Quill in Action: How Chime, Bitly & Udemy Are Winning AI Search

Top 5 Takeaways

1. Agentic playbooks lowered the barrier to AI execution

Teams moved from workflow-building overhead to faster, more conversational experimentation. The shift let content teams focus on strategy and creativity instead of technical setup.

2. Faster iteration changed AI work from a project to an operating rhythm

Bitly framed experimentation as a daily habit instead of a quarterly roadmap item. This mindset shift turned AI search optimization into a continuous learning process rather than a one-time initiative.

3. Programmatic content for AI search can now scale much faster

Bitly showed how Quill plus content management system (CMS) integrations enabled high-volume publishing in hours. What used to take weeks of manual work now happens in a single afternoon.

4. AI-to-AI journeys are becoming a real search strategy

Prefilled ChatGPT prompts can extend engagement and reinforce brand relevance inside large language models (LLMs). This approach creates new touchpoints beyond traditional web pages.

5. Strong adoption combines a core owner with broader self-serve access

The most effective setups pair content engineers, brand guardrails, integrations, and human review. One person owns the system while the broader team contributes within clear boundaries.

AI search rewards teams that test, publish, and learn faster than the market. In a recent AirOps session, Tyler Roehmholdt (Bitly), Nicholas King (Udemy), and Bridget Nelson (Chime) joined Jordan Miller to show how they use Quill to speed up content production, tighten feedback loops, and uncover new AI search plays.
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The discussion focused on one clear shift: teams no longer need to spend most of their time building systems just to start experimenting. Instead, they can use agentic playbooks to move from idea to execution much faster.

Best Practices and Key Learnings

Speed alone doesn't win in AI search. The teams that pull ahead combine faster execution with stronger context, tighter guardrails, and clearer measurement.

Move from workflows to agentic playbooks

Teams in the webinar described the same shift from different angles: they wanted less setup work and more room to test ideas. Agentic playbooks gave them a more natural way to build, update, and refine AI-driven content operations.

Start with one repeatable use case that your team already understands, such as briefs, refreshes, or programmatic pages. Treat your brand kit and knowledge base as live inputs that your team updates as it learns. Use playbooks to cut down on system-building time so your team can spend more time improving outputs.

Build publish-ready programmatic content

Bitly showed that programmatic content only works when the output can move straight into publishing. Quill handled the research, page structure, formatting, and CMS handoff so the team could ship dozens of pages in hours instead of weeks.

Connect your CMS early so your team can move drafts into staging without manual copy and paste. Lock in page templates, formatting rules, and quality assurance checks before you scale output. Use programmatic pages for repeatable formats like integrations, competitors, and use cases.

[Insert image asset: A side-by-side visual showing the old content workflow versus the new agentic workflow. Include research, draft, QA, and publish stages, and show how Quill compresses weeks of work into hours across Bitly, Udemy, and Chime.]

Pair AI speed with differentiated expertise

Udemy focused on a different challenge: AI can draft fast, but it often misses the detail that makes content useful and credible. Their playbook pulled in intent analysis, instructor commentary, and stricter sourcing rules so the team could create sharper articles that still moved quickly.

Add first-party expertise, subject matter expert commentary, or proprietary data to every article where the topic demands depth. Audit intent before you draft—Udemy shared that 73% of its first-page search volume came from keywords with ambiguous intent. Set source rules that favor original research, fresher data, and reputable publishers over recycled blog citations.

Test AI-to-AI journeys and faster refresh loops

The webinar highlighted a newer idea: your page doesn't need to mark the end of the journey. Bitly used branded ChatGPT prompts to extend engagement, while Chime used refresh scouting to react faster when citations dropped.

Add AI-to-AI calls to action on high-intent pages where users want the next step, not just more reading. Run a weekly refresh scout that flags citation declines by topic, page, and competitor. Feed refresh recommendations into your existing review flow so legal, brand, or compliance teams can keep pace.

Give one owner the keys, then widen access

The strongest operating models in the webinar didn't give everyone full control on day one. They gave one person clear ownership, built the system around brand and workflow guardrails, and then opened access to more teammates.

Assign one content engineer or SEO lead to own playbooks, integrations, prompts, and output review. Bring in product marketing, engineering, brand, and compliance to maintain inputs and approvals. Expand self-serve access after your team locks in the brand kit, publishing flow, and review standards.

Close the loop between action and measurement

Chime's refresh scout and AirOps campaigns both pointed to the same next step: teams need tighter loops between visibility data and content action. When you can spot a citation drop, launch a refresh, and track the result in one system, you can make AI search a weekly habit instead of a slow reporting exercise.

Track citation rate, mention rate, and competitor movement at the page and topic level. Launch refresh campaigns from real visibility gaps instead of broad editorial hunches. Review results after each publish cycle so your team can keep the tactics that move visibility and cut the ones that stall.

How to Put These AI Search Insights Into Practice

AI search now favors teams that can shorten the gap between idea, output, and feedback. This webinar showed that agentic playbooks, direct CMS connections, and better measurement let teams move with that speed without losing brand control.

Start with one use case that already causes friction for your team, such as content refreshes, programmatic pages, or research-heavy briefs. Give one owner clear responsibility, connect the right systems, set review guardrails, and measure citations and mentions every week so your team can learn faster with each publish cycle.

Why Faster AI Search Teams Will Pull Ahead

The biggest winners in AI search won't just publish more—they'll test more ideas, learn from outcomes faster, and build systems that turn AI from a one-off project into an everyday habit.

Want to see how AirOps helps teams do that? Book a call.

How AirOps Helps Teams Win AI Search

AirOps gives content and SEO teams one place to build agentic playbooks, connect brand context, publish through existing systems, and track what changes performance. That means your team can move from idea to experiment to measurement without stitching together disconnected tools.

If you want to build faster refresh loops, scale programmatic AI search content, or test new AI-to-AI journeys, book a call.