Who owns LLMO and AIO: marketing, PR, development, and sales

Even after you start AI search optimization (LLMO, AIO, AISEO, GEO), work can stall when it’s unclear what to ask of marketing, PR, development, and sales. Before you add more owners, clarify what each team hands off—and who makes the final call.

The usual setup: marketing owns planning and oversight, PR handles external mentions, development owns the technical foundation, sales brings customer questions, and one operations lead keeps the whole loop moving.Below we spell out each team’s scope, KPIs, RACI, how small teams share roles, and where to draw the line between outsourcing and in-house work.

 

How ownership splits across teams

LLMO and AIO need answer-ready content, third-party mentions, a technical foundation AI can retrieve, and the language customers actually use. No single team can gather all of that—so you connect the four teams with one shared operating flow.

Why multiple teams have to collaborate

When AI generates answers, it doesn’t only use your site—it also pulls from external media, review sites, and public institutions. Improving your site alone isn’t enough; you need both owned and external information in good shape.

 

What AI search tends to reward

AI search weighs semantic fit between the question and your information—and whether the content matches intent. Methods differ by service, but clear answers matter more than keyword matches alone.

You need the link between sales (who hear the real questions) and marketing (who turn them into content). Tech alone or content alone won’t get you the full picture.

Four steps every team shares

Whatever the team, the work follows the same four steps. Fit each team’s tasks into this flow and handoffs—and the next owner—become obvious.

1. Collect customer questions. Sales and customer support own this.

2. Publish the information. Marketing and PR own this.

3. Make it technically retrievable. Development and IT own this.

4. Measure AI exposure and business outcomes. The LLMO operations lead owns this.

If any step is missing, the whole program stalls. Skip collecting customer questions and you’ll ship generic articles AI struggles to evaluate. If priorities are fuzzy, see practical steps for deciding what to prioritize.

What marketing, PR, development, and sales each own

Split ownership so each team can run it as an extension of day-to-day work. Connecting existing deal notes, PR activity, development tickets, and content production beats inventing a pile of new dedicated tasks.

Marketing owns prompts and content

Marketing owns which AI search questions (prompts) you target and the content that answers them. Pick prompts by stage of consideration—comparison, evaluation, or right before purchase.

They take existing deal notes and loss reasons from sales and turn customer phrasing into candidate search prompts.

Main tasks:

• List and prioritize target prompts

• Organize KBFs as evaluation criteria for AI

• Produce and update FAQs, comparison pages, and case studies

• Plan original research with PR

• Spec structured-data requirements and request development

PR grows external mentions and citations

PR grows mentions and citations beyond your own site. Ask AI something like “Which agencies are often recommended in our industry?” list the domains that show as sources, and use that list to decide which media to pitch.

Concrete work includes:

• Bylines and interviews in industry media

• Regular press releases

• Links from public institutions

• Social outreach

• Publishing original research

Research that reflects your USP is easier for external media to cite. Marketing helps plan the study; PR owns external distribution and placement.

Development builds the retrieval foundation

Development and IT prepare the technical foundation so AI search and crawlers can retrieve your information.

Structured data isn’t mandatory for AI search, but implement types that match what’s on the page—Organization, Product/Service, Article, BreadcrumbList—when it helps.

Check whether OAI-SearchBot is blocked in robots.txt. AI crawlers can also get blocked by a CDN or WAF, so confirm with infrastructure.

 

Development’s core job: keep sitemaps and structured data in place so crawlers can retrieve information.

Sales shares customer language as-is

Sales provides the language customers actually use. Don’t ask them to rewrite questions—share existing deal notes, CRM, inquiries, and sales materials as-is.

Asking sales to create new materials is a common stall point—so pull the following from data you already have:

• Questions that come up in deals

• Competitors or services buyers compare

• Pricing, features, proof, and support scope buyers care about

• What decides wins and losses

• Pre-purchase concerns and blockers in internal approval

Sort collected questions into five stages—information gathering, competitor comparison, specifying conditions, easing concerns, and decision-making—then turn them into natural AI search prompts.

Example: if deals often ask how you differ from Competitor A, you can design prompts like:

• Which services are easy for SMBs to implement?

• Compare options including pricing and support.

• Which companies still support you after go-live?

• Which services fit teams without specialists?

Prioritize prompts by question frequency, impact on wins/losses, and fit with the customers you want to win.

Split execution and accountability with RACI

Map major tasks to RACI and it’s clear who executes versus who makes the final call.

 

Keep one Accountable person per task. Multiple Accountables muddy the final decision.

