GoodRx's AI Strategy: How the Prescription-Savings Leader Is Rethinking Consumer Health Discovery in 2026

TL;DR#

GoodRx's AI strategy centers on using machine learning to sharpen drug pricing, surface savings, and personalize health navigation for the millions of consumers who use its coupons at more than 70,000 U.S. pharmacies. Under CEO Wendy Barnes, who took the role on January 1, 2025, the company has expanded its Integrated Savings Program, shipped AI-assisted features like scan-and-search drug lookup, and run internal AI hackathons to find new consumer-health use cases. GoodRx reported $196.0 million in Q3 2025 revenue and has helped Americans save more than $100 billion on medications since 2011. But the hardest question in consumer health — why a patient abandons a prescription, distrusts a price, or quietly stops filling — still gets answered through clickstream data and rating-scale surveys, not the anxious, real reasoning behind a health decision. Conversational AI interviews close that gap by letting a patient explain the "why now" in their own words, at scale, in a category where every word is sensitive. This is exactly the listening layer Perspective AI is built for.

What is GoodRx's AI strategy?#

GoodRx's AI strategy is the company's effort to apply machine learning and generative AI to its core mission — connecting consumers to the lowest available price on prescriptions — by improving price accuracy, recommending the right savings program, and personalizing how people navigate medication decisions. It is an applied AI strategy rather than a model-building one: GoodRx is wiring AI into pricing, search, and consumer guidance across a marketplace that touches tens of thousands of pharmacies, not training foundation models.

That framing matters because GoodRx sits at a unique seam in American healthcare. It is neither a pharmacy nor an insurer, but the price-transparency layer between them. When AI improves how that layer works, it improves whether a real person can afford to start — or stay on — a medication. This post examines GoodRx's actual AI moves through 2025–2026, where its existing data tooling hits a ceiling, and why the most sensitive question in consumer health resists every quantitative method the company already owns.

This article is written for product, customer experience, and consumer-insights leaders in healthcare and digital health who are deciding where AI genuinely changes the patient relationship — and where it just adds polish. For a broader frame on this shift, see our analysis of why AI-first customer research cannot start with a web form.

GoodRx at scale: the company AI is being layered onto#

GoodRx is the largest consumer prescription-savings platform in the United States, and its scale is the reason its AI bets matter. The company operates across more than 70,000 pharmacies nationwide and reported $196.0 million in revenue in the third quarter of 2025, with adjusted EBITDA of $66.3 million, according to its Q3 2025 results filed with the SEC. Since launching in 2011, GoodRx has helped Americans save more than $100 billion on medications, per the company's full-year 2025 results.

The platform's core metric is Monthly Active Consumers — the unique people who use a GoodRx code to buy a prescription and save versus the list price in a given month. Around that core, GoodRx layers a pharma-manufacturer business, the Integrated Savings Program (ISP) that embeds GoodRx pricing inside insurer and pharmacy-benefit-manager channels, and more than 80 unique cash prices on brand medications that are poorly covered or not covered at all.

Here is the shape of the business AI is being applied to:

A marketplace this large generates an enormous amount of behavioral data — searches, coupon pulls, fills, drop-offs. That data is exactly what makes GoodRx's quantitative AI possible. It is also exactly why the company's understanding of patients risks staying behavioral rather than human. The same dynamic shows up across digital health; we trace it in detail in our look at how Hims & Hers replaced patient-intake forms with AI and the Teladoc AI strategy behind 80M+ telehealth visits.

Where GoodRx already uses AI in 2026#

GoodRx applies AI today in three concrete places: drug-price optimization, consumer-facing search, and internal experimentation. Each is real, shipped, and quantitative — and each strengthens the marketplace without yet touching patient reasoning.

Pricing intelligence. GoodRx has integrated AI into its pricing platform to streamline how medication prices are set and to improve price quality for both consumers and pharmacy partners, reducing the price discrepancies that erode trust at the counter. In a marketplace with 80-plus cash prices per brand drug, getting the right price in front of the right person is a machine-learning problem at its core.

Consumer-facing search. The company built a scan-and-search feature that lets users photograph a medication label to find coupons, collapsing the friction between "I have this prescription" and "here is what it costs nearby." It is a small, useful application of computer vision and matching that meets people in a real moment.

Internal AI experimentation. GoodRx has run internal AI hackathons to explore healthcare use cases for generative AI, documented on its corporate site, signaling an organization actively hunting for the next applied use rather than waiting on a single roadmap item.

