The Content Bottleneck Has Shifted from Production to Operations

Just how tangible the cost of fragmentation in brands’ content and creative operations has become. We’ve been seeing teams acknowledging the problem, saying “yeah, our digital assets are scattered, our workflows are messy.” But this year the data puts real business consequences behind that. The survey found 44% of folks reporting employee burnout tied directly to poor asset management. Wasted budget, duplicated work, missed revenue, and delayed launches are no longer hypothetical risks of fragmentation, they are measurable business outcomes.

The other thing that struck me is product content and information as a major theme. Previous reports focused heavily on creative assets, but this year we saw just how much brands are struggling to manage product information alongside their digital assets. 88% of teams can’t keep product content consistent across channels, and more than half are still managing product data completely separately from the assets used to actually market and sell those products. That disconnect is creating real friction, especially as e-commerce demands keep growing.

 

AI is driving the content volume up, but it’s also the thing helping teams manage the surge. 75% of content professionals told us AI has increased their output, and 30% said that increase was significant. At the same time, about half of teams are already using AI to accelerate creation, tagging, and organization. So the same technology pushing volume higher is also becoming the relief valve.

A critical part of the data, though, shows how teams are falling short on the operational side. The volume is growing but the underlying systems and workflows haven’t yet caught up. Only 43% of teams describe their digital content workflows as standardized and automated. The rest are still dealing with manual processes, inconsistencies, and fragmented tools that slow everything down. You can produce content faster with AI, but if your team can’t find the right asset, doesn’t know which version is current, or has to manually push updates across channels, you’re just creating more chaos. The content bottleneck has shifted from production to operations.

Q3: One of the more striking data points is that teams with full connectivity between their digital assets and product information are more than four times as likely to report significant ROI improvements. Why is that gap so wide?
 

That 4x multiplier surprised even us, but when you think about what connectivity actually enables, it makes sense. When your product information and digital assets live in the same environment, you eliminate an enormous amount of duplicated effort. Teams aren’t hunting across systems to match the right image with the right product description, nor manually updating the same information in five different places every time something changes.

The data backs this up across the board. Teams with fully connected systems told us that tasks like locating assets, maintaining brand consistency, and collaborating across teams were “extremely easy” at rates two to three times higher than everyone else. 67% of fully connected teams said locating assets was extremely easy, compared to 21% of those without full integration. That speed and confidence compound across every campaign, every channel update, every product launch. Just as importantly, it all shows up directly in revenue. 65% of teams that can make real-time content updates reported significant revenue increases, versus just 16% of those with slower timelines.

Q4: Only 35% of respondents said they feel very confident that employees are using the most current, approved version of brand assets. What’s driving that confidence gap, and what does it cost organizations?
 

That number reflects the reality of how most brands manage their content today. When assets are spread across cloud drives, local desktops, email threads, and multiple platforms, it becomes almost impossible to guarantee that everyone is working from the same source of truth. 62% of teams are using cloud file storage, 44% have content on local servers, and 41% still rely on individual hard drives. That’s a lot of places where an outdated logo or last quarter’s product spec can be sitting around, ready to get used by mistake.

The cost shows up in a few ways. 30% of respondents reported publishing off-brand or inconsistent content as a direct consequence of poor asset management. You also see it in the 30% who flagged legal or compliance risk. When you’re operating in regulated industries or across global markets, using the wrong version of an asset can have real legal and financial consequences.

Beyond those specific risks, there’s the broader drag on team productivity. People spend time second-guessing whether they have the right file, chasing down approvals, or recreating something that already exists somewhere in the organization.

 

I think a lot of brands have grown into this situation organically. Spreadsheets are familiar, they’re flexible, and when you only have a handful of products or channels, they work fine. But when you’re managing hundreds or thousands of SKUs across e-commerce platforms, retail partners, marketplaces, and your own website, spreadsheets stop scaling. You end up with version control nightmares, no clear ownership, and inconsistencies that erode customer trust.

 

The shift really starts with recognizing that product content and creative assets are two sides of the same coin. A product image, its description, its specifications, its pricing…those all need to move together when something changes. When 78% of teams are using two or more separate solutions just to manage product content, every update becomes a multi-system coordination exercise. Teams told us the improvements that would deliver the most benefit include making product data easier to access across teams, eliminating duplicate or outdated information, and managing product data alongside creative assets. Those are all fundamentally about bringing things together rather than continuing to manage them in silos.
 

The adoption curve is real, and I think it’s actually healthy that most teams are taking a measured approach. 47% are using AI in limited ways, and 16% are planning to adopt. That’s a lot of momentum. But moving from experimentation to deep integration requires trust, infrastructure, and governance, and those things take time.

On the trust side, the news is actually encouraging. 81% of content professionals expressed confidence in AI’s accuracy for tagging and organizing assets, and that confidence gets even stronger with hands-on experience. Among teams using AI most extensively, 77% reported high confidence. The hesitation seems to be more about the operational layer. When we asked about top worries over the next two years, integrating new technologies like AI tied for the number one concern alongside security and access control, both at 30%. Teams want to adopt AI more broadly, but they want to do it in a way that doesn’t compromise brand consistency or introduce new security risks. Brands seeing the biggest returns are embedding AI into a centralized, governed environment rather than layering it on top of fragmented systems.
 

 

That 48% to 0% gap is one of the most compelling findings in the report, and it really underscores that operational maturity isn’t merely a nice-to-have.

In practice, workflow maturity starts with processes for creating, reviewing, approving, and distributing content that are documented and consistent across teams rather than reinvented every time. On top of that, the repetitive work (like tagging, metadata generation, format conversions, routing assets for approval) is automated instead of eating up people’s time. Additionally, the tools are connected so that updates flow through the system rather than requiring someone to manually copy information from one platform to another.

The teams doing this well are also much more likely to have invested in AI, analytics, and template-driven approaches. When we looked at what high-ROI organizations are doing differently, they’re significantly more likely to be introducing automation to reduce manual work, expanding template and modular content approaches, and measuring content performance to refine their processes.

 

I’d say these concerns are actually more interconnected than they might appear at first. Security and access control, AI integration, and brand consistency all improve when you centralize how content is managed and governed. If your assets are scattered across disconnected systems with inconsistent permissions, you have a security problem, a brand consistency problem, and a much harder time rolling out AI in a controlled way.

The practical starting point is getting your foundation right. That means establishing a centralized, structured environment where assets are governed, versioned, and accessible to the right people with the right permissions. Once that’s in place, you can layer in AI capabilities, things like smart tagging, visual search, and content recommendations, with confidence that the AI is working within guardrails rather than amplifying existing chaos. And brand consistency becomes much more manageable when there’s one source of truth rather than dozens of repositories where outdated files can linger.

 

I think content leaders should also be paying attention to the product content dimension. Managing storage costs at 26% was one of the top concerns, and that’s only going to grow as content volume keeps climbing. The teams managing costs most effectively can reduce duplication, improve reuse, and avoid recreating assets that already exist somewhere in the organization.

 

 

Audit your fragmentation! Take an honest look at how many systems, folders, drives, and platforms your team is using to manage content and product information today. The data is really clear that fragmentation is the single biggest drag on performance. Teams using two or more systems to manage digital assets are significantly more likely to experience delays, missed revenue, burnout, and wasted budget compared to those working from a unified approach.

You don’t have to solve everything at once, but understanding the full scope of the problem is the first step. Once you can see where assets and product information are scattered, you can start making intentional decisions about what to centralize, what to connect, and where to apply AI and automation to eliminate the most painful bottlenecks.