Know what others can’t about scaling AI
Artificial intelligence is already embedded in how life sciences organizations think about commercial strategy. From more precise targeting to faster insight generation and personalized engagement at scale, AI plays a central role in how teams plan, execute and optimize performance.
What is becoming increasingly clear, however, is that the impact of AI is shaped less by the sophistication of the algorithms themselves and more by the strength of the data that underpins them. As organizations move beyond pilots and point solutions, attention is naturally shifting to the foundations that allow AI to perform consistently, credibly and at scale.
Why data readiness matters more than algorithms
AI models learn patterns, detect signals and generate predictions based on the data they ingest. When that data is reliable, consistent and contextualized, AI can support confident decision-making across launch planning, targeting and omnichannel execution.
We recently sat down with HealthVerity Co-Founder and COO Andrew Goldberg to unpack what makes healthcare data AI-ready and where agentic AI is already delivering across commercial and RWE, from launch planning to time-to-event. He walked through where HealthVerity eXOs is working today in launch planning, targeting, market share and pharmacovigilance, how teams are using it to expand capacity with full transparency into the data and code, and the core RWE and HEOR applications it supports. Watch the on-demand session to learn what others can’t about agentic AI and data readiness.
When data is incomplete, fragmented or difficult to integrate, however, teams spend more time reconciling sources and validating outputs than acting on insights. This additional effort on data teams and resources is counterproductive to the goal of introducing AI. This is not an inherent issue with the AI system, itself—AI is only as capable as the data it leverages. It instead reflects the reality of evolving data environments that have been built over time for different purposes.
Recent industry research highlights the same challenges across the life sciences landscape. Organizations are adopting AI steadily, but consistent, well-integrated data remains the primary barrier to scaling its value. The biggest data capability issues reported were normalizing and integrating data from multiple sources, and analyzing complex datasets. These findings mirror the challenges commercial teams face when working across claims, specialty, reference and program data.
The organizations seeing the greatest return from AI are those investing in robust data frameworks that reduce friction, build trust and support downstream analytics. In that context, data readiness becomes a strategic accelerator beyond a technical prerequisite.
AI-ready healthcare data
Moving from pilot programs and selective use to enterprise-scale AI holds significant potential return on investment for biopharmaceutical sponsors. To ensure this expansion is efficient—and to support ROI—it requires data that is designed to support modern analytics from the outset.
AI-ready healthcare data shares several characteristics:
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Accurate, refreshed frequently and stable over time, enabling longitudinal analysis rather than isolated snapshots
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Standardized and harmonized, using consistent definitions and coding frameworks so models can be trained and deployed confidently across use case
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Connected, linking patient-level activity across claims, reference data, specialty channels and relevant programs to provide essential context
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Accessible and interoperable, delivered in formats that integrate smoothly into cloud platforms and machine-learning workflows
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Transparent, with clear provenance, documented limitations and strong governance help sustain trust in AI outputs as models move into production and inform strategic decisions
These data attributes are the result of deliberate strategy, thoughtful integration and sustained focus on quality and governance.
Data integration enables AI at scale
Across the pharmaceutical and clinical research ecosystems, there is growing consensus that standardized, connected data is one of the most important enablers of AI at scale.
Fragmented datasets force teams into repeated cycles of cleansing and reconciliation that slow insight generation and undermine confidence. Unified foundations remove those bottlenecks and allow AI initiatives to progress with speed and consistency.
This is as much a strategic consideration as a technical one. Organizations that invest early in harmonized, interoperable data are better positioned to operationalize AI across brands, markets and channels. The result is not just faster analytics, but more resilient AI programs that continue to deliver value as complexity increases.
Integrated, privacy-first linkage through Synoma®
Unlocking AI’s full potential depends on connected data. Synoma®, Symphony Health’s privacy-preserving linkage solution, enables secure integration across healthcare and consumer datasets, creating longitudinal patient views that are essential for advanced analytics and predictive modeling.
With validated patient match rates exceeding 95% and decades of data integration expertise behind it, Synoma® transforms fragmented inputs into a unified, AI-ready foundation. This allows biopharmaceutical sponsors and other life sciences organizations to train models with confidence, maintain privacy protections and scale commercial AI initiatives without compromising data integrity.
Designed for modern AI workflows
Symphony Health delivers curated datasets that are cloud-ready and structured specifically for advanced analytics. Data is available in ML-friendly formats such as Snowflake, as well as Parquet and CSV, enabling seamless integration into existing data lakes and pipelines.
Each dataset includes full lineage, consistent timestamps and pre-derived measures aligned to common commercial use cases, such as treatment sequences, adherence metrics and NBRx signals. By reducing the time spent on data wrangling and preparation, Symphony enables teams to move more quickly from ingestion to insight.
Building the backbone for scalable AI
AI will continue to reshape how life sciences organizations operate. Its ability to scale, however, will always depend on the quality, connectivity and readiness of the data behind it. Strong data foundations create the conditions in which AI can deliver reliable insight, earn trust and support confident decision-making across the organization.
Symphony Health provides that foundation. Through harmonized, transparent and interoperable data designed for modern analytics, it enables life sciences companies to turn AI ambition into sustainable, enterprise-level impact.
To learn more about how Symphony Health, a HealthVerity company, can support your AI readiness, connect with us today.
