In our latest Growth Leader Series webinar, Josh Spilker, head of Content & SEO at AirOps sat down with Alex Birkett and Allie Konchar, co-founders of Omniscient Digital,  to explore what AI really means for the future of content teams.

The conversation reflected on the State of Content Teams Report and revealed both tactical takeaways and a strategic shift: the rise of the 10x content engineer, a new type of content leader fluent in editorial judgment and AI-powered systems.

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Top 5 Takeaways

1. The AI Adoption Spectrum
   Teams are at various stages of AI integration, from early experimentation to full implementation. This diversity in adoption rates presents both challenges and opportunities for organizations.
2. Evolution of Content Team Structures
   The emergence of roles like content engineers signals a fundamental shift in how teams operate. This evolution is crucial for maximizing AI's potential while maintaining content quality.
3. Quality Assurance in the AI Era
   Human oversight remains paramount. Teams must develop robust QA processes to ensure AI-assisted content meets brand standards and audience expectations.
4. Efficiency Gains in Research and Ideation
   AI tools are dramatically reducing time spent on initial content development stages, allowing teams to focus on strategic initiatives and distribution.
5. Future Content Marketing Landscape
   Success will increasingly depend on unique data utilization, distinctive brand voice, and effective cross-team collaboration.

Best Practices and Key Learnings

1. The AI Adoption Spectrum

AI is no longer a theoretical discussion. In our State of Content Teams 2025 report, we surveyed more than 140 content leaders to understand how teams are using AI today. Here’s what they told us:

• 32% are currently experimenting with AI
• 38% have partially integrated AI into their workflows
• 17% report full AI integration
• The rest report no adoption due to blockers like legal, governance, or risk concerns

As Josh Spilker put it during the webinar, teams are all across the maturity curve, and “that middle ground—between experimentation and full integration—is where most teams live, whether they realize it or not.”

Allie noted the biggest challenge isn’t technical, but operational. Teams must shift from skepticism to exploration and develop the maturity to make AI actionable.

“The best teams build SOPs that define how they use AI.” — Allie Konchar

Writers & Strategists Are Becoming Content Engineers & System Architects

Alex Birkett described how the most effective content professionals today are evolving into orchestrators, and blend editorial precision with systems thinking to scale quality through structured workflows.

This orchestration mindset is increasingly valuable as content teams expand their use of AI. Tools may handle research and drafting, but human judgment is required to set the direction, define outcomes, and ensure quality.

Rather than rely on automation alone, the most advanced teams are engineering repeatable systems that compound value over time. Read more about content engineering, what it means and how it impacts you.

Why Teams Need Structure, Not Just Tools

Teams often approach AI by experimenting with tools in isolation—content generators, keyword clustering apps, metadata optimizers—but without a unified system, those efforts stall out.

Teams like Descript have gains from implementing content refresh and optimization using AI workflows.

These gains don’t come from automation alone.

As Allie noted:

"It's more so with all these tools and all of these systems that we're building or working very hard to document--how is that helping us move faster without sacrificing quality? In terms of thinking about how quickly our team can move, how quickly our team can iterate and like iterate on prompts and outputs, and then how we can reallocate time towards more of the the manual tasks or the things that should be manual, so, spending time with SMEs and customers, spending time with clients the stuff that we never really want to automate or outsource to an AI tool and prioritizing those things by becoming more efficient on the AI front."

Where AI Creates the Most Value

"The generative component of AI is actually one of the least interesting." -- Alex Birkett‍

Rather than focusing exclusively on generative output, the conversation consistently returned to research, synthesis, and ideation as the most valuable AI use cases.

Some examples discussed included:

• Turning transcripts, sales calls, and product docs into topic maps
• Using AirOps workflows to create content briefs that include brand positioning, product differentiation, and SME insights
• Automating keyword clustering and internal linking suggestions at scale
• Editorial evaluations that check drafts against brand guidelines, structure, and factual accuracy

Alex pointed out that even editorial QA can now be supported by AI. One team member at Omniscient, for example, uses Perplexity to verify facts and formatting before publication, which dramatically speeds up internal review.

What About Metrics?

As teams shift to AI-assisted workflows, their KPIs need to evolve as well. One of the biggest conversations centered on how to measure success in this new environment.

Alex broke it down into three layers:

1. Outcome metrics: Leads, pipeline, revenue
2. Attribution inputs: Self-reported attribution (“How did you find us?”), referral traffic from LLMs
3. Visibility tracking: Tools like Peak and Profound that estimate where your brand appears in AI-generated summaries

Josh added that AI platforms often surface bottom- and mid-funnel content—such as comparisons and product-focused articles—more than broad ‘what is’ pages. This makes it even more important to have a unique POV, fresh research, and a clear brand voice.

Quality Still Matters

There was wide agreement that human oversight is non-negotiable. While AI can draft, teams need processes in place to ensure outputs meet brand and editorial standards. The result is a hybrid approach where humans define standards, structure the work, and QA the outputs—allowing AI to drive velocity without undermining trust.

The Future of Content Ops Is Iterative

The best teams treat every prompt, output, and campaign as a learning opportunity. “(AI) is capable of so much, and you'll only really unearth all of that if you keep kind of going back to the drawing board and also pulling on all of your team's knowledge and creativity," Allie said.

Final Thoughts

If content marketing in the past was about writing, the future is about building. Systems, not tools. Strategic workflows, not just output. And a new breed of content marketer who knows how to orchestrate it all.

AI is accelerating the pace of content creation. But it’s the teams who embed AI into structured, repeatable workflows with a strong editorial POV that will stand out.

Want to see how AirOps helps teams do exactly that? Book a demo or email us at team@airops.com.

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