Content Operations in the Age of AI: Insights from Top Leaders

In today’s digital landscape, content is more than communication—it’s infrastructure. As artificial intelligence (AI) reshapes how organizations create and deliver information, content operations (content ops) have emerged as the hidden engine behind business transformation.

That reality was front and center in a recent panel hosted by Content Science President Colleen Jones, who gathered leaders from diverse industries to unpack new findings from the firm’s 2025 update from its ongoing study of content operations. Panelists included Steven Pritt, Vice President of Content Marketing at Thomson Reuters; Laura Barnes, Senior Director of Global Marketing at Red Hat; Elizabeth Mendes, Associate Dean of Communication at Emory University’s Rollins School of Public Health; and Cory Bennett, Senior Director of Digital Product Development at Sallie Mae.

Together, they explored how content operations maturity drives measurable success—and how AI is amplifying both the opportunities and the challenges.

Why Content Ops Matter More Than Ever

Organizations are spending billions on AI and digital transformation, yet many struggle to see tangible returns. According to Content Science’s latest research, one reason is clear: Content operations maturity is now one of the strongest predictors of success.

The data shows that 40% of organizations rate their content ops at level 3 (established), 25% at level 4 (scaling), and only 5% remain at level 1 (chaotic)—a dramatic improvement from 2023, when 20% were still at the lowest level.

Even more striking, 80% of “extremely successful” organizations and 70% of “very successful” ones operate at higher maturity levels.

For Steven Pritt of Thomson Reuters, the reason is simple: “Content operations are the framework that lets us deliver high-quality content effectively at scale,” he explained. “It’s the engine that powers the entire content lifecycle—from planning and creation to distribution and measurement.”

Pritt added that as AI accelerates the pace and complexity of digital content, operational rigor becomes nonnegotiable: “Content has become the primary driver of customer experience. Without the right processes, technology, and governance behind it, even the best content strategy will falter.”

Building a Foundation for Success

At Emory University’s Rollins School of Public Health, Elizabeth Mendes experienced firsthand how operational foundations can transform an organization’s communications capacity.

“When I joined, we had almost no formal capability around content or communications,” she said. “In less than a year, we built a sophisticated strategy and operations model.”

Her approach centered on creating a clear operating model that defined roles, systems, and workflows. “When technology changes, or leadership changes—as it often does—the content operations model is what keeps us grounded,” Mendes emphasized.

The lesson: Content ops aren’t just about efficiency. They create stability and resilience in fast-changing environments, allowing teams to deliver consistent quality no matter what’s happening around them.

Vision as a Catalyst

The research revealed that organizations with a defined content vision are more likely to report both higher maturity and greater success. Yet many still struggle to communicate that vision effectively across teams.

For Mendes, establishing a vision was the critical first step. “We sat down with our dean and asked: What is the future state we’re aiming for?” she recalled. “That vision became the foundation for our strategy and helped us secure leadership investment.”

Laura Barnes at Red Hat echoed the importance of vision—but with a twist. In her role leading global marketing, she’s used vision to align teams that historically operated in silos.

“One of my peers leads our documentation team. I’m in marketing. We realized that content operations could be a common language across both,” Barnes said. Together, they created a shared vision that included aligning metadata, taxonomy, and templates—what Barnes calls “a handshake” between marketing and customer content services.

That collaboration evolved into Red Hat’s internal “Content Conference,” a forum where teams share best practices and reinforce a unified approach. “Operations can be a common language,” Barnes said. “It helps people across the organization see how their work connects to the customer experience.”

Measuring What Matters

While half of organizations now measure content effectiveness—up from one-third in 2023—many still cite challenges such as lack of tools, unclear objectives, and limited time.

Yet content measurement is where maturity meets momentum.

For Cory Bennett of Sallie Mae, the key is starting small: “You don’t have to have it all figured out,” he advised. “Start scrappy. Wherever you are, identify what’s most important to your customers and build from there.”

Steven Pritt agreed. When he launched Thomson Reuters’ center of excellence focused on content, his team shifted from vanity metrics to a focused set of indicators tied to business goals.

“At first, we picked just three metrics for thought leadership and three for demand generation,” he explained. “We refined those over time and eventually combined them into a scoring model to evaluate content success. It helped us move from being seen as a ‘content factory’ to a trusted partner that drives growth.”

The takeaway: effective measurement isn’t about tracking everything—it’s about defining what matters most and measuring it consistently enough to build credibility and, ultimately, a system of content intelligence.

AI: From Experimentation to Enablement

Few topics sparked more discussion than artificial intelligence. Content Science’s data showed a significant increase in AI use: 86% of participants report using it, and nearly one in five now call AI central to their content strategy.

However, the organizations at the highest levels of content ops maturity and success are taking a balanced approach—focusing less on pure AI content creation and more on AI-assisted operations, such as metadata tagging, translation, and content analysis.

At Red Hat, Barnes and her team have been early adopters. “We built internal tools like LocSync, a translation assistant, and a metadata bot that saves us 30 minutes per project,” she said. “We also created a ‘content toolbox’ that includes link checkers, dashboards, and a style guide—all powered by AI where it makes sense.”

But the emphasis, she noted, is on augmentation, not automation. “We use AI to assist with content creation, not generate it. For example, it can help outline an article or draft interview questions, but humans make the final call.”

Cory Bennett underscored that point, emphasizing that AI success depends on the strength of the operations behind it. “If your content operations aren’t solid, AI just amplifies the chaos,” he said. “But if you have good governance and measurement in place, AI can accelerate quality and consistency instead of undermining them.”

Meanwhile, at Emory, Mendes’ team is taking a deliberate, ethical approach. “We’re a non-technical team working within an academic institution,” she said. “AI is a helper, not a creator. Our university has issued clear guidance for communicators, which has been incredibly valuable.”

All leaders agreed: scaling AI effectively requires governance, clarity, and an ongoing conversation about quality and responsibility.

The New Maturity Mandate

Across all discussions, one theme was unmistakable: content operations maturity is becoming a strategic differentiator.

As Colleen Jones summarized, “Organizations that invest in content operations—defining content vision, measuring content impact, establishing content governance, and building content intelligence—are not just keeping up. They’re leading.”

The message from the panel was clear: content ops are no longer a backstage function. They are how organizations sustain innovation, consistency, and trust in an AI-driven world.

About the Research

Content Science’s 2025 update to its content operations research draws on data from 100 survey participants and 12 in-depth interviews across industries. The study explores maturity trends, vision and measurement practices, and the evolving role of AI in content management.

Access the full 95-page report, What Makes Content Operations Successful in the Age of AI, to explore the complete data, insights, quotes from other leaders, commentary from our team, checklists, and more: https://content-science.com/content-operations-study/ >>