The latest advancements in AI are reshaping the pharmaceutical commercialization landscape, unlocking new opportunities for more precise, efficient, and personalized strategies. However, the rise of AI also brings ethical challenges, particularly in the areas of personalized drug recommendations and targeted advertising. Addressing these concerns is crucial to ensuring AI is used responsibly and transparently in the pursuit of better patient outcomes.
PM360 asked 8 specialists about their predictions for the advancements of AI in the pharmaceutical industry. We asked:
• How are AI-driven predictive analytics transforming drug marketing strategies and improving patient outreach?
• What role does AI play in optimizing pharmaceutical supply chains to meet the growing demand for precision medicine?
• How are natural language processing (NLP) and AI chatbots enhancing the interaction between pharmaceutical companies and healthcare providers (HCPs)?
• What ethical challenges arise with the use of AI in personalized drug recommendations and targeted advertising, and how can they be addressed?
The opinions expressed by the authors in the Think Tank section are their own and do not necessarily reflect those of their affiliated companies or organizations.
Sr Principal, Patient Analytics
and AI, Global Commercial Solutions IQVIA
goksu.dogan@iqvia.com
Pharmaceutical markets continue to evolve post-COVID. There are increasingly more competitors in the market, including generics and biosimilars—driving demands on healthcare professional’s (HCP’s) attention to an all-time high. In today’s business context, marketers and sales teams are consistently asked to do “more with less” and when HCPs’ attention comes at a premium, efficiency becomes paramount. Engaging HCPs who have relevant patients requires a high level of precision, speed, and flexibility.
More precise HCP targeting, driven by Artificial Intelligence and Machine Learning (AI/ML) technology, has transformed the landscape by enabling complex and accurate patient identification via predictive analytics that source real-time data from numerous datasets. AI/ML techniques help brands maximize the benefit of both spending and reps’ time. AI/ML-driven analytics will help brands engage with a more receptive customer audience, optimize engagement, reach new audiences, and achieve better patient outcomes.
Patient-finding powered by predictive analytics has shown time and again that it can significantly outperform traditional rules-based methods, which are often retroactive or historybased and limited in complexity. Leveraging the power of AI allows patient-finding to be based on thousands of different variables, ranging from clinical events to HCPs’ digital behavior, allowing HCPs to reach a diagnosis earlier and treat patients sooner.
We expect predictive analytics to continue advancing in both speed and sophistication within the pharmaceutical industry. AI/ML techniques continue to improve along multiple dimensions: speed, accuracy, complexity, and the number of different datasets that can be analyzed.
Furthermore, as AI becomes more engrained in the pharmaceutical industry, companies will have to invest in their capabilities and understanding of AI/ML to avoid falling behind. Further integrating AI and predictive techniques into the day-to-day operation of a pharmaceutical marketing team is the future of the industry and crucial to improving patient outcomes.
Chris Tuleya
EVP & Managing Director Underscore Marketing
chris@underscoremarketing.com
In recent years, we’ve seen AI-driven predictive analytics revolutionize drug marketing strategies and patient outreach in the pharmaceutical industry. By understanding how to leverage advanced data science, these analytics can provide actionable insights that drive sales and enhance patient engagement.
Integrating diverse data sources, including sales, CRM, and digital interactions, allows for a holistic view of healthcare provider (HCP) preferences and provides a roadmap for more precise targeting and message segmentation. Custom AI algorithms move beyond traditional single-channel assessments to uncover complex, multivariate insights that optimize promotional strategies. This approach enables marketers to predict which HCPs are most likely to respond to specific promotional messages, allowing for more targeted and effective outreach.
One of the key capabilities of predictive analytics I’ve witnessed evolve is the ability to segment HCPs based on their likelihood to engage with promotional content and ultimately take an action. By analyzing historical data and refining models over time, future behaviors and trends can be forecasted, ensuring that marketing efforts are both timely and relevant. This predictive power is crucial in a highly regulated industry like pharma, where understanding the nuances of HCP engagement can significantly impact drug adoption rates.
An omnichannel approach ensures that all promotional touchpoints work together seamlessly to improve customer experience and sales outcomes. By identifying the optimal mix of channels and messages, brands can maximize their promotional impact while minimizing waste. This not only enhances the efficiency of marketing campaigns, but also ensures that patients receive timely and relevant information about new treatments in a personalized way.