Under Wendy Barnes, the strategic emphasis has been on expanding the Integrated Savings Program and integrating e-commerce with digital prescriptions — making GoodRx less a standalone coupon site and more an embedded affordability layer across the system. AI is the connective tissue that makes that personalization scale. For the buyer's-eye view of how AI engagement tooling is categorized in 2026, see our guide to AI customer-engagement software.

The consumer-health "why" that data can't reach#

The limit of GoodRx's current AI is that behavioral data shows what a consumer did, never why — and in a price-sensitive health category, the why is where retention, trust, and adherence live. A clickstream can tell you a user searched for a statin, pulled a coupon, and never filled it. It cannot tell you the user was afraid of side effects, didn't trust a price that seemed too good, was waiting to ask a doctor, or simply couldn't afford the cash price even after the discount.

That gap is not academic; it is the most expensive problem in American medicine. Prescription abandonment stays below 5% when a medication carries no out-of-pocket cost, but jumps to roughly 45% when the cost exceeds $125 and to about 60% when it tops $500, according to Patient Safety & Quality Healthcare. Medication non-adherence overall is estimated to cost the U.S. healthcare system close to $300 billion every year, the same body of literature reports. GoodRx exists to bend the cost curve on the first number. But knowing whether a discount actually changed a decision — and what else stood in the way — requires asking the person.

The two tools companies reach for here both fail in this category:

• Clickstream and product analytics capture behavior but infer intent. They are blind to fear, distrust, confusion, and "it depends."
• Rating-scale surveys force a person to translate a messy health experience into a 1–5 score and a dropdown. In a sensitive category, that flattening is worse, not better — and response quality degrades as fatigue sets in.

We unpack why static instruments lose the reasoning in AI vs. surveys: why conversations win for real customer research and offer a structural replacement in our AI survey alternative framework.

Why forms make the health-trust problem worse, not better#

Forms and rating scales are the default way consumer-health companies "listen," and they are uniquely poorly suited to a category built on anxiety and price sensitivity. A web form front-loads effort before any value is delivered, demands that a worried patient pre-translate their situation into the company's schema, and offers no way to follow up when an answer is vague. The highest-value moments in health decisions are exactly the messy ones — "I'm not sure," "I want to ask my doctor first," "the price felt too good to be real" — and those are precisely what a dropdown discards.

This is the core Perspective AI thesis applied to healthcare: AI-first customer research cannot start with a web form. When the subject is whether someone will start a medication, a form's flattening doesn't just lose nuance — it loses the signal that predicts churn and abandonment.

There is also a trust dimension specific to AI in health. Roughly 66% of Americans reported low trust in their healthcare system to use AI responsibly, according to a 2025 study reported by STAT. That makes how a company deploys AI to listen as important as whether it does. A transparent, conversational AI interview that lets a person speak in their own words reads very differently from a black-box score, and it earns the candor that quantitative instruments can't. For organizations building this muscle, our guide to building a voice-of-customer program from scratch and the broader 2026 voice-of-customer playbook lay out the cadence.

How conversational AI interviews unlock GoodRx-style customer research#

Conversational AI interviews capture the reasoning behind a health decision by replacing the form with an AI interviewer that asks open questions, follows up on vague answers, and probes the "why now" — at the scale of hundreds or thousands of conversations at once. Instead of a patient choosing from a dropdown, they explain in their own words why they hesitated on a price or abandoned a fill, and the AI digs exactly where a human researcher would.

For a company doing GoodRx-style customer research, that unlocks questions quantitative tools can't touch:

• Abandonment reasoning — Among people who pulled a coupon but never filled, why? Price, fear, distrust of the discount, waiting on a prescriber, or something else entirely.
• Price-trust signals — Does a price that seems "too low" actually reduce confidence? Conversation surfaces it; a survey never asks.
• Navigation friction — Where in the search-to-counter journey do people get stuck, and what were they actually trying to do?
• Program fit — For ISP, copay cards, and patient-assistance programs, which explanation makes a hesitant consumer act?

Because the interviews run as software, they scale the way GoodRx's other data does — but they return the kind of qualitative depth a research team would normally spend weeks gathering. Perspective AI's AI interviewer agent runs the conversations, while a concierge agent can replace a static intake form at the moment of highest intent, and transcripts roll up into Magic Summary studies automatically. Other regulated, high-trust industries have already made this move — see how Lemonade used conversational AI in insurance and how Cigna built conversational care navigation across 190M+ members. The pharmacy-side analog is detailed in our Walgreens conversational patient-experience analysis and the Ro telehealth-pharmacy intake case.

