Insights & Analytics in Life Sciences: How to Embrace a Future with Generative AI

First, A Few Potentially Unsettling Truths

• Adoption and adaptation to Generative AI (GenAI) is a strategic imperative for pharma commercial insights departments if they want to maintain and enhance their relevance, impact, and strategic value within their organizations.
• Organizations are increasingly data-driven; market research teams that do not leverage AI will struggle to provide the timely, nuanced insights that modern pharma marketing organizations demand, diminishing their role in critical decision-making processes.
• Top talent gravitates towards innovation. Departments that fail to adopt cutting-edge AI tools may struggle to attract and retain the best in the field, further eroding their effectiveness and relevance.
• Budgets and resources are tightening for pharma commercial insights—embracing GenAI is a path to getting more from the investment your organization makes in research.

Broader Context & Concerns

From our industry benchmarking research, we know that GenAI is seen as the biggest trend for 2024 in pharma Insights & Analytics (I&A). There is massive pressure from stakeholders, senior management and even board members to do something with GenAI—and constant questions about creating and accessing the capabilities to operationalize it.

Yet these stakeholders themselves may not really understand the technology that they are asking I&A to implement and use. There are legitimate concerns around “hallucinations” and whether GenAI can be used to help pull disparate info together to “tell a story”…which is what stakeholders really want.

At the same time, I&A departments are increasingly coming under resource constraints, from both a research budget and headcount perspective. Organizations are demanding that market research dollars increase value for spend, and insights professionals are being asked to do more as department headcounts get capped or constrained.

From our own experience and from talking widely to (often frustrated) pharma knowledge workers, we know they often spend a disproportionate amount of time on foundational work such as searching for information, gathering data and putting together first drafts of presentations and documents. GenAI allows insights workers to focus their time on higher value work—drawing out meaningful insights, telling the story of what research is revealing and providing strategic recommendations.

The Questions That Matter

I&A team leaders are left with a set of challenging questions—how can they:

• Provide more immediate access to and summarization of brand research, even in its most granular form (e.g. customer verbatims)?
• Increase “institutional” memory of brand research and insights?
• Improve integration of research across projects, time and teams?
• Reduce duplicative research, saving time and money and still getting brand insights?
• Increase the amount of time their team spends on the “high value work” of drawing out insights and recommendations from brand research, thereby increasing productivity?

So Where Should I&A Leaders Focus?

Rapidly Deployable, Scalable Solutions: Pick solutions that can start small and scale easily as your organization embraces the technology. This approach provides a practical, low-barrier entry point into the world of GenAI, tailored to your unique market research needs.

Uncompromised Security and Privacy: Make sure any vendors you work with prioritize the security and privacy of your proprietary data. Solutions should operate within a secure, private GenAI environment, ensuring that your sensitive information remains confidential and accessible only to your team.

Acceptance of and Preparation for Change: Choose solutions that can pivot and change, as GenAI technologies will evolve rapidly. These cannot be “deploy once and forget it” solutions. Use vendors that can provide outside-in perspectives and have robust learnings from deploying solutions across many companies.

Human-in-the-Loop Technology: Mitigate risks–hallucinations and errors are the GenAI risks that are probably cited most. There are AI techniques that can drastically reduce these risks, enable humans to ensure accuracy and reliability, and provide feedback to improve any GenAI system constantly.

In short, implement quickly, scale as needed; focus on secure, private, proprietary, human-in-the loop technology and process to transform the way your insights organization works.

Author: Steven Laux