AI, Authenticity, and the Future of Content Teams

In this episode of Content Powers AI, Red Hat’s Laura Barnes joins Colleen Jones to explore how enterprise content teams can adopt AI without losing the human qualities that make their work compelling, authentic, and differentiated.

AI can generate content at remarkable speed. But speed alone doesn’t make content valuable.

For enterprise content teams, the bigger question is how to use AI to improve the work without losing the expertise, authenticity, and human perspective that make content worth engaging with in the first place.

That’s the balance Laura Barnes, senior director at Red Hat, is working toward.

In this episode, Barnes joins Colleen Jones, president of Content Science, to discuss how Red Hat’s content team is moving from early AI experiments to practical tools and more advanced workflows. Their conversation offers a grounded perspective on what AI adoption can look like when teams start with the work itself—not the technology.

The approach begins with a simple but powerful idea: AI can take on repeatable and quantitative tasks, freeing people to focus on judgment, expertise, and the content that makes a difference to customers.

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Start With Automation, Not Content Generation

When organizations explore AI, it’s tempting to begin with the most visible use case: Generating content. In fact, Content Science research finds content creation is by far the most common AI use case organizations start with. That same research also finds organizations struggle to scale AI content creation. Other AI use cases are far more successful.

Wisely, Barnes suggests a different starting point.

Automation is really the training wheels of AI.

For her team, the early opportunity wasn’t necessarily to have AI write more content. It was to identify repetitive tasks that consumed time and energy without requiring the full attention of a content professional.

Barnes and her team began by asking people what they no longer wanted to do—and what felt like a waste of their time. Those answers helped surface practical opportunities for automation.

The team then used short, six-week sprints to test ideas, gather feedback, and vote on what to build next.

This approach made AI adoption a collaborative process. Rather than imposing a tool on the team, Red Hat focused on the everyday friction content professionals wanted to remove.

It also created a path from experimentation to useful capabilities: start with a clear problem, build something focused, and learn from how people use it.

And Content Science research backs up this approach. Even though

A Metadata Assistant That Gives Time Back

One of the team’s early tools was a metadata assistant. The assistant reads a page and suggests metadata tags, helping content professionals complete a task that can otherwise be repetitive and time-consuming.

The result: a process that can save approximately 20 to 30 minutes per page. That time savings matters, especially across large content ecosystems. But the value goes beyond efficiency.

Metadata supports how content is organized, discovered, and understood. Making that work easier can improve the foundation that both people and AI systems rely on.

The assistant also illustrates an important principle of AI adoption: a focused tool that solves a real problem can be more valuable than a broad AI initiative that lacks a clear purpose.

From Individual Tools to the Content Toolbox

As the team explored more opportunities, its work expanded beyond metadata. Red Hat developed the Content Toolbox, an internal resource designed to support content professionals across the company. The tools help teams understand content performance, identify opportunities, and make more informed decisions about what to create.

One capability analyzes similar content to help teams learn from what has performed well. The goal isn’t to duplicate successful content. It’s to understand the patterns and insights that can inform new work—while preserving the originality and expertise that make that work valuable.

This is an important distinction. AI can help teams identify what has worked before, but people still need to determine what is relevant, what customers need, and what new perspective the content should bring.

Structured Content Is the Foundation for Effective AI

As Red Hat’s AI capabilities have evolved, structured content has become increasingly important.

Barnes puts it simply:

If you have structured content, you have a gold mine.

Structured content makes information more consistent, reusable, and accessible across systems and experiences. It can also give AI more reliable inputs to work with.

Without structure, content can be difficult to interpret, connect, or evaluate at scale. With it, organizations can make content more useful for a range of purposes—from metadata and performance analysis to journey mapping and more advanced AI workflows.

Structure also creates opportunities for teams to work across traditional boundaries. Barnes describes collaboration with Red Hat’s documentation team, including shared thinking around jobs to be done and how content aligns with the customer journey.

When teams understand the needs content is meant to address, they can create more coherent experiences across marketing, product, and documentation.

Let AI Handle the Quantitative Work. Let People Focus on the Qualitative.

The conversation returns to a central question: How should work be divided between people and AI?

Jones captures the possibility:

You’ve been really seeing the opportunity to leverage AI to help with the quantitative side and free up your teams to do more on the qualitative side.

AI can help teams analyze performance, identify patterns, suggest metadata, and surface information at scale. People can use the time that creates to interview subject matter experts, bring forward original perspectives, and make content more compelling and differentiated.

That shift is especially important for enterprise content. Organizations have expertise distributed across their teams—people who understand products, customers, industries, and complex problems.

