Jennifer Ghith, MS, MBA, GlaxoSmithKline; Matt Lewis, MPA, LLMental; Bob Schijvenaars, PhD, Digital Science; Monica Mody, PhD, Madrigal Pharmaceuticals; Andy Shepherd, PhD, CMPP, Envision Medical Communications; Valerie Moss, PhD, CMPP, Prime; Gary Lyons, Coronado Research; Catherine Skobe, MPH, MT(ASCP), Pfizer; Kristyn Morgan, Envision Medical Communications; and Jason Gardner, PhD, Real Chemistry.
The views and opinions expressed in this article are solely those of the individual authors and do not represent or reflect any official policies or positions of their employers.
The medical communications profession is at an unprecedented crossroads, facing extraordinary challenges and opportunities. Historically, the field has been shaped by the establishment of Good Publication Practice guidelines,1 open access, the controversies surrounding “ghostwriting,” as well as the need for significant author contribution, among others. Today, the rise of generative artificial intelligence (AI) presents yet another transformative challenge that will redefine content development and dissemination.2-4
Medical communications professionals within pharmaceutical companies and agencies are navigating the rapid acceleration of technological advances, particularly the implications and uptake of generative AI, amidst an evolving information ecosystem. The shift from traditional communication methods to digital platforms is driven by the increasing demand for timely and accurate health information, creating a complex, attention-based landscape.
Generative AI, with its ability to drive content development at great speed and volume, is changing the industry. However, this transformation is not without its tensions. The integration of generative AI into content development raises questions about quality, responsible use, the role of human expertise—and the business models under which companies operate.
As teams stand on the brink of this new era, it is crucial for pharmaceutical companies and medical communications agencies to collaborate and adapt.
There is a need to provoke new ways of thinking among pharmaceutical companies, data science professionals, the tech industry, and medical communications agencies. This should be coupled with urging these industries to embrace AI-driven innovations while ensuring the highest standards of quality and ethical integrity. By doing so, companies can navigate the complexities of the evolving landscape and achieve shared success in disseminating information across formats responsibly, with the objective of supporting the healthcare community and patients. The purpose of this paper is to synthesize the transformation taking place and share tips that catalyze collaboration as our industry, and cross-functional relationships, evolve in the age of AI.
The Rise of Generative AI: A Topline View of Transforming Content Development
Generative AI models have changed content creation by offering the potential for increased efficiency, relevance, and the ability to handle large volumes of information. By automating repetitive tasks and personalizing content for diverse audiences, AI has the potential to allow humans to focus on higher-level, holistic, strategic and creative work.2-4 This transformation brings both excitement and apprehension for medical content creators who rely on carefully honed skills and subject matter expertise to draft meaningful material that resonates with audiences, supports patients, and meets approval standards. Understanding how physicians, investigators, researchers, payers, other healthcare providers, and patients will react to content generated with the support of AI also needs more investigation.
Multimodal AI models, which integrate text, images, audio, and video, are pushing the boundaries.3 These models can generate contextually relevant content across different formats, catering to diverse audience needs and preferences. Podcasts and videos, for instance, combine narrative power with audio and visual impact. Complex medical information can be made more accessible and compelling at scale. This has major implications, for example, for the future generation of enhanced/extended publication content.
Medical communications professionals express excitement about the potential of generative AI to enhance brainstorming, creativity, efficiency, and personalization. The ability to produce high-quality content quickly and accurately could elevate how medical information is communicated. Saving time could mean information that impacts patient outcomes is released more quickly. However, this transformation also brings challenges. Concerns about the quality and authenticity of AI-generated content, the potential for bias, information overload from too much AI-generated content, and the ethical implications of AI use are present.
Additionally, the rapid pace of AI development means that medical communications professionals must continuously adapt to stay relevant. Some, although they may or may not express it directly, also fear losing their jobs, as well as reducing their critical thinking and creative autonomy , as more automation creeps into their day-to-day activities.5-7 New roles or responsibilities, such as AI compliance specialists, may also emerge.2-4 Rather than viewing AI as a threat, if upskilled, medical communications professionals can and should position themselves as indispensable experts who can effectively manage and leverage AI to provide superior quality content, developed in ways that serve the healthcare community.
Evolving Interplay Between Pharmaceutical Companies and Agencies
Teams at pharmaceutical companies are under pressure from within their organizations to reduce costs and accelerate the dissemination of data in order to advance patient care, all while maintaining the highest standards of quality. They are driven by the goal to improve patient outcomes through the provision of accurate, evidence-based information to healthcare professional audiences and patients, taking into account compliance and scientific integrity. To achieve these goals, they rely heavily on their agency partners to efficiently deliver accurate content, drive innovation, and maintain strong strategic partnerships aligned with shared objectives.
Agencies are also under pressure financially; for them, it is to maintain an acceptable level of profitability, while investing in innovative technologies, models, and staff to support the contractual obligations and expectations of pharmaceutical companies. Medical communications agencies strive to build strategic partnerships with clients and are also motivated by the same imperatives as their pharmaceutical company partners. Their success hinges on providing the best support to their pharmaceutical industry partners through their ability to adapt to new technologies, integrate AI tools into their workflows, and continuously innovate to stay ahead in a competitive landscape.
Even under the best of circumstances, the motivations and operational dynamics between these entities can differ. There are concerns that generative AI can tip dynamics in seemingly unpredictable ways, leading to potential pitfalls as well as creating or exacerbating tensions that impact the partnership and trust (Table).
