AI agents for marketing operations: How to run AI search reporting

AI search has become a new line in the marketing plan, and its numbers probably ended up on your dashboard. Visibility, citations, sentiment, and AI-referred traffic now sit next to the metrics you already report, but they come from different systems and behave differently.

They also hide much of their own impact. When we studied more than 2 million AI conversations and the browsing that followed, we found that people visited a brand's website 1.5 to 2.5 times more often in the week after an AI answer mentioned it. More than 97% of those visits from ChatGPT users carried no AI referral tag, so your analytics credits most of that influence to search, direct traffic, or whatever channel the visitor came through.

All in all, AI search is a hard channel to report on accurately. The data has to be connected across systems, checked before it reaches leadership, and tied to the work that moved it. That's ops work, even when an AEO or SEO team runs the program.

The AEO platform that team uses is where most of the data starts, and the best of these platforms now offer agents that take on the manual reporting work. Profound runs its Agents on the data it collects: more than 2 billion real user prompts, the pages each engine cites in reply, how each engine describes you and your competitors, and the AI crawler and referral traffic on your site.

You can build your own agents in a drag-and-drop builder or start from Profound's library of agent templates. In this article, we walk through five jobs marketing ops takes on as AI search grows, and the agents that help with each.

Connect the data before you report on it

None of the reports below work until the sources are connected, and AI search adds sources many ops teams haven't wired up before. Visibility and citation data live in your AEO platform. AI crawler activity lives in your server or CDN logs. AI-referred visits and conversions live in your analytics, and pipeline lives in your CRM. Profound brings it all together:

Agent Analytics reads your server logs to show how AI bots use your site, split into visits to cite your content in an answer, to index it for search, and to collect it for model training. It also shows human visitors who arrive from an answer engine, by source. Setup runs through the systems that already hold your logs, including Cloudflare, Akamai, Amazon CloudFront, Fastly, Google Cloud CDN, Netlify, Vercel, and WordPress.
The Google Analytics integration lets Agent Analytics import conversion and revenue metrics from your GA4 property, so AI activity sits next to business outcomes. Profound also integrates with Adobe Analytics and Google Search Console to put traditional and AI search side by side.
The Looker integration merges Profound's data with the business data you already model there, which is where pipeline and revenue usually come in.

Before the first report goes out, agree on definitions with the teams that will read it. Decide which prompts and engines are in scope, which tags group them into topics or markets, and what counts as an AI-referred conversion. Those choices are easy to make once but painful to change mid-year because every change breaks comparisons with past periods.

If you're setting this up for the first time, chapter four of our AEO Guide walks through a first audit that covers AI traffic alongside visibility and citations.

Omnilux, which sells LED skincare through a Shopify store, spent a couple of weeks going back and forth with its developers and Cloudflare trying to set up AI traffic tracking, because Shopify doesn't give merchants access to server logs. Through Profound's connection with Nostra, which runs a Cloudflare-powered proxy in front of Shopify stores, the team turned on Agent Analytics in 98 seconds. AI-attributed revenue has since tripled its share, to about 3% of Omnilux's total revenue.

Send the weekly update without building it

A useful weekly AI search update covers visibility and share of voice by engine, how you compare with competitors, AI crawler and referral traffic, and a short note on what changed since last week and why. The first three come from the same sources every week, so an agent can pull them and draft the note. That leaves you the explanation, like whether a dip on one engine is worth raising and what it means for the plan. Two kinds of agents handle the pulling:

The Weekly Brand Health Report and Monthly Brand Health Report agents pull your visibility scores and share of voice from Profound and write them up as an overview you edit before it goes out.
Agent Analytics nodes bring crawler and referral data into any agent. Profound's own example is an agent that runs every Monday and pulls the prior week's bot visits, broken down by bot type. Add the Human Referrals node and the same update shows which answer engines sent people to your site.

Profound connects to Slack and Microsoft Teams for reporting and alerts, so the update can land where your team already reads things. And if you report on several brands, markets, or regions, you can run the same agent in a spreadsheet with one row for each.

Arizona College of Nursing tracks more than 300 prompts across 24 local markets, organized with custom tags. "After an agent is built, we run it per market and generate the insights we need on demand," says Francis Dayton, Web Product Manager. Profound Agents save the team 20 hours a month, partly by automating its weekly AEO reporting.

Check the numbers before they reach the board

AI search metrics are novel enough that few teams know all the ways they break yet, and several of the usual traps look like real performance.

Engines change their answers on their own. When we tracked 15,155 brand setups daily on Google's AI surfaces, visibility on a single engine drifted 2 to 3 points a day without anyone touching anything. Adding prompts to your tracking mid-quarter changes what the score measures. A blended score can hold steady while one engine climbs and another falls. And as the mention study shows, AI's influence often lands in other channels' numbers.

There are three checks that help you catch most of it:

Read trends over a window. Compare weeks or a rolling average instead of single days.
Keep the prompt set stable between periods, or note exactly what changed.
Split results by engine before you explain a blended number.

You can build an agent that runs those checks for you. Have it pull visibility for the last several weeks, compare the latest period with the periods before it, and flag only the moves bigger than a threshold you set that hold for more than a week, with a short note on which engine and which topics drove each one. For traffic, Profound's Agent Analytics example chains an LLM step after the Bot Visits node to trigger an alert when crawler traffic spikes or drops, which catches technical problems before they show up as a visibility dip. When a number still looks off, you can ask AI Marketer (Aim), Profound's orchestrator, to work out the cause behind it (more on Aim below).

