AI’s biggest contribution to adherence can be a new model: reading the signals patients leave before they stop, and responding with support built for that moment, but only if pharma decides who it wants to be to that patient.

In 2003, the World Health Organization reported that adherence to long-term therapy in developed countries averages about 50%. Since then, the industry has taken on that number in waves: first as a compliance problem, then as a cost problem, then as an infrastructure problem, with hubs, copay programs and nurse support built around individual brands. Today, manufacturers are in the beginning phases of building direct relationships with patients, with Eli Lilly reporting more than a million patients engaging with its LillyDirect platform in 2025.

Alongside experimentation with different tactics, the rise of AI has spawned a new breed of possibilities for driving easier patient access, with a large amount of money and attention spent on automation of reimbursement support, prior authorization and benefits verification across digital health providers and pharma in-house.

However, getting a patient to a first fill is only one piece of the puzzle, and keeping that patient on therapy at month six, month twelve and month thirty-six is another. Conversations with commercial and patient services leaders at large manufacturers over the past 18 months suggest most AI investment has been aimed at the first job, which is why the next breakthrough sits in the second.

In a JAMA randomized trial of 9,501 patients at three US health systems, text message reminders or behavioral nudges sent to people who had fallen behind on cardiovascular prescriptions did not improve refill adherence at 12 months, suggesting generic outreach does not move the needle.

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