Life sciences CRM: Five considerations for a strategic decision

The Veeva–Salesforce split marks a rare inflection point—one that’s pushing every life sciences company to rethink not just its CRM platform but also the role CRM should play in powering growth and customer success.

This isn’t just a platform decision. It’s a once-in-a-decade opportunity to reimagine your end-to-end CRM ecosystem as a smarter, more adaptive foundation—one built around customers, not systems.
 

What’s at stake:

• Sticking with the old model means missing the real opportunity: personalized engagement at scale. The industry is moving toward a new kind of commercial model—one that uses AI to personalize experiences and connect with customers in the ways they want. Companies that adopt this model won’t just keep up, they’ll pull ahead. ZS finds personalized engagement at scale boosts impactable revenue by 4% to 8%, with potential for more based on independently validated client benchmarks.
  
• Failing to balance innovation and simplicity puts global consistency at risk. Global CDIOs have a tough balance to strike. They must support early adopter or lead markets that want the freedom to innovate and support various customer-facing roles within the same systems. At the same time, they need to serve foundational markets that support just a few customer-facing roles that rely on simplicity and scale. Any new solution must work for both. It should be easily adopted, leverage common data and technology foundations, harmonize business processes and drive consistency in customer experiences. Your CRM evolution can’t be too rigid for your lead markets or too complex for the rest.
  
• This is the moment to consider how everything connects, even if the current operating model hasn’t yet shifted. Your decisions today go beyond choosing a CRM platform vendor. They lay the groundwork for how your teams will operate in the future. Thinking through how data and apps will integrate with your CRM, how AI will fit into core processes and how teams will work going forward helps you make a smarter, more strategic vendor choice. If you miss this moment to align it all, you risk delayed impact, limited innovation and choices that are costly to unwind.

In most life sciences organizations, CRM is still treated as a utility—used mainly for reporting and compliance, with limited adoption. But in our opinion, that’s a missed opportunity. With the right strategy and investment, CRM can become a true differentiator— one that not only enables personalized engagement at scale but also speeds up product and launch strategies and turns data and AI into an advantage.

In this article, we’ll show you a new way to view CRM as a connected system for managing every customer touch point. We’ll then share what to consider before you make your ultimate platform decision.

Our goal is to help you lock in a CRM strategy built to last—and drive real adoption—for the next decade and beyond.

We believe the CRM should move beyond being a standalone system of record. It needs to become a network of connected systems that are loosely coupled yet tightly aligned to work together seamlessly.

The basic ideas that shape a customer-first CRM—context, intelligence and activation—are the ideas behind these systems. The right contextual data drives intelligence. Intelligence shapes the user experience. And the user experience drives action and better outcomes (See Figure 1).

The concepts of context, intelligence and activation provide a common language for architecting a customer-first CRM. Here’s how to think about these systems and their roles in the architecture:

The CONTEXT system includes connected data, master data management and other data elements to capture and curate insights that are unique to a healthcare provider (HCP) or patient and their CONTEXT. Contextual insights help uncover the reasons behind behaviors. For example, a doctor might favor treatments with fewer side effects—not solely for clinical reasons but because that office cares for elderly patients who struggle with treatment adherence. The CONTEXT system is designed to continuously update and enrich analytic attributes, creating a dynamic and ever-expanding universe of information for the intelligence system to consume.

The intelligence system includes a composable suite of applications that become the intelligence engine. One example here could be a group of agents that pair rep-surfaced HCP pain points from the CONTEXT stream with patterns in secondary data that can activate a workflow.

Finally, the activation system employs generative and agentic AI to streamline workflows across customer-facing roles. For example, it can suggest next steps based on context, much like Next Best Action tools. It could also include a voice-to-text feature that summarizes customer calls in a compliant way, with summaries tailored to different roles and fed back into the CONTEXT data stream.

See Figure 2 to visualize these systems and how they work together as part of a connected system.

Choosing the right path for a customer-first CRM can help you build the foundation around the customer—and set you up to adapt and scale overtime.

Here are five things to consider as you lock in a CRM strategy built to last:

1. Artificial intelligence at the core

The goal: The CRM shifts from repository to a responsive, intelligent system.

