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Pharma AI Monitor
Case studies

Three teams. Three decisions changed.

How three pharma teams used Pharma AI Monitor to change what they said, what they fixed, and where they spent.

Pre-launchA launch team rewrote its brief before it spent on the campaign.ReadIn-marketAn in-market brand sharpened its media plan around the websites AI cites.ReadLabel updateAfter a new indication was approved, AI kept describing the old label.Read
Pre-launchSpecialty launchBrand, Medical, Insights, agency

A launch team rewrote its brief before it spent on the campaign.

The AI baseline showed the category did not yet understand the problem the new mechanism solves. The team reordered its story and redirected the agency.

The challenge

The launch plan led with a new mechanism. Creative territories and the channel plan were already in progress.

What Pharma AI Monitor showed

  • Established competitors owned the familiar practical benefits.
  • The new mechanism appeared with no explanation of why it mattered.
  • Questions about the burden of current treatment got generic answers.

The audience needed the problem explained before the mechanism could land.

What the team changed

  1. 1

    Reordered the message hierarchy.

    Lead with the unsolved treatment problem. Mechanism second. Product claims held for the approved context.

  2. 2

    Rewrote the agency brief.

    “Build awareness” became three assignments: explain the unmet need, make the mechanism clear, and prepare a post-approval comparison framework.

  3. 3

    Re-prioritized spend.

    An unbranded disease explainer and a mechanism resource came first. Education partners were vetted on topic coverage before any budget was committed.

The result

  • A new message hierarchy and two education briefs, before creative was locked.
  • A scientific question list for Medical.
  • A new planning rule: every asset answers one audience question, or it is cut.
  • A baseline to measure the category conversation against at launch.

The takeaway

Find out what your audience already understands before you decide what your campaign should say.

Pre-approval work stayed within disease education and scientific exchange. Client and brand anonymized.

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In-marketEstablished brandBrand, Omnichannel, Medical, agency

An in-market brand sharpened its media plan around the websites AI cites.

Reach reports showed where the campaign ran. Pharma AI Monitor showed which pages shaped the answers. The team added a short list of priority sites to the plan, each with an action and an owner.

The challenge

The channel plan ran on reach, engagement, and site traffic. None of it showed what AI told patients and HCPs about the brand.

What Pharma AI Monitor showed

  • The brand was named, but its key message often was not.
  • Third-party pages cited in answers explained competitors in more detail.
  • A handful of specialist sites were cited more often than high-traffic ones.

The agency needed to know which gaps were worth funding, and which sites to skip.

What the team changed

  1. 1

    Owned pages: closed the gaps.

    Each brief named the page and the question it must answer. Medical reviewed every change.

  2. 2

    Existing partners: shared the gaps.

    The agency shared the specific question gaps with an education partner, for its independent editorial review.

  3. 3

    New sites: qualified before buying.

    Sites were scored on audience fit, topic relevance, and citation evidence. Weak candidates went to a watchlist.

The result

  • A website investment plan: one row per site, with the question, evidence, action, owner, and next decision.
  • Budget review focused on a short list of sites with evidence, not reach alone.
  • Medical reviewed concrete proposals with the evidence attached.

The takeaway

Every site on the plan needs a reason to be there, a specific action, and an owner.

A partnership buys a touchpoint, not an AI citation. Content work and answer changes were tracked separately. Client and brand anonymized.

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Label updateNew indicationBrand, Medical, Omnichannel, agency

After a new indication was approved, AI kept describing the old label.

The PI and owned pages were updated. AI answers were not. Pharma AI Monitor showed where the old label persisted, and which sources were behind it.

The challenge

A new indication was approved. The PI and brand sites were updated, but no one was checking what AI told patients and HCPs.

What Pharma AI Monitor showed

  • Most answers still described only the original indication.
  • Some answers mentioned the new use, but described the eligible patients too broadly.
  • Most cited sources were pages published before the approval.

AI answers were running on the old label. The fix started with the pages AI already cites.

What the team changed

  1. 1

    Owned pages: made the new indication easy to find.

    The HCP and patient sites got a clear, MLR-approved summary of the new indication, in plain language.

  2. 2

    Outdated sources: flagged for follow-up.

    Medical listed the most-cited pre-approval pages and contacted publishers through standard medical information channels.

  3. 3

    PI flags: routed to Medical.

    Answers that described the eligible population too broadly were logged with the full answer, source, and PI section.

The result

  • A clear view of where AI still described the old label, by platform and question.
  • Updated owned pages and a short list of outdated third-party sources to follow up.
  • PI flags documented with the evidence attached.
  • A tracked question set to confirm when answers reflect the new label.

The takeaway

A label change isn't finished when the PI is updated. Check what AI is still saying.

Owned content updates went through MLR. Publisher contact followed standard medical information processes. Client and brand anonymized.

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