From setup to measuring outcomes

Build the system in three steps: name owners, set the information-sharing flow, then measure and improve monthly. Locking the operations lead and KPIs in month one is what makes the program stick.

Step 1: Name the operations lead and each team’s contact

Start with the marketing manager as the first candidate for operations lead—someone who can approve site updates, open development tickets, and see business KPIs. If you name a contact without request authority, work with development and sales will stall.

That person should meet these three conditions:

• Can approve content updates on the company site

• Can open tickets for development

• Can access business KPI numbers

In one kickoff meeting, decide these four things:

• Operations lead (Accountable)

• Each team’s contact (Responsible)

• AI services to measure (ChatGPT, Gemini, AI Overviews, Perplexity, etc.)

• Monthly meeting date and attendees

Kickoff materials should include real measurements of how AI search treats you today. Screens where only competitors are recommended make the gap shared across teams. Startup steps are covered in the five-step practical guide to LLMO implementation.

Step 2: Turn customer questions into prompts

Sales shares existing deal notes; marketing turns them into search prompts. Don’t add new input chores for sales—use the monthly check to fill gaps or clear misunderstandings.

 

In research, check mentions of you and competitors, recommendation order, citation sources, comparison axes, and misinformation. Feed the results into service pages, comparison pages, FAQs, case studies, and sales materials.

If you find misinformation, marketing and development fix the source on your site first. When you need an external statement, PR owns distribution.

Step 3: Measure AI exposure and business outcomes separately

Split KPIs into two layers: AI exposure metrics (marketing reports) and business outcome metrics (operations lead reports). Exposure alone won’t show business impact—or support budget decisions.

Citation rate: how often your site shows as a source. Mention rate: how often your brand or service name appears in the answer text. Recommendation rate: how often you’re presented as a recommendation.

Citations, mentions, and recommendations are separate. A source link doesn’t mean you’re recommended in the copy. Track all three so you know what to fix.

 

Keep the monthly meeting to 60 minutes with a fixed agenda: last month’s three-metric trends, any misinformation, pages to improve this month, and cross-team requests. Tag tasks “LLMO” in tools you already use—Jira, Backlog, or Notion—so you’re not inventing a parallel process.

In small teams, combine marketing and PR

In small organizations, one person often covers marketing and PR, with development and sales joining when needed. The cadences below are examples—tune them to deal volume, publishing capacity, and development load.

When one person wears both hats, prioritize like this:

1. Collect questions from sales (or run a 30-minute monthly interview).

2. Update FAQs and comparison pages—aim for two updates a month.

3. Do the first structured-data pass up front; after that, ship it when new pages go live.

4. External outreach—aim for one press release a quarter.

What to keep in-house vs. outsource

Keep collecting customer questions and judging business outcomes in-house. Raw customer language only lives inside your company, and budget/priority calls can’t be outsourced.

 

If you’re weighing outsourcing cost, also see what LLMO work usually costs.

Common ownership failures—and how to fix them

If one team carries the load, the program dies on a transfer or a busy quarter. One-way information dumps also fail—each team needs improvement results they can use on the next cycle.

Parking everything in SEO

When SEO owns everything alone, work skews to on-site fixes and third-party mentions get ignored. AI search also uses external sources, not just your site.

Typical symptoms:

• Article count rises, but AI answers mostly recommend competitors.

• Misinformation sticks in AI answers with no correction path.

• No external placements, so citations never accumulate.

Fix: add industry-media placements to PR’s quarterly KPIs. It overlaps existing PR work, so the extra load stays manageable.

Dumping everything on development

Structured data and crawler settings are foundational, and they alone will not generate content that can be cited by AI. Even if they are set up to be retrievable, if there is no content to be retrieved, it will not be adopted in responses.

When you request development, include these three points:

• What to implement (e.g., Article or BreadcrumbList schema in JSON-LD on target pages).

• Which pages (list target URLs).

• Done looks like: syntax and on-page content pass Schema Markup Validator (or equivalent).

“Please handle LLMO” isn’t a request development can act on. Marketing breaks requirements into concrete tickets.

Only taking input from sales

Don’t ask sales to organize or summarize—have them share existing CRM records and deal notes.

To keep the loop going:

• Limit sales asks to access for existing data.

• Run a 30-minute monthly interview to capture recent questions verbally.

• Send AI-answer measurement results back to sales for competitive talk tracks.

One-way intake from sales doesn’t show them the upside. Close the loop by returning measurement results.

Leaving leadership out

Without a director-level (or higher) sponsor, cross-team work stalls on authority conflicts. Name one executive sponsor at kickoff and share monthly meeting notes.