Adapting to Change: 10 Tips for Success
Addressing these specific challenges and implementing strategies for effective collaboration will require creative solutions as well as revisiting some foundational principles. Adapting business models to fully leverage generative AI requires a strategic approach that goes beyond cost considerations yet is inclusive of the realities of them.8-10 Both pharmaceutical companies and agencies must embrace innovation, flexibility, and the benefits of collaboration. Recommended tips for success:
- Explore the evolution of business models: Shifts from existing service models and/or types of work may need to be evaluated. Some companies may explore novel ideas as well as value-based pricing, where compensation is tied to deliverables, outcomes, and impact. Do so with appropriate urgency and ensure the appropriate team members are included in discussions.
- Address contractual considerations early and as appropriate: Both parties will need to work with procurement and legal colleagues early to address whether and when contract changes will be needed, given new working models. This should be done quickly to avoid future miscommunication and delays.
- Design collaborative innovation labs: Establish innovation labs where pharmaceutical companies and agencies can co-create and test new AI-driven solutions in a secure environment. This can foster continuous improvement, shared success, and development of optimized tools in a supportive manner.
- Establish transparent best practices for implementation: Both parties should agree on implementation principles for AI integration and content creation. This includes understanding regulatory requirements, as well as setting expectations for quality, key performance indicators, timelines, and compliance standards. Given that the pharmaceutical company bears final accountability, it is critical that they drive decision-making in collaboration with agency partners.
- Maintain feedback loops: Regular check-ins and feedback sessions can help address issues promptly and ensure both parties are aligned. This can prevent small problems from escalating into major setbacks. Expect challenges at the beginning, in particular as teams navigate new ways of working and novel technologies. Being open-minded and agile are key behaviors necessary to drive success.
- Continuously re-orient to shared goals: Both parties should work towards common objectives, such as improving patient outcomes and enhancing communication strategies. This alignment can help mitigate tension points like cost pressures and differing views on AI integration.
- Leadership should model behaviors: Research shows that companies with leaders that are fully engaged and utilizing generative AI themselves drive innovation at their companies. Engaging in conversation, active collaboration, and sharing examples of this at senior levels will drive change. Those supporting senior leaders from both parties could help by keeping senior leaders informed and involved in opportunities to collaborate on AI initiatives more directly. A solutions-oriented mindset is vital.
- AI-enhanced project management: Utilizing AI tools to optimize project timelines and resource allocation can ensure faster and more efficient content delivery, resulting in significant impact. This can help meet the pharmaceutical company teams’ need for speed and cost savings without compromising commitments to quality.
- Collaborative AI training programs: Joint training initiatives can embed benefits of collaboration as well as upskill staff in both sectors, ensuring they are proficient in using AI tools and understanding their capabilities and limitations. This can enhance the quality and efficiency of content creation.
- Shared AI-driven content hubs (in appropriate circumstances): Establish centralized platforms where AI tools and training resources can be accessed and utilized by both pharmaceutical companies and agencies. This ensures consistency and streamlines the content creation process. However, this approach should be executed with care and within systems that are “closed” to external data sources – and agreed upon by both parties. Sharing tools is also not always practical or appropriate in terms of licensing, proprietary data concerns, and/or complexities introduced by teams adapting to different tools.
Adopting generative AI is a transformation, and like all changes, it requires clear communication, empathy, commitment from leadership, and sufficient attention to the shifts in mindset, toolset, and skillset that are required once the innovations have been adopted. Communication should start as early as possible and must articulate the enterprise, divisional, and/or departmental expectations to be satisfied by the implementation. In addition, senior leadership needs to visibly engage with the roll-out, giving front-line staff the space to experiment, recognizing that “failure” is part of the learning process.
Setting unrealistic goals demotivates an organization and lowers productivity when the effort is aiming to achieve its opposite. It is admirable to be ambitious but counter-productive to establish expectations that are unreachable. In addition, far too few organizations recognize that the historical model of change management where one “major” transformation occurs every 3–5 years is no longer viable. Now, life sciences organizations are in a constant state of change. Experts in enterprise transformations have estimated that for every one dollar spent on “digital” implementation, another dollar (or more) should be spent on human/psychological factors, human resources/training, and strategic enablement. This can be either by default or by design, and if institutions fail to invest up front in these activities, they pay the price later when their digital adoption meets significant resistance, fails to meet even reasonable goals, and/or their best people leave the organization.
During the 20th Annual Meeting of ISMPP in May 2025 in a plenary session entitled “Harmonizing the Future: Generative AI’s Effects on Medical Communications and Cross-Industry Collaborations”, the 10 tips for success were discussed by the panelists: Jason Gardner, Jennifer Ghith, Kristyn Morgan, and Catherine Skobe. Polling indicated that 40% of the audience described the current state of collaboration(s) with their agency/industry partners on generative AI-related activities as “exploratory.” The audience was also asked to rate the potential impact (n=160 respondents) and feasibility of the 10 tips (Figure 1) . Establishing transparent best practices for implementation was seen as the most critical tip. In the other categories, items that were rated as “most impactful” were not always most “feasible,” (Figure 2) such as exploring the evolution of business models or designing collaborative innovation labs.
Future Outlook
The future of medical communications with AI promises continued advancements in technology and evolving roles for professionals.2-4,11 The integration of generative AI into medical communications and content is inevitable, but its success will depend on how we address key bottom-line challenges that can erode trust and partnership. With all these bottom-line pressures, preparing for the future involves also considering topline benefits to embrace continuous learning, fostering a culture of innovation, and promoting collaboration. Balancing the capabilities of generative AI with human expertise remains crucial, and professionals need to embrace changes while upholding quality and ethical standards. Most of all, the dynamics of collaboration need to evolve to help stakeholders navigate the complexities of the evolving landscape and achieve shared learnings and success as we all drive to deliver trusted research faster to improve patients’ lives.
Acknowledgements: Michael Platt and the rest of the ISMPP AI Task Force for critical review, Richard McDonald of Prime for editing support, and Envision Medical Communications for figure creation.
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