The GR0 agency chose Profound partly because it runs data continuously instead of in snapshots, which the team needed for accurate look-back reporting to clients.

Assemble the quarterly readout

A quarterly readout takes at least an afternoon, and longer once someone asks a follow-up question. For AI search, it also has to explain a channel that leadership may not fully understand yet.

Chapter six of our AEO Guide gives the readout a structure. It measures an AEO program in three layers (visibility, traffic attribution, and revenue impact) and separates channel-level goals, which track the program's overall direction, from campaign-level goals for specific initiatives. Each layer has its own source:

Visibility: the Monthly Brand Health Report for each month of the quarter, plus Answer Engine Insights set to the full quarter and compared with the one before, broken out by topic and engine.
Traffic attribution: Agent Analytics and your GA4 or Adobe Analytics data, with a note on the AI-influenced visits that arrive through other channels.
Revenue impact: Looker or your CRM, joined with the AI-referred conversions from Agent Analytics.

For the slides themselves, Profound integrates with Gamma to build presentations with Profound Agents, which gets you a first draft of the deck to edit rather than a blank one.

At One Identity, the SEO lead made average position in AI answers a core KPI, alongside visibility, citations, and sentiment, so leadership could see the quality of placements as well as their number. "I can walk into a QBR with Profound data and slides that the board understands," says Ali Sayyed, SEO Lead.

Keep the campaign tracker current

A campaign tracker is current on the day someone updates it. With AI search, work is spread across SEO, content, PR, and product marketing, and results show up weeks later in a different system, so the tracker quickly falls behind.

Aim's Projects keep the work and its results in one place. A project is a goal-based workspace. Aim lays out the opportunity, the data behind it, and the concrete tasks, and each task connects to a Profound agent that can carry it out. Projects show topics, status, and priority, and hold the artifacts each task produces. As the work ships, Aim tracks what was done and the impact each task had on the project's metric, so the results land next to the work that produced them.

If your team runs projects elsewhere, Profound integrates with Atlassian, Linear, and monday.com for project management, and with Notion to sync marketing initiatives to your team's knowledge base.

OpusClip's team uses Profound to show product, engineering, and marketing what's working. When the team works with engineering on a project like new tool pages, it can look in Profound and see what impact the work had. Signups from answer engines rose 37% (read the OpusClip story).

Run AI search reporting with your AI Marketer in Profound

Aim is the orchestrator inside Profound, a veritable marketing teammate that reads everything your team tracks there, including visibility, citations, sentiment, prompt volumes, FactCheck, Agent Analytics, and benchmarks against competitors.

For ops, there are three main use cases:

Projects keep the work and its results in one place. Every opportunity the team acts on becomes a project, a goal-based workspace where Aim lays out the opportunity, the data behind it, and the tasks, then builds the agent that carries out each one. Projects show topics, status, and priority, and hold what each task produces.
You can ask Aim about the data. When a number changes, Aim can work out the cause, whether that's one engine, one topic, or a competitor's gain, before someone asks you in a meeting.
Agents, docs, and sheets live in the same place. You can co-work with Aim on the agents that build your reports, the documents they produce, and the sheets that run them across markets.

If your team runs projects elsewhere, Profound integrates with Atlassian, Linear, and monday.com for project management, and with Notion to sync marketing initiatives to your team's knowledge base.

Ops connects the data and owns the reporting, so you have as much riding on the platform as the team that runs it. If your SEO or AEO team already uses Profound, ask for access. And if it's choosing or setting up an AEO platform, ask to be part of it, since the reporting will run through you.

See which parts of your AI search reporting Profound agents can run for you. Book a demo.

AI agents for marketing operations FAQs

What should an AI search report for leadership include?

Three layers: visibility, traffic attribution, and revenue impact. Visibility covers how often the engines mention and cite you, how they describe you, and how you compare with competitors. Traffic covers AI-referred visits and conversions. Revenue covers the pipeline and revenue you can tie back to them.

How do we connect AI search data to pipeline?

Through your analytics and your CRM. Agent Analytics measures visitors who arrive from answer engines and, when connected to GA4, imports your conversion and revenue metrics. From there, Looker or your CRM can join AI-referred conversions to pipeline, the same way you'd connect any other channel.

Why do AI visibility numbers move so much from day to day?

Because the engines change their answers on their own. The same prompt can return a different answer, with different brands and sources, from one day to the next. In Profound's tracking of 15,155 brand setups on Google's AI surfaces, visibility on a single engine drifted 2 to 3 points a day without any change on the brand's side.

That's why the trend matters more than any single reading. Report weekly or rolling averages, keep the tracked prompts the same between periods, and treat a move as real only when it holds for more than a week.

Which data sources does Profound connect to?

Most of the systems ops already runs. Agent Analytics connects through CDN and hosting logs, including Cloudflare, Akamai, Amazon CloudFront, Fastly, Google Cloud CDN, Netlify, Vercel, and WordPress, and through Nostra for Shopify stores. Analytics connections include Google Analytics, Adobe Analytics, and Google Search Console, and Looker brings in business data.

For delivery and tracking, there's Slack, Microsoft Teams, Gamma, Notion, Atlassian, Linear, and monday.com, plus content management systems and spreadsheets for running agents at scale. The full list is on Profound's integrations page.

Who should own AI search reporting?

Ops runs the reporting, and the AEO or SEO team owns the program. In practice, Ops owns the data connections, metric definitions, reporting cadence, and checks before anything reaches leadership. That split keeps the people closest to the work focused on the work, while the numbers stay consistent from one quarter to the next.