Content Powers AI

This conversation explores the relationship between enterprise content and scaling AI adoption. Colleen Jones, president of Content Science, is joined by Chuck Gahun, principal analyst at Forrester, who brings 25 years of experience advising enterprises across content, commerce, digital experience, and technology strategy.

We discuss why AI adoption is moving more slowly in practice than headlines suggest, why content is becoming more valuable to organizations seeking value from AI, and how businesses need to rethink workflows, guardrails, and publishing cadence for a machine-mediated world.

Key Topics

• AI adoption is still behind the hype, with many organizations struggling to scale pilots and experiments correctly.
• Content is no longer just customer-facing, because machine consumers and answer engines are now interpreting published content and generating responses from it.
• The value of content is shifting from deterministic site experiences to ephemeral, machine-generated experiences.
• Publishing cadence is becoming a competitive factor, especially for product detail pages, B2B applications, and procurement content.
• Organizations are increasingly standing up content operations and strategy initiatives, though they may call them content programs or cleanup exercises.
• Content tech is evolving toward an agentic layer that sits above CMS, DAM, PIM, and commerce systems.
• Successful organizations do not just bolt agentic AI onto old workflows, they redesign work by separating human tasks from agent tasks.
• Policies and guardrails matter as much as the technology, especially in regulated industries where autonomy must be limited.
• B2B adoption of agentic AI is moving faster than B2C because structured content and procurement motions are easier for agents to handle.
• Human-authored, authentic content may become more valuable as AI-generated content becomes more common.

Resource Links

Introducing Content IQ, AI Superagent

https://review.content-science.com/introducing-superagent-contentiq-webinar-recording/

Research Report: What Makes Content Operations Successful in the Age of AI

https://content-science.com/content-operations-study/

Timestamps

00:00 - Why AI adoption is slower than the headlines

01:22 - Behind the hype: More perspective on the role of content in adoption

04:46 - AI agents as new customers and consumers of content

06:12 - Why publishing cadence is becoming a competitive advantage

09:51 - Published content is no longer the last mile

11:18 - Why more companies are really building content strategy programs

13:13 - The rise of content tech built on agentic AI

19:44 - What separates successful AI content programs

20:40 - Redesigning workflows instead of bolting AI on top

23:26 - Why B2B agentic commerce may move faster than B2C

25:48 - Human demand for authentic content may increase

28:17 - Agentic channels as a new route to customers

29:29 - Related resource - ContentIQ

31:06 - Five years out: Cost and regulation issues must be resolved

34:56 - Practical advice for leaders navigating AI change

36:20 - Timely takeaways

Notable Quotes

Gahun: “The level of adoption of AI, agentic AI or even generative AI, is not at the clip that I think maybe some of the big tech leaders are making us believe it is.”

Gahun: “The published content is not the last mile anymore.”

Jones: “There's now a machine layer between what organizations publish and what people actually experience. And that changes everything about how we think about content strategy and operations.”

This conversation breaks down how enterprises can make progress in adopting AI without losing the human qualities that make content compelling, authentic, and differentiated.

Colleen Jones, president of Content Science, talks with Laura Barnes, senior director at Red Hat, about starting with automation, building structured content foundations, and using AI to free up content professionals for higher-value work.

Laura shares how her team has moved from early AI experiments into practical internal tools for metadata, content performance analysis, journey mapping, and more advanced orchestration across marketing and documentation. The big idea throughout: AI can handle the repeatable and quantitative work so humans can focus on judgment, voice, expertise, and customer trust.

Key Topics

• Why many teams get AI adoption backwards and should start with automation and structured content before trying to create with AI.
• How Laura’s team began by asking what content work people wanted to stop doing, then used short six-week sprints to test ideas and vote on what to build.
• A metadata assistant, which reads a page and suggests metadata tags, saving about 20 to 30 minutes per page.
• How AI can help identify similar high-performing content so teams can learn from what works without duplicating the content itself.
• The shift from simple automation toward more agentic capabilities, with tools starting to work together and provide richer evaluations of content quality and findability.
• Mapping content to buyer journeys, nurture tracks, blueprints, and jobs to be done so content aligns more closely with customer needs and business actions.
• Cross-team collaboration with the docs team, including shared thinking around jobs to be done and content alignment across the customer journey.
• The balance between human expertise and AI efficiency, especially the need for subject matter experts to be more visible in content.
• Content as a revenue stream and a way to scale the best ideas and voices inside a company to help customers get things done.

Resource Links

Content Science Review (Magazine)

http://review.content-science.com

Content Science Email Newsletter

https://content-science.com/cs-newsletter/

Timestamps

(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

Notable Quotes

Barnes: "Automation is really the training wheels of AI."

Barnes: "If you have structured content, you have a gold mine."

Jones: “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 for an organization is only as effective as the content and knowledge it can access, trust, and act on. In this launch episode, Colleen Jones explains why content strategy, operations, and governance are essential to moving enterprise AI beyond demos and pilots—and into real organizations.

As president of Content Science and author of The Content Advantage, Colleen introduces the idea behind the podcast Content Powers AI. She shares research on the gap between organizations using AI and those making progress scaling it. Colleen also previews conversations with enterprise leaders, industry analysts and AI engineers with organizations such as Forrester, Red Hat, The Home Depot, Mayo Clinic, and more.

Key Topics

• Why content is more than web pages and marketing copy—it also includes product information, policies, research, data, metadata, and institutional memory.
• Why AI transformation is slow.
• Content Science research findings: 86% of organizations report using AI in some way, while only 29% report moderate or fast progress in scaling adoption.
• How content, knowledge, people, systems, workflows, governance, and business goals work together to support AI.
• Why content strategy can no longer be separated from AI strategy.
• How people, organizational content, and AI form a continuous feedback loop.
• The topics and expert perspectives the podcast will explore, including strategy, architecture, governance, operations, customer experience, and knowledge.
• Why it matters to separate AI hype from what works in real organizations.

Notable Quotes

Jones: “AI is only as useful as the knowledge it can access, understand, trust, and act on.”

Jones: “Content strategy can no longer be separated from AI strategy.”

Jones: “Less than 10% of business and technology podcasts have women as hosts, and less than 10% have women as guests.”

Jones: “AI transformation isn’t just a technology challenge, It’s a content challenge, a knowledge challenge, a governance challenge, an operating model challenge, and, ultimately, it’s a business challenge.”

Resource Links

What Makes Content Operations Successful in the Age of AI?

Research Report

https://content-science.com/content-operations-study/

The Content Advantage (3rd edition)

https://content-science.com/publications/content-advantage/

The AI Strategy Gap

https://review.content-science.com/the-ai-strategy-gap-what-it-is-how-to-start-closing-it/