As part of this shift, your solution must be able to:

• Uncover real customer barriers. Your CRM should leverage data and AI investments in a continuous and connected way. Acting on a few barriers that are both important to the customer and to your business allows for simpler technical and operational execution.
• Generate new contextual insights for adaptive intelligence. In this world, the CRM becomes both a net consumer and generator of data and insights about those barriers. It must provide a continual feed of new contextual insights for an intelligence layer to consume.
• Provide seamless, high-quality data flow. You’ll likely need to close gaps in how data flows between the CONTEXT and intelligence systems. Focus on removing blockers that limit high-quality data flow or real-time interoperability. Watch for limitations that restrict personalization at scale or introduce system-specific dependencies that could hinder regional or global deployment.
• Master signal-to-action. The ability to triage and prioritize reducing noise is critical. You can’t send too many signals or the wrong ones. Your solution must be able to prioritize both market-level patterns and individual customer signals.

2. Process reimagination, with agent support

The goal: The CRM shifts to orchestrate interactions around the customer, not around functional processes.

As part of this shift, your solution must be able to:

• Design workflows around personas, not processes. Journeys should follow the customer’s logic, not internal processes or org charts. These journeys serve as playbooks to orchestrate actions, channels and content at the right moments.
• Reimagine workflows with autonomous agents. When evaluating a vendor, look for the robustness of AI agents to support key workflows. This could be, for example, the ability to integrate seamlessly with marketing automation or analytics tools.

3. Consumer-grade experiences, built for intuitive adoption

The goal: People’s workflows are personalized so they can advance customers and patients on their journeys.
 

As part of this shift, your solution must also deliver a connected, personalized user experience. Employee adoption is a key part of improving CRM. With connected data and AI, users can get personalized dashboards and workflows based on what they need, not a one-size-fits-none view. They can get autoconfigured dashboards that highlight what’s relevant to their function, territory or customer tier. Co-pilots can surface key context and next steps for them to eliminate tab-hopping. Learn what your primary vendor has planned in your decision-making process.

4. Cost efficiency

The goal: Empower the technology organization to optimize long-term capital and operating expenditures through strategic partner and vendor negotiations.
 

When it comes to cost implications, you will need to evaluate:

• Capability evolution: Make sure your CRM vendor’s capabilities align with your broader data strategy to avoid point solutions that lead to costly duplication or rework down the line.
• Using out-of-the-box or custom functionality: Knowing how much out-of-the-box functionality you can use can help minimize costly custom builds now and avoid expensive rework later. Consider this carefully to ensure scalability without added development overhead.
• The benefits of a loosely coupled yet tightly aligned architecture. Explore how a services-first approach can reduce costs. A federated model, like our vision for CRM, lets you connect systems without costly consolidation. This approach reduces infrastructure spending and can also speed up the rollout of enhancements across platforms. It’s an opportunity to make your tech environment more modular—not just for the cost benefits, but for the added agility, scalability and innovation it can bring to your organization.

5. CRM as a driver of strategy, not just standalone software

The goal: The broader organization understands how CRM contributes to the strategic mission and is committed to drive it forward.

As part of this shift, your leaders should agree on:

• How the vision for CRM aligns with business goals. When making your case for change, examine the potential to either boost revenue for brands or improve clinical research goals. In our estimates, shifting toward a customer-first CRM could boost impactable revenue by 4% to 8%.
• Enterprise-level funding. Treat CRM like infrastructure and plan for enterprise-level funding with a central budget and shared investment across multiple years. Funding CRM from siloed budgets can foster turf battles over functionality, resourcing or roadmap prioritization. More critically, it fragments customer data and limits the ability to generate actionable insights, ultimately undermining your broader strategic objectives.
• How to drive behavior changes through strategy. Make sure your program delivers a clear, strategy-aligned message, paired with hands-on training that connects to what users care about and how they’re measured. Provide specific guidance on the AI-related skills employees need to develop and the behaviors you expect to see in a dynamic, AI-supported environment.
  
• Support a scalable innovation engine. Your solution should also enable rapid incubation of new ideas, with clear criteria for progressing from pilot to scaled deployment. This ensures that promising innovations don’t stall and can be operationalized quickly across regions and teams.

A customer-first CRM doesn’t need to be built all at once, but it does need to start with a clear vision. Data and tech leaders can get agreement and start paving the way now.

Questions? Reach out to your ZS team or join our webinar explaining more about our customer-first approach to commercial success.