Report two things to leadership: trends in citation, mention, and recommendation rates, plus AI-influenced inquiries. Skip deep technical implementation detail.

What Queue can support

If you lack LLM engineering depth and want to hand off implementation decisions, pick a partner that can stay with you from diagnosis through improvement.

As of September 22, 2026, Queue’s umoren.ai lists more than 100 cumulative implementing companies, an average AI citation improvement rate of +460%, and 98% customer satisfaction on its official site. The figure that AI-driven traffic has about 4.4× the CVR of traditional SEO comes from an overseas Semrush survey published in 2025. Implementing companies include CyberBuzz, KINUJO, Peach Aviation, RENATUS ROBOTICS, and Eco Pro Coat.

Sales provide customer language; Queue organizes questions, designs prompts, analyzes AI answers, and sets improvement priorities. We list 50+ question patterns, run them through AI, and record citation status.

Support runs a four-cycle loop: Diagnosis → Design → Improvement → Monitoring.

 

Queue runs a large-scale survey called QFO (Query Fan-out) based on usage data from its free tools, gathering first-party technical data on LLMs. We apply the same research design that earns external citations to client work as well.

You’re a fit if your name doesn’t show in AI search (ChatGPT and similar), you’re misrepresented, or only competitors get recommended—and you want support that includes technical implementation. If you lack first-party information or customer feedback, results will take longer.

For a fuller view of support scope, see comprehensive LLMO support details.

FAQ: splitting ownership

Nine questions that come up when you split ownership in practice.

Q1 Do SMBs without a dedicated team still need ownership splits?

Yes—but you don’t need dedicated headcount. One marketer can also be the operations lead, pulling in development and sales on a monthly request cadence. Headcount matters less than naming one final decision-maker.

Q2 How do marketing and PR split ownership?

Marketing owns publishing on your domains; PR owns publishing outside them. FAQs, comparison pages, and case studies sit with marketing. Press releases, industry bylines, and external research distribution sit with PR. For misinformation, marketing and development fix the source on your site first; PR distributes when you need an external statement.

Q3 How do I brief development on the workload?

Break work into estimable tickets. Don’t say “AI search work”—say “Confirm OAI-SearchBot allow status in robots.txt” or “Implement Article/BreadcrumbList schema in JSON-LD on these URLs.” Include target, task, and done criteria. A realistic pattern is adding 1–2 tickets to existing sprints.

Q4 How do we turn customer language into content?

Sort questions into five stages—information gathering, competitor comparison, specifying conditions, easing concerns, and decision-making—and map each to a page type. Competitor questions → comparison pages; concerns → FAQs; decision-stage questions → case studies. Use customer wording in headings so intent is obvious.

Q5 How long until results show?

Crawler and structured-data changes can show in a few days or take months, depending on the AI service and crawl cadence. Improving citation, mention, and recommendation rates takes ongoing analysis and iteration.

Q6 Which team’s KPIs should track progress?

Give marketing citation, mention, and recommendation rates; PR external placements; development structured-data coverage; sales AI-influenced lead conversion. The operations lead owns AI-search-influenced inquiries overall. If one team holds every KPI, other teams stop owning the work.

Q7 How should we organize internally when we use an agency?

Keep an internal contact who can share customer questions and someone who can judge business outcomes. If deal notes, CRM, and inquiries are shareable, question organizing and prompt design can go to a partner. Sales should leave customer wording as-is—don’t make them rewrite questions.

Q8 Do we revisit ownership when algorithms change?

The four-team framework stays; measurement targets and priorities move. When new AI services spread, add them in the monthly meeting. When citation media shift, update PR targets. Revisit KPIs and measurement targets each quarter.

Q9 Should LLMO and SEO have separate owners?

Keep them on the same team. LLMO and AIO sit on technical SEO and content quality—separate owners create duplicate asks for the same page. On how LLMO relates to SEO, see LLMO goals and when to implement and the basic LLMO FAQ.

Takeaway

LLMO and AIO work best when marketing plans and oversees, PR owns external mentions, development owns the technical foundation, sales owns customer questions, and one operations lead runs RACI across the loop.

Keep monthly meetings to 60 minutes with four fixed agenda items, and report citation, mention, and recommendation rates next to business outcomes. Collect questions, publish, make content retrievable, and measure exposure and results in a shared language—and the program survives personnel changes.

Customer language from deals, research only you hold, and real implementation detail are first-party assets that live inside the company. Keep turning them into externally retrievable forms—that’s how you differentiate.

For a fuller program view, see the complete practical guide for companies. If you want help designing cross-team ownership, see umoren.ai’s AI search current-state analysis (LLM recognition analysis).