This advanced guide helps commercial and medical teams in pharma organizations evaluate AI vendors for patient identification, engagement, evidence generation, and real-world insights. The checklist below reflects not just minimum qualifications, but key differentiators that separate commodity data providers from strategic AI partners
There’s more to a patient than vital signs, lab results, medical history, and medication allergies. While these data points are undoubtedly important, they don’t paint a full picture of a patient and their needs.
Theo Ahadome just returned from Reimagine Pharma Marketing, where he was honored to deliver a keynote on the evolution of personalized marketing in pharma. It was a unique opportunity to take the time to answer the question: if we could push reset on pharma marketing and really start from scratch, what would we do differently?
The healthcare AI landscape has reached an inflection point in 2025. Physician AI adoption has surged 78% year over year, with 66% now actively using AI tools. Add on ambient listening technology and the broader adoption of enterprise-wide AI solutions, and one key question emerges: how can medical affairs teams effectively apply AI to real-world health data in a way that keeps pace with quickly evolving technology?
This advanced guide helps commercial and medical teams in pharma organizations evaluate AI vendors for patient identification, engagement, evidence generation, and real-world insights. The checklist below reflects not just minimum qualifications, but key differentiators that separate commodity data providers from strategic AI partners.
The rush to integrate AI into pharma workflows — from commercial to medical — has created a flood of vendor claims:“Predictive insights.” “Patient-level triggers.” “End-to-end platforms.”But how do you separate the truly clinically grounded, compliant, and scalable AI solutions… from the ones that just look good in a demo?...
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