Generative AI is continuously improving pharma marketing and life sciences. Several pharma marketers are still uncomfortable working with AI tools like ChatGPT, Microsoft Copilot and others. However, these tools can improve quality, productivity, speed, and cost. This helps biopharma organizations save large sums that they can later reinvest in serving more patients and improving patient outcomes. Here are some key use cases of generative AI in pharma marketing:
Offers critical insights
Brand marketers and Chief Marketing Officers (CMOs) often struggle with the overwhelming amount of research, presentations and data that make it difficult to identify key insights needed for strategic shifts. One marketer highlighted the issue, sharing how they received over 300 slides and multiple dashboards when asking for the insights behind their brand’s strategy. In response, some organizations are investing in insight agents AI-powered Q&A systems that allow marketers to quickly extract insights from various data sources, including dormant research files, tableau dashboards and processed secondary data. A few companies are also leveraging generative AI marketing to analyze call center transcripts and extract actionable insights, enabling more real-time strategy adjustments.
Creates creative briefs
Marketers have shown some hesitation toward the idea of using generative AI for “first-mile content” creation, while CMOs are far more enthusiastic. Traditionally, the process of developing creative concepts begins with a detailed brief, which can take weeks to prepare. Following this, agencies typically spend four to eight weeks on concept development and ideation. Generative AI offers an opportunity to streamline this process, with AI-generated creative briefs reducing the back-and-forth and helping marketers work more efficiently. In some cases, AI-generated concepts are turned into images or sketches for testing, with a few marketers experimenting with tools like Midjourney, DALL·E and Firefly to create innovative campaign ideas. However, pharma marketers currently lack the skills for prompt engineering and using large language models, requiring upskilling in these areas. The potential impact is significant.
Helps in medical-legal review processes.
Mid-level marketers are highly doubtful that generative AI could help with the medical-legal review (MLR) process, which they describe as cumbersome and time-consuming. However, CMOs see this as a top priority, believing AI can significantly enhance personalization efforts. Pharma companies often struggle with personalization due to the long, rigid approval process, which can take 20 to 50 days per promotional asset, requiring multiple reviews. This challenge is exacerbated by junior marketers needing to learn the process, along with a surge in content as personalization demands increase MLR workload by up to 500% compared to a few years ago.
Some organizations are beginning to use AI to streamline MLR processes by highlighting pre-approved messaging, auto-populating references, checking for minor label updates, and providing similarity or risk scores for content. AI is also being tested to offer “first draft feedback” for junior marketers, learning from previous regulatory feedback and automating redlining of content. These innovations could potentially speed up time-to-market by 50% and increase content output by 25% to 40%.
With generative AI and effective pharmaceutical consulting, organizations can streamline their market access processes, personalize the experience and respond rapidly to market changes.