How Gen AI is Reshaping Pharma Marketing

The pharmaceutical landscape is undergoing a seismic shift, propelled by the unstoppable force of generative artificial intelligence (Gen AI). This revolutionary technology is not just knocking at the door of pharma marketing—it’s bursting through, reshaping strategies, and redefining possibilities. From personalized content creation to brand compliance audits, Gen AI is arming marketers with unprecedented tools to engage healthcare professionals and patients alike.

As the industry grapples with stringent regulations and the ever-increasing demand for tailored healthcare solutions, understanding the impact of this AI revolution becomes not just important, but essential for survival and success in the competitive pharma market. The fusion of big data, machine learning, and creative algorithms is ushering in a new era of marketing efficiency and effectiveness, promising to transform how pharmaceutical companies connect with their audiences and deliver value in the healthcare ecosystem.

The Current State of Pharma Marketing

The pharmaceutical marketing landscape is currently navigating a complex terrain, characterized by stringent regulations, evolving patient expectations, and digital transformation. Pharma marketers face unique challenges, including strict FDA regulations on promotional content, the need to balance scientific accuracy with engaging messaging, and the imperative to reach both healthcare professionals and patients effectively.

According to a recent McKinsey report, only 10% of pharma companies feel fully prepared for digital marketing transformation, lagging behind other industries like retail and banking where the figure stands at 30%. One of the most pressing challenges is the shift toward personalized medicine, which requires more targeted marketing approaches. A study published in the Journal of Medical Marketing found that 68% of physicians prefer personalized content, yet only 24% of pharma marketers feel they’re meeting this need effectively.

Compared to fast-moving consumer goods (FMCG) industries, where digital ad spending accounts for over 50% of marketing budgets, pharma’s digital ad spending hovers around 30%, indicating room for growth. However, the pharma industry outperforms others in terms of content marketing effectiveness, with 65% of healthcare professionals reporting that they find pharma-produced content valuable, compared to a 45% average across other B2B sectors. As the industry grapples with these challenges, there’s a growing recognition that embracing advanced technologies like AI and data analytics is crucial for staying competitive and delivering value to stakeholders across the healthcare ecosystem.

The Promise of Generative AI

Generative AI is poised to revolutionize the way pharma companies engage with healthcare providers and patients. One of the key benefits is improved efficiency. According to McKinsey, generative AI could boost productivity across the pharma and medical products industries by $60-$110 billion annually. By automating tasks like content creation and document generation, generative AI frees up valuable time for medical liaisons and researchers to focus on higher-value activities.

Personalization is another area where generative AI shines. By analyzing vast troves of data, these models can generate highly tailored marketing materials that resonate with individual healthcare providers and patients.

As one industry leader noted, “Generative AI allows us to craft messages that speak directly to each customer’s unique needs and concerns.” This level of personalization can drive better engagement and improved health outcomes.

Crucially, generative AI also holds the promise of time and cost reduction while delivering stronger personalization and engagement. As the technology continues to advance, the impact of generative AI on pharmaceutical marketing will only grow. By harnessing its power, forward-thinking companies can gain a significant competitive edge while ultimately improving patient care and access to life-saving therapies.

Key Use Cases for Gen AI in Pharma Marketing

A. Quick, Efficient Insights Generation

One of the most promising applications for generative AI in pharma marketing is rapidly distilling vast amounts of data into actionable strategic insights. Pharma marketers often find themselves drowning in a sea of research, dashboards, and presentations, struggling to extract the critical insights that should inform their brand strategy. In fact, one marketer reported receiving over 300 slides and links to four dashboards when asking for the core insights behind a brand’s strategy. This information overload can significantly slow down decision-making, with situational assessments often taking over a month to complete.

Gen AI offers a solution in the form of AI-powered insight agents. These tools allow marketers and insight leads to quickly query various data sources, from market research to call centre transcripts and receive immediate, text-based responses. Imagine being able to ask an AI agent about access perception for a brand and getting a concise summary of the key findings, including specific areas for improvement. This capability can enable more real-time strategy shifts, as marketers can rapidly mine insights from sources like call centre transcripts to inform their next moves.

By harnessing the power of Gen AI to quickly surface meaningful insights, pharma marketers can make more informed, agile decisions, ultimately driving better outcomes for their brands and patients. As one CMO noted, “40% of what we do will be affected by gen AI within the year and more beyond that” , underscoring the transformative potential of this technology in the industry.

B. Easing the path of first-mile content

Pharma marketing has traditionally been a time-consuming and labour-intensive process, particularly when it comes to the early stages of content development. The creative brief and concept generation phases can often take weeks, involving multiple rounds of review and revision before the final copy is approved.

However, the emergence of generative AI (Gen AI) technologies presents an opportunity to streamline this process and unlock significant efficiencies. One key use case is leveraging Gen AI for creative brief writing and initial concept generation.

By using Gen AI to assist with the creative brief, marketers can cut down on the back-and-forth and time required to develop this foundational document. Gen AI can help capture the key elements of the brief, such as the campaign objectives, target audience, and messaging, in a more efficient and consistent manner.

Beyond the brief, Gen AI can also be employed to generate initial campaign concepts and ideas and even first drafts. Platforms like Midjourney, DALL-E, and Firefly can be used to create innovative visual concepts based on carefully crafted prompts, helping to kickstart the ideation process.

C. Tweaking last-mile content

One particularly promising application is the ability to “tweak” last-mile content for more inclusive and globally relevant campaigns. The challenge faced by many pharmaceutical marketers is the need to create campaigns that resonate with diverse audiences around the world, but with a limited pool of core creative assets. Often, the initial campaign may not fully capture the nuances required for certain regions or population segments. This can lead to a disconnect, as global marketers lament, “We cannot capture every variable” in the original content.

