Quantifying generative AI’s impact on commercial life sciences

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Life sciences leaders are eager to leverage generative AI in commercial functions, including sales and marketing. Their challenge? Identifying, prioritizing and connecting the use cases that will drive the most value and ROI for the organization and the people it serves.

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One way life sciences firms can identify high-impact commercial use cases is by exploring options through the lens of time as a product-the idea being that use cases that save time and resources are easier to quantify and make a clearer business case, laying the foundation for broader transformation and scale.

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High-value commercial generative AI use cases

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Let's highlight four use cases that exemplify this time-as-a-product concept and explore how they can be connected to maximize value for life sciences organizations.

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1.    Localizing marketing materials

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Content generation is one of the most common use cases for generative AI. Take, for example, Pfizer's Charlie, a generative AI platform introduced earlier this year to help the company's marketing team overhaul its content supply chain.

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Charlie can assist in all aspects of content creation and editing, as well as fact-checking and legal reviews. The platform can also help personalize campaign elements by altering ad headlines, communication copy, and other asset details based on recipients' needs, preferences, situation and experience.

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One of the major areas of opportunity we see for similar tools is in localizing content for different markets. This could include translating copy or making materials more culturally relevant through optimal imagery and design choices.

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Traditionally, these localization efforts have been largely manual, requiring significant time and resources. With the help of generative AI, life sciences commercial teams can feed marketing materials into a global template and, within minutes, convert these assets into ads appropriate for local consumption in virtually any market. In our experience, using generative AI in this way can help increase productivity by approximately 30% and reduce agency and marketing operations spend by 40%. 

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2.    Enhancing call center operations and customer support

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At the 2024 World Economic Forum in Davos, Switzerland, Paul Hudson, the CEO of French pharma firm Sanofi, discussed how the company rolled out AI-enabled tools to 11,000 employees. He framed the decision to equip people with intelligent solutions as a way to accelerate "the journey to more meaningful work," adding that people "don't want to do PowerPoint, they want to be weaponized and amplified."

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Augmenting and enhancing human workers is precisely the role I believe generative AI should play within the customer support function.

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For example, life sciences companies can use AI and generative AI to act as a service agent companion, providing real-time prompts and scripts to help agents manage requests and provide consistent, complete and accurate answers to complex questions based on the company's knowledge repositories. These tools can also monitor caller sentiment to adjust responses in real time based on their tone of voice and word choice.

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Huge gains are to be made by companies that leverage generative AI across call center operations and customer support channels, whether as a companion tool or self-service channel. In working with our clients, we often report sizable increases among key metrics, including quality of life, customer effort score, and trust factor, when life sciences firms integrate intelligent tools within service workflows. 

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Life sciences leaders are eager to leverage generative AI in commercial functions, including sales and marketing. Their challenge? Identifying, prioritizing and connecting the use cases that will drive the most value and ROI for the organization and the people it serves.

One way life sciences firms can identify high-impact commercial use cases is by exploring options through the lens of time as a product-the idea being that use cases that save time and resources are easier to quantify and make a clearer business case, laying the foundation for broader transformation and scale.

High-value commercial generative AI use cases

Let's highlight four use cases that exemplify this time-as-a-product concept and explore how they can be connected to maximize value for life sciences organizations.

1.Localizing marketing materials

Content generation is one of the most common use cases for generative AI. Take, for example, Pfizer's Charlie, a generative AI platform introduced earlier this year to help the company's marketing team overhaul its content supply chain.

Charlie can assist in all aspects of content creation and editing, as well as fact-checking and legal reviews. The platform can also help personalize campaign elements by altering ad headlines, communication copy, and other asset details based on recipients' needs, preferences, situation and experience.

One of the major areas of opportunity we see for similar tools is in localizing content for different markets. This could include translating copy or making materials more culturally relevant through optimal imagery and design choices.

Traditionally, these localization efforts have been largely manual, requiring significant time and resources. With the help of generative AI, life sciences commercial teams can feed marketing materials into a global template and, within minutes, convert these assets into ads appropriate for local consumption in virtually any market. In our experience, using generative AI in this way can help increase productivity by approximately 30% and reduce agency and marketing operations spend by 40%.

2.Enhancing call center operations and customer support

At the 2024 World Economic Forum in Davos, Switzerland, Paul Hudson, the CEO of French pharma firm Sanofi, discussed how the company rolled out AI-enabled tools to 11,000 employees. He framed the decision to equip people with intelligent solutions as a way to accelerate "the journey to more meaningful work," adding that people "don't want to do PowerPoint, they want to be weaponized and amplified."

Augmenting and enhancing human workers is precisely the role I believe generative AI should play within the customer support function.

For example, life sciences companies can use AI and generative AI to act as a service agent companion, providing real-time prompts and scripts to help agents manage requests and provide consistent, complete and accurate answers to complex questions based on the company's knowledge repositories. These tools can also monitor caller sentiment to adjust responses in real time based on their tone of voice and word choice.

Huge gains are to be made by companies that leverage generative AI across call center operations and customer support channels, whether as a companion tool or self-service channel. In working with our clients, we often report sizable increases among key metrics, including quality of life, customer effort score, and trust factor, when life sciences firms integrate intelligent tools within service workflows.

3.    Enhancing the customer experience through proactive engagement

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Generative AI isn't just for enhancing traditional inbound and outbound customer communications. It's also a way to enable proactive engagement.

