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The life sciences industry has undergone transformative change in recent years. Social distancing norms during the COVID-19 pandemic disrupted traditional commercial models, accelerating the shift to virtual engagement and digital-first customer interactions. Today, healthcare professionals (HCPs) increasingly consume medical and clinical information via computers, laptops, or mobile devices, reducing the frequency of in-person, sales representative-led visits.
Figure 1: HCPs device preference for consuming information
Patients also prefer digital channels such as social media, sponsored forums, and company websites when engaging with life sciences organizations.
Figure 2: Channels as the primary source of pharma-created information for patients
To keep pace, companies are rethinking their strategies and using advanced Generative AI use cases in life sciences to understand customer preferences, increase operational efficiency, and deliver personalized, data-driven campaigns. By applying life sciences commercial analytics solutions, organizations can transform vast datasets into actionable insights, automate workflows, and strengthen decision-making. Drawing from our work with industry leaders, we have identified five foundational AI-driven opportunities that are essential to achieving sustainable growth and commercial excellence.
1. Laser-sharp identification of Key Opinion Leaders (KOLs)
KOLs play a pivotal role in shaping perceptions and influencing prescribing behaviors, making them an essential focus for life sciences commercial teams. With their vast reach, strong credibility, and trusted voice within the medical community, the right KOLs can significantly amplify brand visibility and engagement. Aligning commercial strategies with these influential experts is a proven way to gain (and sustain) customer mindshare.
However, identifying the most relevant KOLs is no simple task. It often involves sifting through years of clinical literature, research publications, and medical journals from hundreds of HCPs, as well as assessing their influence at the national, regional, and local levels. This process can be both time-intensive and resource-heavy.
By leveraging specific AI use cases in life sciences, organizations can dramatically accelerate KOL identification. AI-powered search and life sciences commercial analytics solutions can scan public databases such as PubMed and Scopus, retrieve targeted publications in specific therapeutic areas (e.g., vaccines or immuno-oncology), and validate relevancy in seconds. Social media platforms like LinkedIn and X (formerly Twitter) can also be mined for valuable insights into HCP discussions, expanding visibility into each KOL's reach and influence.
Since social media data is inherently unstructured, Natural Language Processing (NLP) becomes a critical enabler. NLP models convert vast volumes of fragmented data into structured formats, detecting sentiment, identifying emerging trends, and pinpointing recurring themes in HCP conversations.
Figure 3: NLP workflow
These insights, combined with a scoring system based on defined attributes, allow companies to rank HCPs objectively — ensuring they engage with the most impactful KOLs for their therapeutic area of focus. This not only boosts targeting precision but also strengthens life sciences commercial effectiveness.
2. Data-driven omnichannel campaigns
Across the healthcare value chain, from HCPs and patients to payers, customers now expect the same personalized, contextually relevant experiences they enjoy in other industries. This demand for personalization is especially strong among HCPs, with 63% in an Indegene survey stating they want pharmaceutical representatives to share only relevant content that makes each interaction more valuable.
Meeting this expectation requires more than traditional marketing tactics. By leveraging specific AI use cases in life sciences, organizations can analyze large volumes of engagement data to uncover recurring patterns in behavior, channel preference, and content consumption. Customized AI algorithms can then group audiences into micro-segmented cohorts, such as HCPs consistently engaging with cardiovascular treatment content or attending webinars on advanced oncology devices, enabling tailored campaigns for each group.
AI also optimizes timing by identifying the days and hours when different audience segments are most likely to interact with content across websites, email, and social media. These insights help life sciences companies trigger campaigns at peak engagement windows, resulting in measurable gains including up to a 12% increase in HCP engagement and a 20% boost in annual sales conversions.
