Many AI initiatives in the pharmaceutical sector fail to create value—or even progress beyond the concept stage—not due to technical constraints, but because the product strategy fails to account for the industry’s unique operational, scientific, and regulatory landscape.

^{Build Specialized AI with Established Regulatory Boundaries}

Integrating a large language model-based AI like GPT-4 or Gemini to enhance a brand's user experience may be effective in consumer technology, but it doesn’t translate well to pharma. In life sciences, all promotional content must undergo a thorough Medical-Legal-Regulatory (MLR) review before being shared with healthcare professionals, patients, or consumers. This mandatory review process is inherently at odds with the real-time content generation capabilities of general-purpose AI platforms.

"General-purpose AI can certainly help accelerate internal operations and automate routine tasks, but when it comes to customer-facing applications in pharma, you can't just import solutions from other industries," explains Arpa Garay, former chief marketing officer at Merck and chief commercial officer at Moderna. "When communicating about treatment options, an incorrect phrase generated by AI isn't just a small error—it's a potential compliance disaster. Unless the model is specifically designed to deliver pre-approved messaging, maintain full audit logs, and include safeguards trusted by our medical, legal, and regulatory teams, it has no place in patient or customer interactions."

While many AI developers focus heavily on refining model inputs—whether for general-purpose AI or niche specialty models—this emphasis on input curation, including training and tuning, only covers half the equation for life sciences. These models often lack mechanisms to ensure outputs remain compliant. The most effective pharma AI tools operate within closed-loop systems that only generate responses using pre-approved language. For AI to thrive in life sciences, compliance needs to be embedded into the system architecture from the start, not added later.

When companies attempt to use generic AI models to answer HCP inquiries or boost patient engagement, they run the risk of generating hallucinations, off-label statements, or noncompliant phrasing—eroding trust and exposing the company to legal and regulatory consequences. Just one such mistake from a new AI feature can derail a product launch and cause broader organizational skepticism toward future AI efforts. Conversely, organizations that have successfully implemented AI rely on secure, vetted closed-loop systems that ensure only MLR-approved content is ever displayed, keeping communication aligned and fully compliant.

^{Maintain Compliance While Prioritizing Usability for Patients and Clinicians}

Many companies develop AI tools that meet every compliance standard, yet still fall short in actual use. Passing even the strictest compliance checks—with the backing of a highly skilled AI governance team—doesn’t guarantee a doctor will actually use the app during a patient visit or that a patient will complete a registration form.

Jennifer Oleksiw, chief customer officer at Eli Lilly, describes the situation: “At Lilly, we are part of a growing movement reshaping healthcare, driven by consumers who want more control over their health. They’re looking for more than medication—they seek information, support, and collaboration. Committed to improving health outcomes, we take a comprehensive approach and use AI to personalize and enhance experiences throughout the patient journey. However, there are challenges in responsible AI adoption that must be addressed to unlock digital health’s full potential. It’s crucial to collect data responsibly and ensure the right message reaches the right person at the right time.”

Oleksiw’s perspective highlights that usefulness is equally vital. Take a patient enrollment chatbot built to simplify access to financial aid: although its text was fully approved, complex wording and poor interface design led most users to abandon the process. Meanwhile, teams achieving success with AI adoption combine regulatory rigor with top-tier UX—refining plain-language copy, navigation cues, and visual elements through direct feedback from patients and clinicians. When usability receives as much attention as compliance, AI moves from merely being “approved” to genuinely enhancing decisions and outcomes.

^{Establish Enterprise-Wide Alignment Early On}

Even the most advanced AI platform will struggle without enterprise-wide coordination. As Diogo Rau, chief information & digital officer at Eli Lilly, explains:

“Some of the biggest opportunities for AI lie in life sciences. But I’m convinced it’s not just about how many GPUs you have. You need scientists with deep insight, machine learning experts with fresh thinking, labs to test those ideas, manufacturing specialists who understand scalable production, and more. We can’t let just one team push AI forward; it must be a company-wide effort. Solving big problems won’t come from a single model generating molecules in isolation.”

Rau’s observation clarifies why pharma AI initiatives rarely fail due to technical reasons alone. More commonly, they stall because brand, medical, legal, IT, and commercial teams pull in different directions. Without early and continuous buy-in from all stakeholders, promising pilots often die in committee. Companies that scale AI successfully treat it as an enterprise capability from the outset—engaging every function involved in approval, deployment, and measurement—ensuring lab breakthroughs lead to real-world impact.

Start AI Projects That Deliver Tangible Results

The most frequent strategic mistake? Launching AI simply because it’s trending rather than to solve a specific business or clinical challenge.

“AI isn’t the goal—it’s the outcome it creates,” says Dalya Gayed, MD, VP & US marketing lead for Reblozyl at Bristol Myers Squibb. “In life sciences, we should adopt AI not because it’s new, but because it helps us achieve better results faster, smarter, and more efficiently. Innovation is no longer optional—it’s essential to staying relevant and delivering real value.”

Successful pharma AI deployments begin with a clearly defined objective—such as shortening time-to-diagnosis, improving adherence, increasing HCP engagement, or speeding up clinical trial recruitment. The AI solution is then selected and applied to support that objective—not the other way around.

Ultimately, AI holds great potential to transform the life sciences industry—but only for organizations that take a deliberate, context-aware approach. In regulated environments, trust and usability matter just as much as technical performance. The leaders driving this transformation will be those that align innovation with compliance, strategy with execution, and technology with human behavior.

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