Generative artificial intelligence across life sciences marketing and key account engagement

When we talk about creating value across businesses in today’s competitive landscape, generative artificial intelligence (AI) has the potential to turn thought into performance. 

Generative AI in life sciences marketing 

Generative AI is no longer an afterthought when it comes to reshaping how organization approach marketing. The technology is changing the pace of content creation, the depth of customer engagement, and the economics of modern go-to-market strategy.

What organizations are learning, though, is that technology alone does not drive the transformation. The shift demands investment in operating models and change management in equal measure. Without both, AI tools remain stuck at the pilot stage, without any tangible results. 

Use cases for generative AI in life science marketing

From writing hyper-focused customer-first content to building strategies and creating content derivatives, gen AI tools are empowering life science marketers to build better relationships with their customers. However, it is only a small number of marketers who are adopting and leveraging these tools, while many struggle to embrace the shift due to:

• Lack of AI literacy and a knowledge gap, which results in the fear of implementing AI tools

• When activities are outsourced to agents, there is a higher chance of privacy and compliance complications, which can take years to resolve.

• Despite the upgrades, AI outputs may be prone to inaccuracy.

• Lack of direction, especially if the leaders are unfamiliar with the tools

Despite understanding its value, many life science marketers are still stuck in the loop of understanding its effectiveness in real-world jobs. Only 20% of companies are investing in integration, rethinking and training talent and reengineering their system beyond just watching, testing and waiting. The following use cases continue to define the gen AI agenda.

Accelerating concept creation

Life science leaders, often supported by artificial intelligence consulting partners, are introducing gen AI pilots for concept ideation and creating quality content in half the time it takes for agencies to do so. These instances have helped several leaders save agency fees and cut iteration cycles. 

In-house content generation

Once a campaign is finalized, many organizations are looking inward for content production, refining copy, imagery, and channel specifications without going back to the agency for every change. Leaders moving in this direction expect savings of 20% to 30% on agency spend and are now looking forward to improving in-house capabilities supported by AI-enabled workflow tools.

Medical legal review (MLR) process reengineering

Medical-legal review remains one of the most persistent and frustrating bottlenecks for chief marketing officers (CMOs). Marketers often describe it as a black box with several cross-functional stakeholders and lower prioritization. Several organizations are experimenting with a multi-tier MLR concept review to create low-risk derivative assets. Reengineering is necessary to align MLR processes with new AI capabilities to drive results. 

Integrating data for insight mining

Meaningful insight will come when structured and unstructured data are brought together and placed directly in the hands of marketers. Some organizations are already moving in this direction, integrating call center data with research transcripts and secondary data so that marketers can get real-time answers.

Despite these use cases, the primary barrier to gen AI adoption is mindset. Many marketers understand the theoretical value of gen AI but are unsure how to make it part of their daily workflow. Addressing that uncertainty through training, upskilling and aligning leadership is the key to unlocking its full potential.

Gen AI in life sciences key account engagement

The impact of generative AI extends well beyond marketing. 

Key account management (KAM) is one of the most relationship-driven functions in life science. The system is in place to help leaders develop long-term relationships with the most valuable clients. But with the advent of gen AI, it is beginning to change how teams prepare, engage and deliver value. This is further explained in the following use cases:

Understanding key account needs

Gen AI allows KAMs and their teams to identify and interpret data seamlessly, turning a labor and time-intensive research task into a strategic partnership. Instead of mapping decision-makers, their sentiments and identifying strategic priorities, gen AI helps teams to spend more time planning strategically and collaborating where it actually derives value.

Tailoring solutions

AI-driven analysis helps KAMs improve their engagement strategies, identify risks early, and align solutions more precisely with account objectives. Managers and KAM leaders can use AI to summarize account plans, identify best practices, identify customers’ unmet needs, and initiatives towards fulfilling those needs across therapies and benchmarks.

Personalizing content at scale

Both medtech and pharma are using AI customer avatars to develop an understanding of customer demands. With AI, organizations can personalize content to cater to the specific preferences of customers.

Streamlining implementation and compliance for the teams

Gen AI can help KAMs develop strategic initiatives and focus on relationship-building activities, rather than generating reports manually, sharing minutes of meetings (MOM), building account plans and other manual work. By automating these daily internal tasks, gen AI can help teams allocate their time to achieving goals and implementing plans that enhance organizational effectiveness. Gen AI can also help monitor data inputs, flag compliance risks in real time, and protect sensitive information actively.

Measuring program performance

KAM datasets span across structured, semistructured and unstructured data, from account profiles to customer data and plan and program descriptions. Gen AI can draw coherent insights from these sources, allowing teams to establish performance indicators. 

While these use cases define the potential of generative AI, turning it into performance is where most organizations need support. The life sciences companies, relying on business technology consulting partnerships, are building the right account planning and management infrastructure, marketing frameworks and fostering continuous improvement among their team members stand to lead this transformational curve. Companies who align their workflow with technology investments, rather than treating AI as a standalone initiative, will find it easier to move from early wins to enterprise-wide impact.

Implementing generative AI in life sciences does not mean replacing human judgement. The technology can allow leaders to build better relationships and drive better outcomes. 

About Author

The author has worked with leading life science organizations. She has cited multiple articles on generative AI and the positive changes it can bring if utilized proactively. More recently, her work has focused on understanding new AI technologies and how they’re impacting the evolving healthcare ecosystem.

Powered by Froala Editor