Shoppers want more before they’ll make a decision and click buy: more images, more detail, more proof that a product does what it claims, more reviews from people who already own it. Catalogs are growing to meet them, adding new SKUs, new variants, and new channels every quarter.  

The teams responsible for the content behind all of it, on the other hand, are not growing at the same rate. And the skills these newer content demands require, from review program design to AI-ready data structuring, are hard to find and harder to hire for on short notice. 

That combination puts most content teams in a bind: expectations rising, catalogs expanding, headcount flat, and specialized expertise nowhere on the org chart. Three approaches are helping brands close that gap without simply asking a smaller team to do more. Each solves a different piece of the problem, so the right starting point depends on which piece is breaking first. 

The content gap is actually two separate constraints 

It’s tempting to treat “we need more content” as one product experience problem with one fix: increasing capacity—either by hiring more people or hoping existing staff and new technology and processes can absorb the load. In practice, those solutions rarely hold up to the economic reality many organizations face today: frozen headcounts, tightening belts, and roles spread thin across fragmenting tactical requirements and strategic needs.  

The first gap is production capacity: photographing, verifying, building and assets for a SKU and attribute count exploding faster than an internal team can turn them around.  

The second is velocity and consistency: getting that content and data into the market and keeping descriptions, attributes, and layouts current across a catalog that never stops changing. 

A brand struggling with one of these gaps rarely fixes it by throwing more hours at another. But some are using creative, unconventional ways to meet the rising demand for content. 

Turning shopper feedback into a stream of conversion-boosting content 

Some enterprising brands are outsourcing a portion of their content generation needs to a potentially surprising workforce: their customers.  

User-generated content (UGC) can be a goldmine of cost-effective, highly effective information and feedback. As discovered in research for The State of Product Experience 2026, ratings and reviews are now the most considered factor in making online shopping—edging out other traditional decision leaders like price. Products with even a handful of reviews convert considerably better than products with none.  

Most brands recognizing that reviews matter. It’s the challenge of generating enough of them, consistently, across an expanding catalog that keeps growing. 

MillerKnoll, the design group behind Herman Miller and Design Within Reach, ran into exactly that wall. The company manages 153,000 SKUs across 3,370 product pages and 164 brands, and review volume across that catalog started out too thin to give shoppers the confidence a considered, high-ticket purchase requires. Working with PowerReviews by Syndigo, the team built a structured, incentivized review program: sweepstakes campaigns offering furniture in exchange for honest feedback, timed post-purchase emails, and cross-brand syndication so a review written on the Herman Miller site could also appear on Design Within Reach. Seventy-six percent of shoppers say they’re more motivated to leave a review when offered an incentive, and MillerKnoll’s results bore that out. Its first sweepstakes campaign lifted review volume by nearly 400% compared with the prior period, and the program has now run three times.

The payoff showed up directly in conversion. MillerKnoll saw a 350% increase in conversion when shoppers engaged with reviews and a 566% increase when they engaged with Q&A.  

Offloading tactical tasks to make room for strategy 

More companies are routing defined pieces of content management to outside specialists rather than expanding their own studio and QA operations.  

It’s a targeted move: in many cases, it doesn’t make sense to invest heavily in internal full time headcount for the highly specialized skills needed for much of PXM’s production work. Perhaps their needs are seasonal or limited to a handful of items that don’t need constant attention, or on the other hand maybe their demands are so great it would take a whole dedicated department to keep up with content work.  

In cases like that, some businesses send the repeatable, high-volume, specialized work, like product photography, CGI rendering for items that haven’t shipped yet, dimensional measurement, and data verification, to a team built to handle it at scale. That frees the internal team to keep their focus on strategy and innovation instead of getting bogged down in the minutia of product data management and syndication. 

Syndigo managed content services is one example of this model in practice. Its managed teams handle physical photography, CGI for pre-production items, verification against retailer requirements, and enhanced content builds, functioning as an extension of a brand’s existing team rather than a replacement for it.  

Legendary ice cream brand Blue Bell’s portfolio-wide UPC transition shows where this model can be especially useful. Moving more than 100 products to new codes required the company to coordinate packaging, product data, retailer reauthorization, and ecommerce imagery on a fixed timeline. They used a specialized content studio to capture and create all the imagery and product details they needed for the project quickly and efficiently. 

Specialized capture and QA work, done by people who do it every day, tends to produce fewer retailer rejections and faster launches than the same work squeezed into an already-stretched internal team’s schedule. For a brand deciding whether to hire three more content specialists or bring in a partner built for exactly this kind of volume, the second option often gets products to shelf faster without adding permanent headcount. 

AI takes on volume and optimization (with a human still in the loop) 

AI-assisted tools are starting to earn a real place in keeping pace with a constantly-changing catalog, though the evidence for this tactic is newer and narrower than the others.  

Growing demand for content and product information is pushing brands to look at AI not as a replacement for content teams but as a way to handle the tedious, repetitive parts of content production: generating draft descriptions, mapping attributes to retailer-specific taxonomies, and assembling enhanced-content layouts from existing assets instead of building each one from scratch. 

AI-assisted Attribute Value Mapping & Transformation is a good example of where this pays off. Matching a brand’s product data to each retailer’s individual data fields used to mean manually reconciling mismatched attribute names one by one, but now AI can automate that matching at scale across an entire seller network. 

AI tools are proving useful for scaling the mechanical parts of content work, like layout generation, attribute mapping, and first-draft copy, while judgment calls about brand voice, accuracy, and final quality still belong to a person reviewing the output. Brands adopting these tools now are betting on where the capability is headed, and building in enough human review to catch what the automation gets wrong along the way. 

Match the fix to the gap that’s actually breaking 

None of these three approaches replaces the others, and none requires a brand to pick just one. A team that’s short on production capacity gains the most from outsourcing defined, high-volume work. A team with thin review coverage on a considered-purchase catalog gains the most from a structured, incentivized review program. A team drowning in repetitive content-maintenance tasks gains the most from AI-assisted generation and layout tools, deployed with a reviewer still in the loop. 

The starting point is identifying which piece, production, trust, or velocity, is the one actually holding the business back right now, and bringing in the specific capability built to solve it instead of asking an already-stretched team to somehow do all three.