Choosing the right life sciences intelligence platform

Your checklist for long-term success

Choosing a platform is about more than features. It’s about how easily your teams can use it, how well it fits into your operations and how well it enables you to scale across regions and markets. The right platform should deliver measurable value by accelerating adoption, maximizing team utilization and ensuring that your investment translates into real business outcomes.

This checklist offers considerations and questions to help you find the platform that will scale with you for the long haul. Download the full version to learn why these capabilities are essential to delivering change and impact across your life sciences organization.

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What to look for

Core capabilities

What to ask
A true platform, not just a custom build

A true platform delivers regular enhancements to all clients through shared, versioned releases. When updates are handled separately for each implementation, it leads to delays, inconsistent experiences and missed opportunities to benefit from innovation.

Embedded life sciences intelligence

Out-of-the-box life sciences-native platforms are built on decades of domain experience, which reduces onboarding time, accelerates time-to-market, improves accuracy and ensures compliance without heavy lifting.

Scalability and performance

Global teams need systems that scale without slowing down. If performance degrades with volume, growth becomes a risk. Make sure the platform is cloud-native, enterprise-ready and can handle the data from all the systems you work in.

AI that is embedded and domain-trained

Generic AI products lack context. Domain-specific AI is built for real-time decisions and regulated environments. It works as a coordinated system in the products you already use and delivers clear insights, smart recommendations and compliant execution—all in one governed environment.

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Is it a true platform?
Do all clients receive regular platform upgrades and enhancements, or are updates handled separately for each implementation?
Is intelligence embedded?
How is your platform preconfigured for life sciences workflows, best practices and compliance?
Is it scalable?
Can the platform handle a high volume of data in real time without performance issues? Are there constraints due to Salesforce overlays or legacy architecture?
Can we trust its insights?
How is AI embedded in your platform? Is it trained on life sciences data and integrated into your workflows? Can it leverage our data?
What to plan for

Enterprise readiness

What to ask
CRM and ecosystem interoperability

You shouldn’t have to rebuild your ecosystem to adopt a platform. Interoperability protects past investments and supports future growth and scale.

Global consistency, local flexibility

Pharma operates globally but executes locally. You need centralized governance that supports configurable workflows that drive consistency so local teams can adapt without rebuilding processes.

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Is it interoperable?
Is the platform CRM-agnostic? Can it integrate with any CRM, like Salesforce and Veeva, and with your broader data and analytics stack?
Is it enterprise-ready?
Can the platform support global standards while adapting to regional needs?
See what ZAIDYN can do
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Discover how ZAIDYN®, the life sciences intelligence platform backed by ZS, brings the capabilities in this checklist together in one modular, enterprise-ready platform.
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What to expect during go live

Setting up for success and sustaining daily agility

What to ask
Implementation quality and user adoption

Success depends on more than going live—it’s about how effectively the solution is deployed, adopted and used. Look for a partner that brings strategic expertise and long-term experience in life sciences to drive change and deliver impact while supporting your requirements to operate self-serve or through managed services.

Business user empowerment and operational simplicity

A scalable platform should empower business teams to self-configure, govern and operate the system independently with their own development teams or using agentic AI to ensure it keeps pace with changing business needs.

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Is it easy to adopt?
How are your implementation and onboarding teams structured? Do business and tech experts work together to guide setup and support adoption?
Is it easy to operationalize?
What percentage of changes (workflows, rules, updates) can be made by business users? How easy is it to manage the platform day-to-day without constant vendor or IT support?
Ensure value

Driving sustained ROI and strategic evolution

What to ask
Customer success and enablement

Long-term value depends on proactive partnership, not just initial delivery. Having a customer success manager assigned to you ensures continuous adoption of new features and therefore your investment’s optimization throughout your journey.

Security and compliance

Security and compliance aren’t negotiable in life sciences. Your platform must meet the highest standards—by design.

Flexibility for innovation pilots and co-creation

The right partner doesn’t just meet your needs today; they evolve with you and the needs of the life sciences industry across all brands and markets.

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Will it drive lasting results?
What support is provided beyond go-live? Are there dedicated success teams, roadmap visibility, certifications and peer forums?
Is it secure and compliant?
Is the platform built to meet global privacy and pharma compliance standards?
Will it flex and foster innovation?
Can the platform support innovation pilots or co-create new use cases with your team?
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