AI drives automation and optimization that empower pharmaceutical marketers to rapidly scale commercialization-oriented content and activities.
Transformation is a word often paired with artificial intelligence (AI). The transformative impact of AI on pharmaceutical marketing is significant, with much to consider regarding its promise and how it’s reshaping the industry. Data suggest AI adoption is real, significant, and far beyond hype. The Life Science AI Research Report1 shows that 91% of respondents—primarily pharmaceutical and medical device executives—recognize AI’s value, with only 7% expressing skepticism. An impressive 74% of respondents reported they are either currently using, testing, or actively exploring AI solutions. Among them, a majority (54%) have an AI solution (or multiple AI solutions), and nearly half (49%) intend to use AI to improve their business efficiency (see Figure 1).
Respondents cite workflow automation, data analysis, and realtime content generation as areas of interest within their organizations. AI supports marketers in key areas such as personalized campaigns (with tailored messages to healthcare professionals (HCPs) and patients), predictive analytics, and social sentiment analysis. Marketers also favor AI-powered tools that allow them to optimize resource allocation and ensure compliance with stringent- and content-intensive regulatory requirements. Early results are indeed impressive, with the potential to cut month-long processes of analysis, synthesis, and content generation by half.
AI is also driving the development of important commercialization-oriented clinical content. Two promising applications for AI include Global Value Dossiers (GVDs) and Systematic Literature Reviews (SLRs).
Challenges in Implementing AI
So, does AI mark the end of laborious processes in pharma marketing? Can inefficient workflows be resolved, giving high-value professionals extra hours each week to concentrate on high-impact tasks? The answer is yes and no.
Technology proof-of-concepts often wow users with tantalizing impact and their ability to reduce repetitive work. However, operationalizing them to enable professionals to shift their focus to validating the accuracy of AI solutions at scale is an entirely different matter.
In our seven years of supporting AIdriven development and deployment at pharmaceutical companies, some common challenges often emerge—issues that can undermine AI deployments—impacting accuracy, quality, and overall efficiency. The most prominent among them include:
- AI misinterpretation of critical information: As an example, a team might ask ChatGPT to answer a question, and the AI engine may deliver an impressive volume of information, while misaligning with the core business, leading to conclusions that are either overvalued or flat-out wrong.
- Incomplete context: For instance, AI may return outdated results, while more recent and relevant information published on niche sites is not considered.
- Staying ahead of AI evolutions: With the rapid evolution of AI technologies—ranging from prompt engineering to retrievalaugmented generation (RAG) to agentic techniques, underlying AI and large language model (LLM) implementations—are changing frequently.
Pharma marketers rely on information technology (IT) expertise—either internal or outsourced—to evolve back-end technology in lockstep with industry trends. IT experts perform rigorous testing and quality assurance (QA) practices that help minimize AI errors. When the underlying AI, solution fits the use case, pharma marketers can scale their work with confidence and keep up with market events in a way that less-optimized competitors cannot.
Making the most of AI initially requires careful judgment and forethought as to where AI fits best—lest the tech cure to laborious processes and inefficiency fail to deliver against inflated expectations. In many areas, human expertise remains essential for contextual interpretation, quality control, and stakeholder engagement, ensuring that AI-driven outputs are accurate, relevant, and actionable. This integration of human insight is imperative to strengthen marketing initiatives, bolster the quality of regulatory submissions, and enhance market access strategies.
Human-in-the-loop design is a driving strategy across AI implementations for good reason. Incorporating human oversight and intervention at key points rather than solely relying on algorithms can boost quality while optimizing time for experts for more complex tasks.
Applying AI to GVDs
Amassing comprehensive productand market-related data, GVDs demonstrate the value of a pharmaceutical product to various stakeholders, including payers, healthcare providers, and regulatory bodies. Brand managers use GVD assets across multiple regulatory and strategic deliverables, including data summaries for efficacy, safety, and cost-effectiveness that feed health technology assessments. To effectively deliver a GVD and support a product launch, elements like quality- adjusted life years , comparative effectiveness data for payer negotiations, and competitive effectiveness summaries for marketing campaigns and sales pitches must be meticulously synthesized.
