Your AI visibility dashboard is live. Now what? A 90-day plan

Once your AI visibility dashboard is live, turn every finding into an owned task within 30 days. Sort findings into four queues (accuracy, technical, on-site content, off-site authority), name an owner for each, and freeze a baseline. Ship fixes in days 31–60. In days 61–90, re-measure on the same prompts and report shipped changes alongside the score. The plan works with Profound, Semrush, Ahrefs, Peec AI or any other tracker.

01Why do dashboards stall after launch?

Most teams buy the dashboard to answer "how visible are we?" Then the harder question arrives: "who fixes this?" McKinsey found only 16% of brands systematically track AI search performance,^[1] so many teams are building the process at the same time as reading the data.

The usual pattern is predictable. The first report goes to leadership, the score gets discussed, and the opportunity list sits untouched because it crosses SEO, content, PR, product marketing and regional teams. No single owner, no shipped change.

02Days 1–30: how do you triage what the dashboard shows?

Export everything your tool flags: wrong facts, missing topics, uncited pages, competitor-cited sources, crawler issues, negative sentiment. Then sort each item into one of four queues. Each queue moves at a different speed and needs a different owner.

The off-site queue tends to be the biggest. McKinsey found a brand's own website makes up only 5–10% of the sources AI search references,^[1] and Ahrefs found branded web mentions correlated with AI Overview visibility at 0.664, against 0.218 for backlinks.^[2] If nobody owns that queue, the dashboard will keep showing the same gaps.

03How do you set a baseline you can trust?

Before changing anything, freeze three things for the quarter: the prompt set, the engines and markets, and the competitor list. Export the starting data with a date and version.

AI answers vary between runs. A SparkToro and Gumshoe study found the odds of an identical brand recommendation list appearing twice were under 1 in 100.^[3] Use your tool's repeated runs, average over at least two weeks, and treat single-day changes as noise.

04Which dashboard numbers should you watch, and which can you ignore?

Every tracker shows more metrics than a team can act on. Watch the numbers that point to a specific task, and treat the rest as context.

Worth watching closely:

• Wrong or outdated facts about your company. Each one is a task.
• Cited sources for your priority prompts, yours and competitors'. These tell you where the work is.
• Share of answer on a fixed prompt set, by topic and market. This is the trend leadership cares about.

Useful context, but rarely actionable on its own:

• Average position in a list. It changes too much between runs to manage directly.
• A single composite score. Vendors calculate these differently, and some do not publish the method. Use it for direction, not targets.
• Total mentions across thousands of generic prompts. Large numbers feel good but often include prompts no buyer would type.

If your tool offers many views, pick three or four for the weekly and monthly routine and stop there.

05How does this change across markets and languages?

AI answers differ by country and language, because the engines draw on different local sources. A brand can be well described in English and wrong or missing in Arabic, German or Japanese. Treat each priority market as its own baseline, with its own prompt set written by someone who speaks the language natively.

The four queues still apply, but owners change. Local teams usually know which regional directories, publications and forums matter, and they often hold approval rights for local wording. Give them a clear role in the plan from day one rather than sending them findings later. Our guide to Arabic AI search in the GCC shows how different one market can look.

06Who owns what?

Assign owners before the first fix ships. A simple responsibility table prevents most stalls:

If one person's name fills the whole Responsible column, you have found your bottleneck. That is the point to consider done-for-you help for part of the work.

07Days 31–60: what should ship first?

Ship in this order: accuracy, technical, on-site, off-site. Accuracy fixes are the easiest to verify and the most damaging to leave. Technical fixes unblock everything else. On-site content and off-site authority run in parallel after that, because off-site work takes longer to land.

1. 1
   Accuracy sprintWeeks 5–6

   Correct the top wrong facts at their source, on your site and at third parties. Log each change.
2. 2
   Technical clean-upWeeks 5–7

   Fix crawler access, rendering and structured data on pages the dashboard shows are cited or should be.
3. 3
   Content batchWeeks 6–8

   Rewrite five to ten priority pages so the direct answer comes first, with specific facts.
4. 4
   Authority pipelineWeeks 6 onward

   Start on the ten to twenty domains that cite competitors most often: profiles, reviews, pitches, community answers.

Hold a weekly 30-minute ship meeting. The agenda is the change log: what shipped, what is waiting for approval, what is blocked and who unblocks it. Strategy belongs in the monthly review, not here. Our guide to AI visibility governance covers approval rules for multi-market and regulated teams.

08Days 61–90: how do you measure and report progress?

Re-run the frozen prompt set and compare with the baseline. Read results in order of how directly they connect to your work:

1. 1Accuracy: are the specific wrong claims you fixed still appearing?
2. 2Citations: are your updated pages and new off-site placements now cited?
3. 3Share of answer: how often you appear across runs, by topic, engine and market.
4. 4Sentiment and framing: does the description match your positioning?

Report to leadership with the change log next to the numbers. A one-page format works well:

If the score has not moved, read why your AI visibility score is low before changing strategy. Often the cause is that fixes have not shipped yet, or that off-site work has not had time to land.

09What changes after the first 90 days?

The second quarter is about scaling what worked. Expand the prompt set, add markets or languages, and automate routine content work if your tool supports it. Keep the weekly ship meeting; it is the part teams drop first and miss most.

Also review staffing honestly. If the change log shows most items waiting on one person or one approver, adding prompts will only lengthen the queue. Fix the capacity first, then grow the scope.

This is the gap OwnlyReal is designed for: an operating team that is tool-agnostic, works from your dashboard, and ships through your CMS, ticketing and sign-off chain. The why section explains the reasoning.