Everest Group has placed ZS at the very top of its 2025 Life Sciences AI and Analytics Services for Commercial PEAK Matrix®, naming the firm a Leader and ranking it highest among all evaluated providers. For an industry racing to turn AI ambition into real commercial results, the distinction signals more than brand strength—it highlights which partners are actually operationalizing AI across the life sciences value chain.
The report evaluated 30 service providers on market impact, vision, and capability, with a specific focus on how AI, advanced analytics, and consulting services are being applied to commercial life sciences functions. ZS’s top placement suggests that, in Everest Group’s view, the firm is not just experimenting with AI, but embedding it deeply into how pharmaceutical and medtech companies plan launches, engage customers, and make decisions at scale.
Why This PEAK Matrix® Matters
Life sciences companies are under intense pressure. Patent cliffs are looming, launches are more complex, healthcare providers are harder to reach, and regulators are watching AI use closely. At the same time, generative and agentic AI are promising faster insights, better targeting, and more personalized engagement—if they can be implemented responsibly and effectively.
Everest Group’s Commercial PEAK Matrix® zeroes in on that challenge. Rather than evaluating AI in isolation, the assessment looks at how service partners support end-to-end commercial activities, including:
- Launch planning and forecasting
- Pricing and market access
- Omnichannel marketing and engagement
- Sales force effectiveness and enablement
- Patient and provider engagement
In short, it asks a practical question: Who can actually help life sciences companies turn AI into commercial advantage?
What Defines a “Leader,” According to Everest Group
In the 2025 report, Everest Group characterizes Leaders as firms that go beyond point solutions or pilot projects. Leaders typically demonstrate:
- Comprehensive commercial coverage, spanning multiple functions rather than siloed use cases
- Operationalized generative and agentic AI, such as next-best-action engines, automated insight generation, and AI-driven decision support
- Strong platform integration, especially with major CRM, cloud, and data ecosystems used across pharma and medtech
- Scalability, enabling AI solutions to move from proof of concept to enterprise-wide adoption
ZS’s highest-in-Leader placement indicates it met—and exceeded—these criteria relative to its peers.
ZS’s Differentiators: Platform, Consulting, and Scale
Everest Group highlighted several factors that pushed ZS to the top of the rankings, starting with its proprietary technology.
At the center of ZS’s offering is ZAIDYN®, the firm’s AI-powered analytics platform. ZAIDYN combines advanced modeling, recommendations, and insight generation to support decisions ranging from field force deployment to customer engagement strategies. Unlike standalone analytics tools, the platform is designed to sit directly within commercial workflows, helping teams act on insights rather than just review dashboards.
But Everest Group’s analysis suggests technology alone wasn’t the deciding factor.
ZS’s consulting-led delivery model also played a major role. The firm blends its platforms with advisory services to help clients define the right problems to solve, align stakeholders, and prioritize AI use cases that actually move the needle. In an industry where AI projects often stall due to unclear ownership or overambitious scope, that combination of strategy and execution matters.
The report also points to ZS’s partner ecosystem, which allows it to deliver modular or fully integrated solutions across leading cloud, CRM, and data platforms. This flexibility is increasingly important as life sciences companies try to modernize without ripping out existing systems.
Finally, Everest Group emphasized ZS’s broad client coverage. The firm works with organizations across the commercial maturity spectrum—from emerging biotech companies preparing their first launches to global pharmaceutical giants optimizing complex, multi-market operations. That breadth provides both scale and real-world validation of its AI and analytics capabilities.
Real-World Applications, Not Just Theory
The PEAK Matrix® assessment includes concrete examples of how ZS’s AI and analytics capabilities are being applied in practice.
In one case, a global pharmaceutical company used an AI-enabled sales training simulator developed with ZS. The system replicates real provider interactions, allowing sales teams to practice conversations and receive personalized, AI-driven feedback. Rather than generic training modules, the simulator adapts to individual performance, helping reps build skills more efficiently.
In another example, ZS supported a medtech company facing declining demand in a mature product category. By applying advanced analytics to consumer behavior data, ZS helped uncover new insights that informed a refreshed commercial strategy and improved customer engagement—demonstrating how AI can support not just growth, but course correction.
These kinds of applied use cases help explain why Everest Group viewed ZS as strong in translating AI potential into measurable outcomes.
The Bigger Picture: AI Moves Into the Commercial Core
ZS’s recognition comes at a moment when AI in life sciences is shifting from experimentation to expectation. Early efforts often focused on isolated analytics projects or back-office automation. Today, the focus is squarely on commercial impact—how AI can improve launch success, increase sales effectiveness, and deepen engagement with providers and patients.
Everest Group’s 2025 PEAK Matrix® reflects that shift, and ZS’s top ranking suggests the firm is aligned with where the market is headed: toward AI that is embedded, scalable, and tightly linked to business decisions.
For life sciences leaders evaluating partners, the message is clear. The competitive edge won’t come from AI hype alone, but from providers that can combine data, platforms, and consulting into systems that actually work in the real world.
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