AI improves efficiency and opens new avenues in GVD, including:
- Personalization and Localization: Leveraging AI for content localization optimizes content delivery across diverse markets. AI algorithms analyze countryspecific regulatory guidelines, payer preferences, and linguistic nuances to ensure accurate and contextually appropriate translations. This process enhances message consistency, compliance adherence, and overall content efficacy while streamlining workflows and reducing time-to-market.
- Streamlining Dossier Creation: Generative AI tools assist experts in creating GVDs by drafting sections like executive summaries, clinical evidence reviews, and economic arguments. These tools also improve consistency and reduce errors by automatically filling dossier templates with the necessary data and insights.
- Ensuring Regulatory Compliance and Quality Assurance: AI-driven tools enhance GVD compliance and quality by checking dossiers against guidelines from organizations like NICE, FDA, and EMA, ensuring the content meets requirements. These tools also improve overall dossier quality by flagging inconsistencies, inaccuracies, or missing information.
Major pharmaceutical companies are utilizing AI in GVDs to quickly analyze large data sets to incorporate the latest clinical evidence, help predict patient responses, optimize drug delivery systems, and fuel realtime responses to market events, among other functions.
Figure 1. AI Initiatives Among Life Science Executives, Life Science AI Research Report, 2024
Systematic Literature Review at AI-Speed
SLR also benefits from AI’s automation and optimization. SLRs are mandatory literature assessments for regulatory submissions in drug development and are also required for most clinical journal publications. In the commercialization process, SLRs are fundamental to demonstrating that innovations are thoroughly vetted, scientifically justified, and likely to be safe. SLRs aim to answer specific questions related to drug safety, efficacy, and comparative effectiveness, often contributing to GVDs. Currently, the processes involved in literature reviews are manual, time-consuming, siloed, and error-prone, diverting high-value professionals (typically PhDs) from their core responsibilities to perform mundane manual tasks. Additionally, a single literature review can cost as much as $140,000, which can significantly drain research budgets.
AI-enabled tools optimize the SLR process in many ways, including:
- Efficient Search Optimization: I algorithms search a variety of sources, such as PubMed and other private databases, refining results based on study criteria. They automatically remove duplicate studies and prioritize the most relevant findings, streamlining the review process and reducing manual workload.
- Study Selection and Screening: AI streamlines the literature review process by quickly classifying abstracts and full-text articles. AI also ensures quality by flagging potential biases in study methodologies, such as small sample sizes or conflicts of interest.
- Content Synthesis and Extraction: AI automates content synthesis, generates summaries, formats manuscripts, and extracts relevant data.
AI tools perform SLRs with high accuracy, especially as part of a human-guided technology process. This saves time and allows PhD researchers and highly credentialed pharmaceutical leaders to focus on achieving breakthroughs that enhance health and wellbeing, rather than spending time on mundane tasks.
For instance, a major pharmaceutical manufacturer utilized AI in the SLR process for searching, deduplication, screening, summarization, and data extraction. It leveraged experts’ input at key points in the process, to assess and validate the work. This AI-aided literature review process supported by expert validation, enabled the company to complete the literature review process 40% faster—with 90% accuracy in title and abstract screening and over 80% accuracy in summarization and data extraction.
The Evolving New Standard Pharma marketing teams seeking a roadmap for embracing advanced technologies like AI in this dynamic and highly regulated industry are fortunate that many of their peers share their path and often their roadblocks. Embracing AI to support pharmaceutical activities sets a new standard for evolving internal processes, as well as therapy development and commercialization. Pharma marketers investing their time and energy to hone AI in service for product commercialization are equipped to make smarter, faster decisions to ultimately support more timely delivery of life-changing therapies to patients.