Here are 8 crucial steps that go into building a hyper-personalized omnichannel engagement strategy:
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Evaluate your data landscape and AI maturity through discovery workshops
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Combine multiple data sources and integrate them using a cloud-based data platform
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Leverage AI algorithms to build affinity scores by mapping HCPs' historical activity across channels and content
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Create micro-segmented models to define HCP personas across variables
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Understand channel and content affinities among HCPs by executing controlled experiments through hypothesis testing
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Design an integrated omnichannel call plan and activate them through integrations with downstream CRM systems
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Automate salesforce recommendations with AI
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Build a closed-loop performance measurement plan
3. Hyper-personalized content
For any omnichannel campaign to succeed, a customer-first content strategy is essential. Poor content quality can weaken engagement, diminish brand perception, and drive customers away. Organizations can address this challenge by using specific AI solutions in life sciences (like ChatGPT tools) to create content that is timely, relevant, and tailored to each audience segment.
This way, companies can understand what topics resonate most with specific HCP cohorts or patient groups, and deliver hyper-personalized content across their preferred channels. This approach not only improves message relevance but also strengthens life sciences commercial effectiveness by ensuring every interaction adds value to the customer experience.
HCPs surveyed by Indegene said they are overwhelmed by product promotional content pushed by life sciences companies on various digital channels
HCPs feel that sales representatives do not completely understand their expectations.
Figure 4: Illustrates what a hyper-personalized content journey looks like with AI
4. Automated content generation
Personalization demands relevant content - lots of it and at an accelerated pace. With the explosion of digital touchpoints, even seasoned life sciences marketers struggle with content marketing at the required pace.
This problem is far too common in other industries.
Marketers are under pressure to create assets or deliver campaigns more quickly
Marketers revealed they now need to create ten times as many assets as before to support all customer touchpoints
Thanks to technologies like Natural Language Generation (NLG), life sciences marketers can now let AI ease some of their content generation load.
NLG, a part of NLP, studies large datasets and converts them into plain natural language at an extraordinary scale, accuracy, and pace. NLG models can be trained to generate content for pre-defined templates, turning structured data like numbers, tables, charts, and graphs into descriptive reports aligned with the brand's voice. It works best for long-form content when humans are an integral part of the process.
Figure 5: Illustrates the six stages involved in NLG.
5. Next Best Action (NBA) engine
Earlier in this article, we discussed how many HCPs feel overwhelmed by the sheer volume of promotional pharma content they receive. This has pushed life sciences commercial teams to move away from a product-first mindset and instead deliver personalized, contextually relevant messages.
An NBA engine makes this possible. Rather than focusing on finding the next best customer, it prioritizes identifying the next best proposition for each customer — one that is timely, relevant, and aligned with their needs. An effective NBA model, integrated with downstream systems, uses AI and machine learning to recommend the most appropriate action based on a customer's past behavior, recent interactions, interests, and preferences.
This approach enables marketing and field-force teams to improve personalization and strengthen life sciences commercial effectiveness. At its core, an NBA engine combines predictive analytics, which forecast the likelihood of engagement with a specific content or campaign, and adaptive analytics, which learn from each interaction to refine recommendations over time. The result is a system that guides every customer engagement, optimizes marketing outcomes, and increases both engagement and sales conversions at scale.
Figure 6: Illustrates orchestrating a customer journey using NBA techniques.
Act now
Artificial intelligence is no longer an emerging trend. It is already transforming how healthcare is delivered, experienced, and scaled. For life sciences organizations, the opportunity lies in acting decisively to embed AI into the core of their commercial strategies. The first step is to identify your most pressing customer engagement challenges and assess whether your current capabilities are equipped to address them. Next, map these needs against targeted AI use cases in life sciences that can deliver measurable improvements in life sciences commercial effectiveness.
Begin with focused pilots that solve high-impact problems, generate actionable insights, and validate what works in your specific context. Use these learnings to scale AI-powered commercial capabilities that work seamlessly together, from advanced analytics and hyper-personalized content to omnichannel activation and Next Best Action engines.
The organizations that move now will be positioned to achieve sustained commercial excellence. Those that delay risk falling behind in a rapidly evolving, AI-driven marketplace. The future of customer engagement in healthcare is already here, and it is being built by those who choose to lead with AI.
Authors
Gaurav Kapoor
Vikas Mahajan
