Using AI to Create an End-to-End Customer Experience in Pharma

AI is fundamentally transforming life sciences, with the potential to impact many aspects of the industry’s value chain -- from drug discovery to manufacturing. These AI applications are already available for forward-thinking pharma organizations. The commercial function is no exception – there are myriad use cases for AI that can be applied to reimagine life sciences sales and marketing. The challenge is in prioritizing those use cases to drive the most value and ROI.

Today’s AI applications are allowing marketers in the know to do amazing things, making deeper connections with patients and healthcare providers. “While AI can be used to generate meaningful insights and engagement, one of the most practical ways to get started with AI in pharmaceutical marketing is to think of ‘time as a product,’” explains Vyom Bhuta, Global Head, Commercial Innovation Life Sciences and Digital Health, at Cognizant. “Use cases that save time and resources are easier to quantify and make a clearer business case, laying the foundation for broader transformation and scale.”

Accelerating brand launches and campaigns around customer care can help organizations drive higher yield and throughput. Here are three use cases that put the “time as a product” concept into practice:

1. Moving from Static to Dynamic

Companies are tapping AI to build and generate images, content, digital ads and other promotional materials with generative AI. This is especially useful when it comes to the localization of these assets. “Localizing content for different markets -- translating it, making sure it culturally relevant -- has traditionally been a manual, time and resource intensive process,” Bhuta says. “With generative AI, you can actually feed marketing materials into a global template and, within minutes, prompt it to convert these assets into ads appropriate for local consumption.”

This improves the customer experience as well as speeding this information to doctors, nurses, and patients who actually need it, saving time and delivering better quality care. Based on client work, Bhuta estimates that companies can increase productivity by approximately 30 percent while reducing agency and marketing operations spend by 40 percent.

2. Creating the Perfect Co-Pilot

Pharmaceutical companies that are embracing AI in call center operations are seeing big benefits – including a lift in sales, decreased costs in customer care and an improved customer experience. AI can act as customer service co-pilot, suggesting responses in real-time to customer emails and calls by scouring internal and external knowledge bases. It can be embedded in customer portals and apps to generate dynamic responses to inquiries. These same capabilities can be leveraged to enable increased self-service functionality as well.

This is the next progression in the industry’s omnichannel shift. Pharma, historically, has been hyper focused on delivering customer experience through the field workforce. And over the last several years, and especially since COVID, that focus has shifted to digital channels. In both cases though, the focus has remained on outbound messages. “These are still push channels – pushing the message out to address the organic needs of the customer,” Bhuta says.

The real opportunity here lies in harmonizing customer experiences, consolidating fragmented customer care centers, and then leveraging AI within call center operations. “Pharma’s traditional push activities ultimately generate ‘pull’ reactions that result in inbound inquiries from HCPs, patients and wholesalers,” Bhuta explains. “It’s at this point in the customer journey that organizations can make the best use of AI to drive customer self-service and personalization, creating a bionic co-pilot for the customer care agents.”

3. Staying Compliant  

Compliance remains one of pharma’s key focus areas. Operationally, it consumes significant time and resources and limits speed to market. However, compliance is inherently rules-based, making it ripe for AI intervention, specifically in the medical, legal and regulatory (MLR) space. Marketing and legal can feed compliance policies and guidelines right into AI tools.

“AI can audit promotional content against compliance and design guidelines, creating a report on what is and is not compliant,” Bhuta says. “The tools can then make recommendations on what edits need to be made to bring those marketing assets into compliance before they’re rolled out to the public. Generative AI can go one step further, too, looking at the recommendations and, in real time, show what that those edits would look like.”

Maximizing Impact

Clearly, AI can serve as a commercial accelerator through use cases like these and others. The magnitude of its impact, however, will ultimately depend on an organization’s ability to navigate complexities around talent, ethics, policy and data.

How does AI impact the industry’s ongoing talent struggle and its related incentive structures? How can it be deployed ethically and safely? How can it leverage internal and external data sets to drive meaningful interactions with customers? These are the kinds of questions that must be answered before AI’s full value can be realized.

To learn about Cognizant’s Life Sciences practice, please visit

The editorial staff had no role in this post's creation.