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One leading medtech company that serves more than one million people with diabetes leveraged conversational texting tools to send automated reminders to people when it was time to reorder medication. The success of this program, which improved reorder rates by 11%, prompted the company to adapt and expand the platform to serve people in other ways as well, such as by replacing or upgrading devices, confirming shipping information and ensuring continuity of care during daylight savings time changes.

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In call centers, the focus has always been on "push channels." But now, with the advancement of generative AI, it's possible for companies to begin to position these channels as pull mechanisms, drawing people into a deeper level of engagement that inspires higher satisfaction and better outcomes. 

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4.    Streamlining marketing compliance workflows

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As noted above, generative AI has a key role to play in content localization. And one of the most consequential elements of content localization for life sciences companies is compliance.

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AI can audit promotional content against compliance and design guidelines, identifying which aspects of the communication adhere to local regulations and which will need to be adjusted. The tools can then make recommendations on how to revise materials, even presenting mockups that humans can review and iterate on. 

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AI-enabled content tools can also evaluate marketing strategies and ensure the proper data standards are upheld, as well as any relevant regulations related to personalized marketing, especially on digital channels.

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For example, the US and the European Union have markedly different rules when it comes to the use and collection of customer data. Likewise, the acceptable use of social media channels varies from country to country. Rather than relying on human workers to maintain an ongoing, intimate knowledge of these various regulations, an AI-enabled tool can lead this function, flagging potential violations or areas humans should investigate more closely.

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For life sciences companies, this capability is not only a huge productivity driver, but also a way to reduce risk. After all, compliance is a rules-based exercise-and, when trained properly, computers are typically far better than humans at adhering to complex rules.

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Putting it all together: connecting the generative AI strategy through time as a product

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While we examined four distinct use cases that can drive cost savings and resource optimization, it's important to recognize that, when executed as part of a holistic commercial strategy, the collective impact of these use cases will vastly outperform their individual benefits.

\r\n

Further, AI use cases should generally be thought of as part of a holistic program. In this case, they represent a progressive approach, where the core capabilities in one area can be applied to the next step and so on.

\r\n

For example, an initial AI use case may use a generative AI-enabled tool to localize content campaign elements. Building on that, the company can then integrate compliance workflows and ensure local regulatory standards are also met. Some of those campaign elements may aim to drive traffic to service channels, including AI-enabled self-service chats or service agents that are equipped with a companion tool.

\r\n

These initial interactions could also transition into a proactive engagement if the AI-enabled tool identifies a natural and appropriate avenue to advance the conversation.

\r\n

In this way, each of these use cases is part of the commercial strategy-and a critical part of the future of life sciences.

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3.Enhancing the customer experience through proactive engagement

Generative AI isn't just for enhancing traditional inbound and outbound customer communications. It's also a way to enable proactive engagement.

One leading medtech company that serves more than one million people with diabetes leveraged conversational texting tools to send automated reminders to people when it was time to reorder medication. The success of this program, which improved reorder rates by 11%, prompted the company to adapt and expand the platform to serve people in other ways as well, such as by replacing or upgrading devices, confirming shipping information and ensuring continuity of care during daylight savings time changes.

In call centers, the focus has always been on "push channels." But now, with the advancement of generative AI, it's possible for companies to begin to position these channels as pull mechanisms, drawing people into a deeper level of engagement that inspires higher satisfaction and better outcomes.

4.Streamlining marketing compliance workflows

As noted above, generative AI has a key role to play in content localization. And one of the most consequential elements of content localization for life sciences companies is compliance.

AI can audit promotional content against compliance and design guidelines, identifying which aspects of the communication adhere to local regulations and which will need to be adjusted. The tools can then make recommendations on how to revise materials, even presenting mockups that humans can review and iterate on.

AI-enabled content tools can also evaluate marketing strategies and ensure the proper data standards are upheld, as well as any relevant regulations related to personalized marketing, especially on digital channels.

For example, the US and the European Union have markedly different rules when it comes to the use and collection of customer data. Likewise, the acceptable use of social media channels varies from country to country. Rather than relying on human workers to maintain an ongoing, intimate knowledge of these various regulations, an AI-enabled tool can lead this function, flagging potential violations or areas humans should investigate more closely.

For life sciences companies, this capability is not only a huge productivity driver, but also a way to reduce risk. After all, compliance is a rules-based exercise-and, when trained properly, computers are typically far better than humans at adhering to complex rules.

Putting it all together: connecting the generative AI strategy through time as a product

While we examined four distinct use cases that can drive cost savings and resource optimization, it's important to recognize that, when executed as part of a holistic commercial strategy, the collective impact of these use cases will vastly outperform their individual benefits.

Further, AI use cases should generally be thought of as part of a holistic program. In this case, they represent a progressive approach, where the core capabilities in one area can be applied to the next step and so on.

For example, an initial AI use case may use a generative AI-enabled tool to localize content campaign elements. Building on that, the company can then integrate compliance workflows and ensure local regulatory standards are also met. Some of those campaign elements may aim to drive traffic to service channels, including AI-enabled self-service chats or service agents that are equipped with a companion tool.

These initial interactions could also transition into a proactive engagement if the AI-enabled tool identifies a natural and appropriate avenue to advance the conversation.

In this way, each of these use cases is part of the commercial strategy-and a critical part of the future of life sciences.

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