Given the more complex challenge in patient marketing, utilizing HCP insights allows for better targeting and understanding of patient locations. By integrating this information with brand data on HCP proximity and patient willingness to travel for treatments, we can effectively prioritize patients who are more likely to encounter resistance from HCPs.
Amish Dhanani
Partner
Beghou Consulting
amish.dhanani@beghouconsulting.com
AI can be a valuable source of knowledge for pharmaceutical companies—knowledge about HCPs and their information preferences, patients, and their treatment journeys, etc. With this, pharma companies can make more accurate predictions, dynamically target HCPs, deliver more personalized promotions, and improve patient support and engagement.
Companies today have plentiful data at their fingertips. They can integrate traditional data (e.g., claims) with information such as social determinants of health to gain a richer understanding of patients and their treatment journeys. Using generative AI tools, companies can structure previously unstructured data from electronic health records (e.g., physician notes, lab results) and deepen their hypothesis and downstream insights. With these rich data sets, companies can use deep learning models to better understand the many factors that play a role in patient treatment journeys and uncover hidden insights that allow them to more accurately predict patient and HCP needs and behavior.
These predictive capabilities open doors for more targeted (and effective) customer engagement strategies. For example, more accurate patient prediction enables dynamic targeting of the most relevant HCPs. HCP engagement data also provides companies with the opportunity to understand channel and content affinities and craft personalized conversation starters and messages that align with an HCP’s information preferences (e.g., some HCPs may seek efficacy information, others may seek dosing information).
AI-powered predictive analytics also allows for timely patient engagement. Armed with the right insights generated out of reading patient notes or automated AI driven triggers, companies can intervene at the right time to provide patients with the support and assistance they need to continue to adhere to treatment.
Danny Sigurdson
Founder and CEO Courier Health
courierhealth.com
AI-driven predictive analytics are revolutionizing drug marketing strategies and patient outreach by enabling a more data-driven, personalized, and automated approach. Biopharma companies are already leveraging vast data sets, including proprietary first-party data, to identify areas of opportunity, segment markets, and refine marketing messages and channels. This increases the likelihood that marketing efforts reach the right audience at the right time, while also accelerating the cycle of learning going forward. By understanding patient and customer engagement more deeply, biopharma companies can deliver more tailored patient journeys and marketing outreach.
Perhaps most notably, AI-driven predictive analytics are transforming the way commercial teams engage and support patients and other stakeholders. Purpose-built systems can identify and flag at-risk patients, trigger timely interventions, and intelligently recommend the next best action. This frees up commercial resources and helps shift from reactive, manual support (e.g., manually reviewing spreadsheets or task lists to identify what needs attention) to proactive, tailored outreach. This improves overall engagement and enables resources to be more efficient and strategic in their outreach and support.
It’s worth noting that while there’s significant buzz around AI, not all solutions are created equal, particularly for a complex, high-stakes industry like ours. To add value, tech solutions need to be developed with a deep understanding of the complexities of biopharma and the nuances of connecting, capturing, and synthesizing sensitive patient data. This requires the right purpose-built, patient- focused commercial infrastructure in place. Leading biopharma companies are expanding their investments in patient-focused solutions to streamline their operations today and power increasingly specialized applications of AI in the future.
Timothy Martin
VP, Product
Yseop
tmartin@yseop.com
The increased demand for precision medicine is reshaping the pharmaceutical industry, requiring supply chains that are not only efficient but adaptable to highly specific, patient-centered therapies. AI has emerged as a pivotal tool in this transformation, enabling pharmaceutical companies to meet these demands while maintaining the rigorous standards of safety and accuracy required.
AI directly impacts the pharmaceutical supply chain through capabilities like inventory management, predictive analytics, and quality control. It helps forecast demand, mitigate potential disruptions, and optimize logistics, ensuring that targeted therapies reach patients faster. For example, AI-driven predictive analytics can reduce delays caused by supply chain bottlenecks, improving the reliability of drug distribution.
Equally transformative are AI’s indirect contributions. By automating complex processes such as Clinical Study Reports (CSRs) and patient narratives, generative AI solutions have helped companies reduce submission timelines from months to weeks. This acceleration not only enhances time-to- market for precision medicines, but also ensures supply chains are prepared to handle the increasing demands of tailored treatments. As we move further into the era of precision medicine, AI is redefining pharmaceutical supply chains, making them more agile, efficient, and aligned with the individualized needs of patients.