However, Gen AI is offering a solution to this problem. By leveraging image generation platforms like Midjourney, DALL·E, and Firefly, marketers can now prompt subtle tweaks to the campaign assets to make them more culturally appropriate and inclusive. This could involve adjusting the ethnicity or appearance of models, modifying the setting or background, or even personalizing the messaging to better suit the target audience.

The value-add of this capability is substantial. Estimates suggest that pharmaceutical companies could save $20-40 million by using Gen AI to adapt their core campaign assets, rather than having to produce entirely new content. Additionally, this approach allows for greater personalization and localization, which is increasingly important as customers expect more tailored experiences.

One pharma CMO noted, “Global marketers agreed this capability could fit nicely in the increasingly important in-house creative services being built in large pharma organizations. Agencies think this is their value to capture, but we need to make it ours.” This sentiment underscores the strategic importance of mastering Gen AI-powered content adaptation, as it can provide a competitive edge and significant cost savings.

D. Improving the final content and images

For some years now we have leveraged AI to improve copy and images for higher engagement and results. The way this works is that the AI deconstructs the content into components which can include the copy, the images, the CTA, the formatting, the emotional content, and the description. Then the AI generates all possible permutations of this piece of work (different language different images, different formatting etc) leveraging a semantic database. Typically, this generates over 1,000,000 different permutations. Then versions are sent to statistically significant samples to learn which elements work. Then these will be analyzed by component. Then the algorithms will generate a piece of work that combines the winning components together in a meaningful way. The results from this are impressive.

In one campaign, revenue lift from the AI-created copy versus the original human copy was a revenue lift to 66%. In a second campaign, this was raised to 127%. In each of these the AI could identify the percentage of elements (language, image, emotion etc) that contributed to the revenue lift.

E. Easing personalization and medical-legal review

One of the major challenges facing pharma marketers is the cumbersome and time-consuming MLR process for all marketing content to remain compliant which can significantly hinder their ability to deliver personalized content. The typical MLR process can take between 20 to 50 days and involves two to three rounds of review. This rigid process, coupled with the need for junior marketers to learn the ropes, has resulted in a “bolus of content” as personalization and new content iterations increase, leading to a 200% to 500% increase in MLR workload compared to just a few years ago.

However, Gen AI is emerging as a promising solution to ease this burden. A few pioneering pharma organizations are already exploring the use of Gen AI to streamline the MLR process.

Some key applications include:

 

1. 1. Highlighting messaging that has already been approved versus net new content, to expedite the review process.

1. 1. Auto-populating references and automatically checking for minor label updates.

1. 1. Providing “similarity” or “risk” scores to help identify content that may require a more thorough review than simpler, more derivative content.

1. 1. Offering “first draft feedback” to junior marketers, effectively learning from prior regulatory and medical comments and providing automated redlining of messaging and images.

These Gen AI-powered applications have the potential to improve speed to market by up to 50% and increase content delivery volume by 25% to 40%. As one CMO envisioned, “AI could be ‘the lawyer’ for low-risk, derivative content you change a few headlines, colors, fonts, visuals, nothing too risky it’s possible and would make the velocity of content incredible and on par with more consumer-driven industries, but in healthcare a human may always need to be in the loop.”

By harnessing the power of Gen AI to streamline the MLR process, pharma marketers can unlock the full potential of personalization, delivering more tailored and engaging content to their customers while ensuring regulatory compliance. This transformation can have a significant impact on the overall success of pharma marketing campaigns.

Overcoming Barriers to Gen AI Adoption

The pharmaceutical industry is heavily regulated, with strict guidelines and standards that must be adhered to. The adoption of Gen AI poses unique challenges in this regard.

One of the primary concerns is data privacy and security. Generative AI models often require large datasets, which may include sensitive patient information. Ensuring compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) is crucial.

To overcome these regulatory and compliance challenges, pharmaceutical marketing teams must establish strong partnerships with their legal, regulatory, and compliance teams.

These teams can help navigate the complex regulatory landscape, identify potential risks, and develop strategies to ensure that Gen AI implementations adhere to all relevant laws and guidelines. They can also assist in developing robust data governance policies and procedures to protect sensitive information.

Eularis often get called in to help pharma clients get AI projects through the legal and compliance reviews as often the internal legal and compliance teams pose questions about the data and the algorithms that the marketing team cannot answer.

By working closely with these teams, pharmaceutical marketers can build trust with regulatory teams, as well as regulators, and demonstrate their commitment to responsible and ethical AI adoption.

Adopting Gen AI in pharmaceutical marketing also requires a significant shift in mindset and organizational culture. Marketers, agencies, and leaders must be prepared to manage this change effectively.

Implementing Gen AI solutions often involves redefining workflows, roles, and responsibilities. Resistance to change can be a significant barrier, and it is crucial to address this through comprehensive change management strategies.

Effective communication, training, and stakeholder engagement are essential to ensure that all team members understand the benefits of Gen AI and are equipped to leverage it effectively. Leaders must also be prepared to champion the adoption of Gen AI and foster a culture of innovation and continuous improvement.

By addressing these barriers and embracing the opportunities presented by Gen AI, pharmaceutical companies can unlock new possibilities in marketing and beyond. The successful adoption of Gen AI will require a multifaceted approach that balances regulatory compliance, technological innovation, and organizational transformation.

Steps for Pharma Marketers to Begin the AI Journey