AI Moves Beyond Efficiency to Build Trust in Pharma

How AI Is Being Used as a Trust Builder in Pharma Marketing

Key takeaways

• Trust building is a higher-value use of AI than cost-cutting alone.
• Co-creation with patients, caregivers, and HCPs improves authenticity and accuracy.
• Guardrails are essential to keep outputs consistent, compliant, and safe.

AI in pharma marketing is moving past efficiency talk. The priority is shifting to trust - earning it, proving it, and protecting it. That means less hype and more real-world use cases built with the people who live the disease every day.

Why trust is the best use case for AI in pharma

Political scrutiny around DTC advertising and pricing isn't going away. In that climate, AI can either widen the gap or bridge it. Marketers are betting on the latter by amplifying authoritative voices and putting patients, caregivers, and HCPs at the center.

"Get the voices of authoritative medical sources, patients and caregivers amplified enough so they're the ones influencing AI," says Adam Daley of CG Life. The goal: make sure what models surface reflects vetted facts and lived experience.

Co-create with patients, caregivers, and HCPs

Consumer skepticism is real - especially among younger audiences. Morning Consult data shows Gen Z is more negative on AI than millennials, and a chunk have stopped buying from brands they don't trust on AI use. Source: Morning Consult.

Use AI to listen, not replace. Daley notes AI is strong for social listening in rare diseases - finding families, mapping conversations, and spotting message patterns - while keeping human influencers and advocates front and center.

From SEO to GEO: teach AI what "good" looks like

The "new SEO" is GEO - generative engine optimization. Instead of optimizing only for search, create authoritative, structured, and patient-informed content that large language models can learn from.

Co-produce explainers, symptom glossaries, and treatment guides with patients, caregivers, and HCPs. Publish clear, medically reviewed content across owned channels. The aim: when models answer, they pull from sources that reflect clinical accuracy and community reality.

Case study: AI visuals that make symptoms tangible

Incyte's MPN initiative, "The Unseen," used generative AI to turn patient-described symptoms into 360-degree visual scenes. Patients chose their MPN type, selected a symptom, and described it in detail. The tool produced immersive visuals based on that input.

Outputs looked like flooded bedrooms, cages in a storm, or a dark couch shrouded in smoke to express brain fog. The intent wasn't spectacle - it was conversation. The visuals helped patients track symptoms and start better discussions with their doctors and the broader MPN community.

"Using AI helped with conversations and connections, and it actually pulled a more human element out," says Kristen Griffiths of Incyte. That's the point: tech that makes people feel seen earns trust.

Guardrails that make AI consistent and safe

• Do not generate patient likenesses. Keep visuals symbolic, not identifiable.
• Lock style guides into the model or tool to maintain consistent look and feel.
• Bake in compliance from day one, including HIPAA considerations. See HHS HIPAA.
• Test live with real patients, advocates, and HCPs before scaling.
• Be transparent: disclose when and how AI was used. As Daley says, "People need to know if AI was involved."

What to do next: a practical playbook

• Form a patient-caregiver-HCP council to co-create briefs, vocabularies, and review criteria.
• Publish a plain-language AI transparency statement and apply it across channels.
• Stand up GEO: build medically reviewed content hubs, FAQs, and symptom libraries that models can ingest.
• Run social listening for unmet needs and message gaps, especially in rare diseases.
• Define guardrails: no likeness generation, style locks, PHI screening, toxicity filters, and audit logs.
• Pilot fast, in person: run workshops where patients test AI tools and give direct feedback.
• Measure trust signals: symptom reporting quality, HCP conversation rates, content accuracy audits, and sentiment shifts.
• Close the loop: publish what you learned and how you improved based on community input.

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