The challenge is often making that expertise visible and useful to customers. AI can support the process of finding and organizing information. But human expertise is what turns that information into a perspective customers can trust and act on.

Map Content to Customer Needs and Business Outcomes

Barnes also discusses how content can be mapped to buyer journeys, nurture tracks, blueprints, and jobs to be done. This helps teams move beyond producing content for its own sake.

Instead, they can ask:

• What is the customer trying to accomplish?
• What information do they need at this stage?
• What job is this content intended to help them complete?
• How does the content connect to the next step in the journey?
• What signals will tell us whether it is working?

This perspective can help content teams align their work more closely with customer needs and business actions.

It also creates a stronger foundation for AI-enabled analysis. When content has a clear purpose and is structured appropriately, teams can more effectively evaluate its role in the customer experience.

Moving Toward Agentic AI—With Guardrails

Red Hat’s work is also moving beyond individual AI assistants toward more advanced capabilities. As tools become more connected, AI can begin to support larger workflows and richer evaluations of content quality and findability.

But greater capability also raises questions about autonomy.

What should an agent be allowed to do? What does good output look like? Where should a person review the work? How can teams ensure that an AI-enabled workflow stays aligned with business goals and customer needs?

Barnes and Jones discuss defining expectations and maintaining human checkpoints. AI systems need clear direction and criteria for success. Teams need to understand where the technology can work independently and where human judgment remains essential.

The goal is not to automate every step. It is to build workflows in which AI and people contribute where they are most effective.

Content Is a Revenue Stream—and a Way to Scale Expertise

One of the conversation’s broader ideas is that content can be more than a supporting function. Content can help customers understand products, solve problems, make decisions, and get work done. It can also make the expertise inside an organization more accessible.

Barnes sees an opportunity to scale the best ideas and voices within a company—helping subject matter experts contribute to content that reaches more customers.

That doesn’t mean replacing those experts with AI-generated material. It means making it easier for their knowledge, experience, and perspective to become part of useful content. As AI makes content production more efficient, the differentiating value may increasingly come from the people and expertise behind the content.

The Future Is a Thoughtful Mix of Human and AI Contribution

What will content teams look like five years from now?

Barnes and Jones talk about a future in which the balance between human and AI contribution continues to evolve. The right mix will depend on the work, the content, the risks, and the value people bring.

Some tasks may become largely automated. Others will continue to depend on human judgment, creativity, expertise, and the ability to understand what customers really need.

The challenge for content leaders is to design teams and workflows that make the most of both.

AI can make content operations faster and more informed. Structured content can create a stronger foundation for AI. And human expertise can ensure that content remains compelling, authentic, and differentiated.

Explore the Episode

Watch episode 3 of Content Powers AI to hear Colleen Jones and Laura Barnes discuss automation, structured content, AI-enabled workflows, and how content teams can preserve authenticity while adopting new technology.

Episode Highlights

• 00:00 — What Content Teams Are Really Trying to Balance: Compelling Work, Authenticity, and AI
• 01:52 — Behind the Hype: Start With Automation, Not Content Generation
• 03:53 — Beginning With Repetitive Tasks and the Team’s Early AI Ideation Process
• 05:17 — Building and Testing the Metadata Assistant to Save Time on Every Page
• 07:49 — Creating the Content Toolbox for Content Professionals Across the Company
• 09:18 — Using Similar Content Analysis to Guide New Content Without Duplicating It
• 10:33 — Why Structured Content Is the Foundation for Effective AI
• 11:30 — Shifting Human Effort Toward Interviews, Expertise, and Differentiation
• 17:05 — Mapping Content to Stages, Blueprints, and Jobs to Be Done
• 18:32 — Collaboration With the Docs Team and Shared Jobs to Be Done Thinking
• 20:24 — Where Agentic AI Fits and How Red Hat Is Approaching It Safely
• 22:28 — Defining Good Content With Data, Benchmarks, and Performance Targets
• 25:27 — Creating a Wish List for Agentic Capabilities and Defining Team Skills
• 28:56 — Why Content Is a Revenue Stream and a Way to Scale Internal Expertise
• 32:42 — Five Years Out: Finding the Right Percentage of Human and AI Contribution
• 34:08 — The Gold Mine of Structured Content Plus the Human Job of Being Compelling and Differentiated
• 36:57 — Timely Takeaways

Related Resources

• Report: What Makes Content Operations Successful in the Age of AI?
• Content Science Email Newsletter

Content Powers AI is a podcast from Content Science, hosted by Colleen Jones. The show explores what organizations need to make AI work—from content strategy and operations to governance, people, and business transformation.