The 2026 context: AI's biggest health-care payoff is consumer experience#

The strongest near-term return on AI in healthcare is in consumer experience and engagement — and that is precisely where qualitative listening is weakest. In McKinsey's survey of healthcare leaders, 62% identified consumer engagement and experience as the area where generative AI has the greatest potential, yet only 29% had begun implementing gen AI for any purpose, according to McKinsey. The gap between belief and adoption is the opening.

For GoodRx specifically, the implication is direct. Its competitive moat is being the most trusted, lowest-friction path to an affordable prescription. Defending that moat in 2026 means understanding not just that a consumer left, but why — continuously, at scale, in their own words. The companies that win the consumer-experience layer will be the ones that turn the why into a standing research habit rather than a quarterly survey.

That is the through-line across every case study we've published in this space — from how a $5B telehealth platform like Hims & Hers ditched intake forms to Maven Clinic's AI onboarding for 17M+ members and UnitedHealth Group's member-experience strategy. Teams stress-testing their tooling can start with our customer-research tools stack for 2026 or the end-to-end AI customer-experience guide.

Frequently Asked Questions#

What is GoodRx's AI strategy in 2026?#

GoodRx's AI strategy in 2026 applies machine learning and generative AI to drug-price optimization, consumer-facing search, and personalized savings navigation rather than to building its own models. The company has integrated AI into its pricing platform, shipped a scan-and-search drug-lookup feature, and run internal AI hackathons to find new healthcare use cases — all aimed at connecting more of its consumers to the lowest available prescription price across 70,000+ pharmacies.

How does GoodRx make money and how big is it?#

GoodRx makes money primarily by collecting fees from pharmacy benefit managers when consumers use its codes, plus a growing pharma-manufacturer and subscription business. The company reported $196.0 million in revenue in the third quarter of 2025 and adjusted EBITDA of $66.3 million, per its SEC filings, and has helped Americans save more than $100 billion on medications since 2011.

Why can't clickstream data and surveys explain why patients abandon prescriptions?#

Clickstream data and surveys capture behavior and scores but not reasoning, which is the decisive factor in prescription abandonment. Analytics can show that a consumer pulled a coupon and never filled it, but cannot reveal whether the cause was cost, fear of side effects, distrust of a low price, or waiting on a doctor. Rating-scale surveys force a messy health decision into a dropdown and can't follow up, so the "why" is lost.

What is prescription savings and why does cost drive abandonment?#

Prescription savings is the practice of finding and applying discounts, coupons, and cash prices that lower a consumer's out-of-pocket medication cost, which GoodRx pioneered at scale. Cost drives abandonment sharply: prescription abandonment stays under 5% when there is no out-of-pocket cost but rises to roughly 45% above $125 and about 60% above $500, according to Patient Safety & Quality Healthcare, making affordability the single largest lever on whether people start their medications.

How can conversational AI improve GoodRx customer research?#

Conversational AI improves GoodRx customer research by running AI-led interviews at scale that ask why a consumer hesitated, distrusted a price, or abandoned a fill — and follow up on vague answers the way a human researcher would. Instead of inferring intent from behavior, teams capture the actual reasoning behind abandonment, price trust, and program fit, turning one-time surveys into a continuous, in-their-own-words listening habit.

Is it safe to use AI to research patients in a sensitive health category?#

Using AI to research patients is safe and effective when it is transparent, consent-based, and conversational rather than a black-box score. Because roughly 66% of Americans report low trust in their healthcare system to use AI responsibly, how AI is deployed matters: an AI interviewer that lets people speak in their own words and explains what it is doing earns more candor than a hidden algorithm, while still scaling to thousands of conversations.

Conclusion#

GoodRx's AI strategy is a genuine, applied bet: machine-learning pricing, computer-vision drug search, and personalized savings navigation aimed squarely at its mission of connecting 70,000+ pharmacies' worth of consumers to affordable medications. Those moves make the marketplace faster and more accurate. But the question that decides whether a patient actually starts and stays on a medication — the anxious, price-sensitive why behind an abandoned fill — still lives outside clickstream dashboards and rating-scale surveys. In a category where 66% of people distrust how their health system uses AI, the way you listen is as important as whether you do.

Conversational AI interviews are how a prescription-savings leader closes that gap: capturing reasoning, not just behavior, at scale, in the consumer's own words. That is what Perspective AI is built for. Start a research study in minutes to hear the why behind your own consumers' decisions, or explore the AI interviewer agent to see how conversations replace the forms that flatten health-trust signals into noise.