Evaluating opportunities and challenges shaping the future of life sciences’ content: Content Supply Chain 2.0

28 Sep 2023

In today’s experience economy, creating memorable customer interactions has become vital. To uplift these interactions, organizations must deliver top-quality, channel-appropriate, and personalized content at scale. Although the life sciences industry has made great strides in this area by implementing centralized, global-to-local content operations, a lot still needs to be achieved to make the supply chain future-ready.

At the Indegene Digital Summit, Libby Driscoll from Eli Lilly, along with Michael Kurr and Michael Schropp from Boehringer Ingelheim, discussed the progress they have made in the content space and shared strategies and possibilities they see for the future.

Here are the key takeaways from the session.

Building a future-ready content powerhouse can bolster content efficiency, consistency, and scale

In today’s day and age, delivering generic and run-of-the-mill content to customers is a cardinal sin that can truly damage a brand’s reputation. At the same time, providing personalized content at scale and promptly is not an easy task either. To address this complexity, Michael Kurr recommended building an agile and responsive global content infrastructure. This infrastructure should combine modular content methodologies with an automated MLR review framework and a data-driven feedback loop to define the next-best action. He added that such an agile infrastructure can empower organizations to withstand unforeseeable disruptions, adapt quickly, and allow the free flow of content and metadata from a global to local scale, enabling the delivery of compliant, consistent, and personalized content across geographies, regions, and markets.

You need to have an agile and responsive global content infrastructure that allows your metadata and tags to travel with the content, so that your core claims and messages are not diluted when content travels from global to local

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– Michael Kurr

Reality check: GenAI is not a cure for all your content challenges

Generative AI is perceived to be this occult panacea that can solve anything. From deriving valuable insights and patterns from huge amounts of data and refining the Medical, Legal and Regulatory (MLR) review process to creating concise summaries from large text corpora and more, there is no denying GenAI’s ground-breaking potential to transform life sciences content as we know it. But one still needs to be pragmatic and wary of its shortcomings. Having successfully initiated and implemented a Gen-AI powered content infrastructure at Boehringer Ingelheim, Michael Schropp urged organizations to maintain a realistic outlook regarding GenAI and its capabilities. He also highlighted the importance of human oversight in training and refining AI models, so they deliver on what is expected of them. By setting realistic expectations, finding the right use cases and being mindful of regulatory or ethical concerns, organizations can leverage GenAI to drive commercial success and transform customer engagement.

GenAI is not a magic solution. Although it has tremendous potential to transform content, we should be realistic about what it can and cannot do… We need to keep refining it and make sure that there's always a human oversight.

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– March Schwartz

A captive GenAI framework that is continually trained is vital to building a competitive edge

With everyone jumping on the GenAI bandwagon and trying to use tools such as ChatGPT and Bard to create content for the ‘same audience’, there is a growing uneasiness among industry pragmatists regarding gaining and retaining their competitive edge in the market. This unsettling feeling is intensified by the poorly defined data privacy and security norms, and several unanswered questions around transparency, accuracy, and accountability issues.

While we all want to make the most of this amazing technology, we must remember that it can only be as smart, compliant, and accurate as we make it. Commenting on how organizations can continue to differentiate themselves, Libby Driscoll recommended building a captive GenAI model that is continually trained using in-house materials, claims, and governance standards. She also highlighted the merit of finding ways to assimilate an external perspective to train these models, albeit in an ethical manner. Backing all this with an R&D mindset that bolsters a culture of experimentation and innovation, appropriate systems, an army of skillful practitioners, and robust governance guidelines can enable organizations to make the most of the technology while transforming the content they deliver.

We are not going to put our confidential information in ChatGPT. Organizations will have to create their own internal infrastructure to create a continuous learning environment for AI so that you are bringing in that external lens as well as training it on many of your own materials, claims and quality standards that you have.

Lorem

– Libby Driscoll

So, how does a future-ready content supply chain look like?

A futuristic content supply chain is technology-led, data-driven, and agile. As technologies like Generative AI get ramped up and present life sciences organizations with numerous opportunities to expand and evolve their content operations, the power of human oversight will remain unmatched, at least in the foreseeable future. By taking a leap of faith and experimenting with these technologies, being cognizant of their risks and devising contingencies to mitigate them, and most importantly, building an agile technology-powered content infrastructure that powers adaptability, and responsiveness, organizations can achieve scalable content personalization that earns them a top spot in the customer experience race.

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