Peak commerce moments have a way of revealing the truth about a company’s operations. When demand surges, the hidden cracks in a system start to show, and there’s no room for error. Weak product data can lead to wasted clicks, while inventory mismatches can result in disappointed customers.
Peak Season Becomes Constant Flux
In the past, peak season was a predictable event, but now it’s a constant state of flux, with a steady stream of surges, promos, trends, and channel shifts. Companies used to have most of the year to prepare for big sales, but now they must be able to adapt quickly to changing buyer behavior.
Closing the Commerce Execution Gap
The Rithum 2026 commerce readiness index, based on a Wakefield Research survey of 200 retail and brand executives in the U.S. and U.K., highlights the challenges companies face in staying ready for an always-on peak season. Almost 75% of respondents said that data quality issues sometimes affect business decisions, and more than one-third said it happened “often” or “all the time.”
Meanwhile, 100% of retailers and 99% of brands say they feel confident measuring performance across marketing and commerce channels. However, there’s a gap between data quality and data confidence, which is the root of the problem. Agility depends on signal quality, and if the inputs are inconsistent, teams either hesitate and miss the moment or move fast on the wrong information and pay for it.
Closing the Data Quality Gap
The best fix for this gap is to focus on cleaning up product truth across every channel. Starting with product truth and enforcing it as a basic infrastructure is key. If shoppers click an ad and land on the wrong selection, trust collapses in seconds.
Companies can start by choosing the product fields that cannot vary across priority channels and locking them down: titles, primary images, core attributes, and variant mapping. Running a daily check on top promoted SKUs across top partners and fixing mismatches before scaling spend is also essential.
Another key step is to stop running commerce at spreadsheet speed. Nearly half of retailers and brands say 26% to 50% of workflow still relies on spreadsheets, manual data entry, and manual approvals. Poor data quality impacts are felt across the organization, and data quality matters even more now because AI and automation scale whatever you feed them.
Eliminating Spreadsheet-Driven Workflows
Manual steps and messy data don’t just slow work; they multiply errors across channels. Companies should pick one handoff that creates repeat errors or delays, such as catalog updates, inventory sync, order exceptions, or returns disposition, and replace one manual step with a rule based on current data.
Putting a reality gate in front of spend is also key. Before putting paid support behind a SKU, companies should confirm three basics: mapping is correct, inventory is real, and the delivery promise is one they can keep. If any one of those fails, they should pause spend until reality matches the message.
In the last year, 91% of retail leaders and 84% of brands changed their marketing channel mix. Companies should make the gate shared across marketing, ecommerce, and operations to ensure that they’re not scaling unprofitable demand.
Understanding True Order Costs
Knowing what an order costs to fulfill before chasing more of them is essential. Margin leaks through channel fees, fulfillment choices, returns volume, and service contacts. Promotions can worsen the leak by shifting demand into the least profitable shipping option.
External volatility adds pressure, too. In the survey, 46% of retailers and 59% of brands say they are at least somewhat concerned that tariff and trade policy uncertainty will disrupt sourcing strategies. When costs vary, companies need to know which orders stay profitable.
AI raises the stakes because it scales whatever you feed it. In the survey, 41% of retailers and 29% of brands already use AI-based automation across areas like pricing, inventory, and marketing. Another 41% of retailers and 57% of brands have concrete plans and are preparing to implement soon.
Instead, go back to that infrastructure layer: get cleaner inputs such as product attributes, inventory signals, fees, shipping costs, and return rates. Standardizing what true looks like for the data the system touches and blocking bad data before it reaches customers is key.
When inputs fail, companies should stop the automation. They should not let it keep spending, routing, or repricing using bad inputs. By focusing on cleaning up product truth and putting a reality gate in front of spend, companies can build in agility and serve customers without breaking their promises.
