.
AI tools that recommend the necessary fix have little impact if the workflows for executing those recommendations remain unchanged. Even if the insights are good, the AI investment becomes difficult to justify; Gartner expects more than 40% of agentic AI projects to be canceled by 2027 over unclear ROI.
To see a return, brands need to shorten the time between recommendation and execution: A listing update published in near real time can protect the sale; the same update published next season can only explain the loss.
How brands are rebuilding their operating model around execution
Adding recommendation tools to the process didn’t fail because the recommendations were wrong, but because team members were still reviewing each suggestion and publishing it to the marketplace. The solution is to hand execution over to an AI agent, closing the gap between recommendation and action so changes go live in near real time.
When a listing loses share of search, the agent updates the content within the rules the team has set. The fix goes live within the hour, so the brand regains visibility while shoppers are still searching. The same real-time execution that exists for content also applies to media. When a SKU goes out of stock, the agent shifts spend away from Sponsored Brands and DSP ads that point to that listing, preventing the brand from sending paid traffic to a page that can’t convert.
This continuous monitoring and execution happens across the entire catalog. Teams managing thousands of SKUs could only publish so many updates per day, so listings that were selling only a few units per month had to wait a full season for a refresh, while bestsellers received the most attention. Now, an AI agent can continuously optimise every listing, so brands can start to see revenue from the long tail they had been leaving untouched.
The agent isn’t executing without guardrails, since team members set the brand rules and budget limits it operates within, so every change it publishes reflects a decision the team already made. Team members’ hours go to negotiating with marketplaces and planning promotions, not updating listings one at a time.
Brands executing first are taking sales from those stuck in review
Adding AI tools that recommend the necessary changes has little impact if the execution workflows remain unchanged. To be competitive today, brands don’t just need agentic AI; they need a solution that can identify the fix and be trusted to go live with it (based on human-set parameters), rather than waiting on a team member to review.
Brands struggling to prove ROI are likely using agentic AI to make recommendations that still sit in a review queue before anyone has time to act on them. As marketplaces move faster and the window to correct a listing narrows, the cost of that delay increases every quarter.
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You are in: Home » AI » GUEST COMMENT Retail’s AI problem isn’t generating insights – it’s executing on them
GUEST COMMENT Retail’s AI problem isn’t generating insights – it’s executing on them
Rahul Shah
In a highly competitive retail environment where speed is of the essence, simply spotting a problem isn’t enough – the key to conversion is in the fix, says Rahul Shah, CommerceIQ CEO.
With marketplaces recalculating prices and rankings by the hour, even CPG brands that detect a problem within minutes can still lose the sale if they can’t shorten the time to execution. Adding more AI-generated recommendations doesn’t solve the problem when those insights enter the same workflow, where a team member still has to review the suggestion and manually make the change on the marketplace.
That execution gap is why enterprise software is shifting from tools that surface reports to systems that can act on them. Gartner expects agentic AI to put $234 billion in enterprise software spending at risk by 2030 as buyers reconsider capabilities that stop short at recommendations. Brands need agentic AI that carries the recommendation through to execution, making updates before a competitor takes the sale.
Why brands can’t execute fast enough on their insights
The advantage no longer goes to the brand that spots a problem first. All brands use technology that surfaces stockouts or ranking drops, so what gives one brand an edge over another is how quickly they can publish a fix. That speed used to depend on review cycles and manual approvals designed for a marketplace that, not long ago, changed much more slowly.
If a listing lost share of search on a high-volume keyword, the dashboard would flag it within the hour, and AI-generated recommendations would identify a fix. Content teams would refresh listings a few times a year during periodic resets, so the fix would wait until the next seasonal update. It would then pass between the content owner and the media owner, who worked in separate tools. That handoff could take weeks, and by the time the listing was finally updated, the shoppers searching that keyword could already have bought from a competitor.
AI tools that recommend the necessary fix have little impact if the workflows for executing those recommendations remain unchanged. Even if the insights are good, the AI investment becomes difficult to justify; Gartner expects more than 40% of agentic AI projects to be canceled by 2027 over unclear ROI.
To see a return, brands need to shorten the time between recommendation and execution: A listing update published in near real time can protect the sale; the same update published next season can only explain the loss.
How brands are rebuilding their operating model around execution
Adding recommendation tools to the process didn’t fail because the recommendations were wrong, but because team members were still reviewing each suggestion and publishing it to the marketplace. The solution is to hand execution over to an AI agent, closing the gap between recommendation and action so changes go live in near real time.
When a listing loses share of search, the agent updates the content within the rules the team has set. The fix goes live within the hour, so the brand regains visibility while shoppers are still searching. The same real-time execution that exists for content also applies to media. When a SKU goes out of stock, the agent shifts spend away from Sponsored Brands and DSP ads that point to that listing, preventing the brand from sending paid traffic to a page that can’t convert.
This continuous monitoring and execution happens across the entire catalog. Teams managing thousands of SKUs could only publish so many updates per day, so listings that were selling only a few units per month had to wait a full season for a refresh, while bestsellers received the most attention. Now, an AI agent can continuously optimise every listing, so brands can start to see revenue from the long tail they had been leaving untouched.
The agent isn’t executing without guardrails, since team members set the brand rules and budget limits it operates within, so every change it publishes reflects a decision the team already made. Team members’ hours go to negotiating with marketplaces and planning promotions, not updating listings one at a time.
Brands executing first are taking sales from those stuck in review
Adding AI tools that recommend the necessary changes has little impact if the execution workflows remain unchanged. To be competitive today, brands don’t just need agentic AI; they need a solution that can identify the fix and be trusted to go live with it (based on human-set parameters), rather than waiting on a team member to review.
Brands struggling to prove ROI are likely using agentic AI to make recommendations that still sit in a review queue before anyone has time to act on them. As marketplaces move faster and the window to correct a listing narrows, the cost of that delay increases every quarter.
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