Differentiating your life sciences key account engagement process with generative AI

This article was co-authored with Brian Kiep, COO and product lead at Valkre Solutions. 

Gartner declared in 2023 that “Generative AI … has profound business impacts on content discovery, creation, authenticity and regulations, as well as customer and employee experiences. For sales, it can produce content in the form of emails, customer-facing content, meeting summaries, training content and role plays. Because of these use cases, Gartner predicts that B2B sales organizations using generative-AI-embedded sales technologies will reduce the amount of time spent on prospecting and customer-meeting prep by over 50% by 2026.”
 

This opportunity extends into the world of key account management (KAM) in the life sciences industry. In key account management, gaining timely insights, coordinating cross-functional customer engagement and being able to assess and communicate the impact delivered to customers is crucial and complex. We explore five generative-AI-driven use cases that can differentiate key account management across all phases of a typical customer engagement process, along with a few important steps to harness the power of KAM in life sciences. 

Key account management teams and the KAMs who coordinate them need a deep understanding of their account’s needs and priorities. Gen AI allows KAMs to focus on interpreting data rather than collecting it. By alleviating labor-intensive and time-consuming research tasks such as compiling account partnerships, gauging stakeholder sentiments, mapping key decision-makers and identifying top strategic priorities, Gen AI empowers KAMs to spend less time on information gathering and account profiling. This shift allows them to devote more energy to strategic thinking and collaboration with their cross-functional team members. Gen AI helps KAMs gain a deeper, more nuanced understanding of their customers, leading to more effective strategic planning and execution by analyzing patterns in proprietary KAM data.

Gen AI can help key account managers tailor solutions to the needs and priorities of the account. By harnessing AI-driven text analysis, KAMs can refine their engagement strategies, pinpoint potential risks and leverage opportunities with increased efficiency. This empowers teams to evaluate the alignment between KAM strategies and organizational objectives, identify best practices to scale and address the risks associated with KAM strategies.
 

Frontline managers and KAM leaders can leverage AI to analyze and summarize account plans across functions and therapy areas. Examples of how gen AI can support strategic decision-making by KAM leaders include suggesting to leadership the top unmet customer objectives, identifying the initiatives that are most impactful to customers and calculating benchmarks such as account health.
 

Gen AI offers insights for marketing and enablement teams on how different solutions are being used across customers. It identifies what could make value propositions more effective and use intelligence to create tailored solutions to meet specific customer objectives. As noted in our assessment of gen AI in the pharma omnichannel customer engagement space, “The content asset can be hyper-personalized to these micro-preferences [from healthcare providers or patients] and deployed for up to 40% better engagement rates on digital channels …”

For their mutually agreed upon solutions, key account managers can use Gen AI as an assistant to develop detailed implementation plans and define the right milestones. Gen AI is able to suggest the right cross-functional partners that need to execute on the various tasks of the program. Using historical plan and program data relevant to specific types of plans, it can identify leading and lagging indicators of success that are used to track specific milestones. Negotiations with customers often happen in this phase. Many companies are now using AI customer avatars to practice and simulate conversations with customers. This could be a powerful use case for offerings like ZS’s Virtual Rep and Manager Trainer to enable that practice.

A survey of the Strategic Account Management Association (SAMA) Community found that 88% of KAMs spend more time on internal work than customer-facing activities. Any time saved doing customer research, pulling together financial projections, building account plans, gaining internal alignment, running meetings, generating reports and more means more time for KAMs to focus their energy on strategic initiatives and relationship-building activities. This increased efficiency would not only improve individual performance but also enhance overall organizational effectiveness.
 

Gen-AI-driven solutions that automate or simplify these daily internal activities at the point of need have the potential to improve the balance of strategic work internally and externally. Burdensome tasks such as assembling content for an account plan review, writing up and sharing meeting minutes or making suggestions to cross-functional team members on what work should be done next to achieve collective goals are all examples of work ready for AI to simplify.
 

AI-powered solutions also play a crucial role in compliance monitoring and risk mitigation. By continuously monitoring activities and data inputs, AI can flag potential compliance risks in real time. For instance, it can identify sensitive information in communications and prompt KAMs to take corrective actions. This proactive approach ensures adherence to regulatory standards, mitigates risks and fosters a culture of compliance within the organization. 

Tracking and measuring KAM programs and engagements has been a long-standing challenge for life sciences companies. KAM data sets go beyond what is traditionally captured in a CRM system and often require pulling data from many disparate sources. This data can range from structured data such as account profiles, semi-structured data such as stakeholder relationship and voice of customer data, to unstructured data such as plan and program descriptions—all of which are used to inform and track various leading, lagging and shared indicators of success. Gen AI can simplify and streamline drawing insights from these disparate sources across the overall KAM program, as well as for individual customers, catering to KAMs, KAM leaders and marketers.

Getting excited about the gen AI possibilities in KAM is easy, but executing on this promise requires an asset that most life sciences companies do not have: A KAM data set. It’s a challenge that Jerry Alderman, Valkre Solutions CEO, has summarized. In short, the life sciences companies investing in purpose-built KAM technology are best positioned to take advantage of the gen AI benefits in the coming years. Gartner agrees, noting that account planning and account-based marketing technologies have reached the “slope of enlightenment” and will be mainstream applications within the next two years.
 

To harness the full potential of AI in KAM, life sciences companies need to take the following steps:

1. Generate proprietary data: Set up best-in-class account planning and management infrastructure for KAMs and their cross-functional partners to set the right foundation to capture data.
2. Integrate the data sets: Define the right data models to bring together public as well as proprietary data to serve as a foundation for AI enablement.
3. Define AI use cases: Develop AI use cases, keeping in view your organizational maturity based on your proprietary data sets and processes. Focus on what will help you create competitive advantages.
4. Foster a continuous improvement mindset: Bring people, processes and technology together in a continuous improvement framework to adapt and evolve with changing needs.

Gen-AI-driven insights and efficiency tools offer transformative benefits to life sciences’ KAM programs across the spectrum of customer engagement activities. By leveraging these technologies, organizations can empower their customer-facing teams to navigate complexities more efficiently, ultimately driving better outcomes and strengthening relationships with key account customers. And while the hype is exciting, the life sciences companies that are currently focused on getting the right data, processes and systems in place will be the ones best positioned to create sustainable competitive advantage using generative AI.