Resource | Press Release
Why Agentic Commerce Won’t Fix Siloed Decision-Making
This article was originally published on Forbes on June 23, 2026
By Dani Nadel, President and COO
Dani Nadel
Dani is the President and Chief Operating Officer at Feedvisor. She is a recognized marketing and digital expert with more than 20 years of hands-on experience managing nationally recognized consumer and corporate brands.
The headlines about agentic AI focus on the consumer side: shopping agents that compare products and systems that transact on their behalf. Whether this reshapes consumers’ behavior remains an open question.
But it’s only half the story. On the other side of the transaction, agents are already running the business itself. Less discussed, but already creating separation. Some brands using agentic systems are protecting margin and capturing demand, while others using those same tools see efficiency metrics improve even as economics decline.
For those brands, the symptom is a contradiction. Advertising spend is up. Pricing is competitive. Promotions are working. Yet Marketplace Pulse research shows only 23% of sellers are growing revenue and improving margins at the same time; the rest are either grinding or distressed. The metrics look right. The economics don’t. This isn’t a data or a talent problem. It’s a coordination failure.
The Disconnection Tax
Most brands operate through separate systems. Advertising sits with a single team and a single tool. Pricing is the same, and so is inventory. Each system has its own targets and view.
That structure worked when channels operated independently. On marketplaces today, they don’t.
Consider this scenario: A brand is running a strong advertising program. Campaigns look healthy. Then a competitor drops the price by 15%. The ad platform doesn’t register the change. It keeps bidding into a now-uncompetitive product, watches conversion slip and raises bids to compensate.
The brand now pays more to convert fewer customers. Pricing can’t see rising acquisition costs. Inventory forecasts lag demand. Three systems. Three teams. One product. No shared awareness. This is “the disconnection tax,” which doesn’t appear in any dashboard and only shows up after the fact in P&L.
Better Tools, Same Problem
The default response is predictable: Upgrade the stack. Smarter bidding. Faster repricing. It helps incrementally but reinforces the underlying issue: Each system improves at operating in isolation.
After more than a decade of building these systems and billions in managed commerce, one pattern repeats: Local optima do not sum to a global optimum. They never do.
Pricing affects conversion. Conversion affects ad efficiency. Budget allocation affects inventory velocity, which feeds back into margin. Tightly coupled feedback loops, yet most systems behave as if they’re independent.
When each system optimizes a partial view of reality, they distort each other’s signals. Your ad engine responds to declining conversion without seeing pricing. Your pricing engine reads demand signals shaped by ad spend, mistaking paid demand for pricing power.
What Agentic Commerce Actually Means
One crucial step for getting started with agentic commerce is having a unified view of the business, reasoning across variables and acting on multiple levers at once.
Consider the above scenario in reverse. A competitor ends a promotion and returns to full price. Demand is stable. Inventory is deep. Reviews are strong. A traditional ad platform might eventually notice. A pricing tool might eventually adjust. The delay costs margin. Each system sees only its slice. Multiply that blind spot across thousands of micro-decisions, and you see the structural cost disconnected systems have been hiding.
An agentic system sees the price change and simultaneously evaluates inventory, search trends, margin and competitive positioning, raising bids to capture visibility while adjusting prices to expand margin.
No team coordinates that fast. No tool spans those decisions. No stitched-together stack connects them. However, faster AI alone doesn’t solve these challenges. It can make siloed systems faster, not fundamentally different. Agentic systems weigh tradeoffs as a strategist would, in milliseconds, against the full state of the business.
What changes isn’t just speed, but what becomes visible. Tradeoffs previously split across three teams and surfaced months later now appear in real time, before margin disappears.
From Channel Metrics To Economic Outcomes
This transformation forces brands to rethink what gets optimized. Instead of evaluating isolated metrics like marketing efficiency, they must track their actual contribution margin at their specific spend level.
The difference isn’t semantic. Two brands can report identical marketing efficiencies while one expands its net margin and the other compresses it. Strong returns often mask unprofitable growth when acquisition costs, pricing and inventory are managed in silos.
When decision-making is unified, ad spend can be weighed against margin, demand, inventory and competition simultaneously to give a holistic view of profit. That reframe exposes that many brands aren’t underperforming, but that they’re highly efficient at pursuing the wrong objective.
What This Means For Brands
Hero products, brand recognition, reviews and organic ranking are compounding assets that take years to build and can erode when decisions are made in isolation. Closing that gap requires a different operating model:
1. See the whole business, not the channels. Coordinated decisions require systems operating from the same view. Fragmented dashboards and isolated tools see only a slice.
2. Break functional ownership. Assign accountability for how advertising, pricing and inventory interact, not just how each performs alone. The decisions are coupled, so the org structure should be, too.
3. Focus on margin first. Leaders should set margin floors before optimizing their spend against what those floors allow. Let margin lead the decisions, not channel metrics.
4. Act with the market. Siloed models force a trade-off: Act fast on limited data, or act slowly on broader context. When implementing agentic systems, the goal should be to mitigate this trade-off by synthesizing more signals and acting while the market is still moving.
The Real Constraint
Agentic AI is often framed as a future capability, but it’s already here in commerce.
McKinsey’s recent research on retail merchandising confirms: Early adopters are already seeing significant revenue and margin improvements, but most organizations lack the structure to scale it.
Look at your marketing dashboard. Then look at your P&L. If they tell different stories, the issue often isn’t your team or your budget but that your systems weren’t designed to operate together.
Building agentic systems isn’t trivial. It requires domain-specific intelligence tuned to how categories move, guardrails against overconfident errors and human oversight where decisions carry real risk. But the capability exists.
The remaining question: Are you still optimizing in silos? Because siloed organizations don’t just underperform, they become economically incompatible with how the market now operates.
Press Contact
Marissa Incitti, Associate Director of Contentmarissa.incitti@feedvisor.com