Nick Spuhler
SVP, Director of Applied AI
Fingerpaint Marketing
nspuhler@fingerpaint.com
True generative experiences directly between pharmaceutical companies and HCPs are a regulatory non-starter for the time being, as MRL teams can’t pre-approve content generated in real-time. Yet natural language processing (NLP) has turbocharged how pharma companies analyze and deliver healthcare information to customers, while AI chatbots are proving to be a promising 1.0 version of generative UX that help HCPs more easily access and apply this information in practice.
The power of NLP lies in its ability to wrangle the vast universe of unstructured data in healthcare. Pharmaceutical companies are sitting on an untapped goldmine of insights: 80% to 90% of healthcare data are unstructured, growing three times faster than structured data, and up to 97% go unused. NLP lets pharma companies efficiently analyze everything from social media posts and market research transcripts to clinical literature and claims histories. The resulting insights add crucial realworld context and data-backed emotional dimensions to traditional brand storytelling.
Digital engagement with doctors has evolved from simple push messaging to sophisticated data-driven content recommendations. Now, in the era of generative AI, brand interactions are becoming increasingly user-led, where doctors can use natural language to easily explore (pre-approved) pharmaceutical information through the lens of their specific practice needs and patient cases. AI chatbots are the first version of this engagement evolution, but certainly not its final form. As generative user experiences become increasingly multimodal—incorporating voice, vision, and data visualization—their value will only grow.
If pharma companies can deliver new experiences that help doctors better manage and make sense of healthcare information, the downstream effects are obvious but no less profound: improved care and outcomes for patients.
Tom Mueller
VP, Digital Innovation & Product Management
Inizio Engage
Tom.Mueller@inizio.com
Natural language processing (NLP) is getting better with each passing moment in terms of understanding both context and user intent, creating a promising foundation for effective chatbot experience design centered on positive engagement with HCPs. Although these developments can minimize HCPs needing to search on exact wording to get the desired result and find the right information faster, the integration of these technologies is not without challenge.
Poorly designed chatbots can frustrate users by misinterpreting queries or providing irrelevant responses, ultimately hindering effective communication and diminishing trust. Effective chatbot design needs to find the balance between efficiency and experience similar to effective Virtual Agent design. It should craft a great experience for the HCP, regardless of what it takes, always respecting the little time HCPs have for seeking answers. Chatbots should serve as the guides to the soughtafter information, not gatekeepers, and should be designed to do intake geared towards getting the resources they need as quickly as possible.
Because chatbots are offering a different channel or time of day to quickly obtain information that might not be otherwise available to them when they need it, they have the opportunity to provide a truly unique engagement. When the chatbot delivers on this promise, it does much to enhance the HCP’s experience with the pharmaceutical company.
Aside from chatbots, NLP can be used to enhance interactions with HCPs through speech analytics, creating a much larger feedback loop. Speech analytics offers short term impact with immediate feedback and coaching for contact center agents, as well as deeper analyses to highlight trends in HCP requests, points of confusion, etc., leading to new content creation opportunities. The use of NLP and AI, when designed with good engagement practices, will go far to overcome the challenges technology presents in positive customer experience.
Roshan Rahnama, MPH, MBA
Executive Vice President
Avalere Health
roshan.rahnama@avalerehealth.com
Increasingly, it has become the norm that brands truly understand and anticipate customer preferences, behaviors, and actions through predictive analytics, and subsequently curate personalized experiences. In healthcare marketing, this is no different, and perhaps arguably, the personalized brand experience carries even more significance considering the power it can have over treatment decisions and patient outcomes. Today, AI-driven predictive analytics is the engine that fuels this critical brand experience and enables a precise approach in healthcare marketing.
Interestingly, in parallel to the rise of AI-driven predictive analytics, is a movement toward the use of whole person health in healthcare marketing. Whole person health is a model that looks beyond the medicalization of disease and assigns importance to a broader health context—such as the conditions in which people live, what they have access to, what they consume, who their communities are, what their behaviors are, and so on. In turn, this broader aperture of whole person health, when overlaid with the power of AI-driven predictive analytics, allows healthcare marketers to reach customers with a brand experience that is more authentic and resonant.
Together, predictive analytics and whole person health, afford brands an unprecedented opportunity to differentiate by precisely anticipating customer needs, from prevention and diagnosis to prognosis and outcomes. In marketing, this approach further allows brands to not only rethink what they communicate to customers at each of these moments but also how and through whom.
Looking ahead, the use of AI-driven predictive analytics will extend beyond how drugs are marketed— when coupled with whole person health, it has the potential to redefine customer empowerment and set a new standard for personalized brand experiences.