Organizations across the life sciences are entering 2026 with a clearer view of the capabilities they need to scale effectively. Conversations at IQVIA’s recent Impact Summit reinforced several themes that continue to guide priorities for data, analytics, and technology investment. These themes reflect where leaders are focusing their attention as organizations prepare for 2026.

Why enterprise data foundations matter for scalable growth

Organizations are prioritizing enterprise data foundations to support clearer decision making, stronger compliance, and more consistent activation to drive better business results. Summit discussions underscored why these investments are accelerating.

Last fall’s Impact Summit brought information, technology, and people into one integrated agenda. With representation from more than 100 organizations, sessions addressed the flow of considerations, from market realities to enterprise operating models, and then to multisource data stacking and governance. Discussions focused on how teams use data, analytics, and processes to accelerate performance, strengthen compliance, and measure outcomes with discipline.

The industry overview highlighted the concentration of growth within a small set of products and the pressures created by complex launch dynamics. Those pressure included payer coverage constraints and competition which continued to drive rapid drop-off, with only 4% of patients remaining on their original prescribed therapy after 12 months.

Speakers emphasized the need for shifting value earlier in the lifecycle, applying behavioral and decision science to provider adoption, and integrating diverse signals into action. “Integration is not optional; it’s the foundation for innovation,” said Luke Greenwalt, IQVIA’s VP and Lead, U.S. Thought Leadership & Innovation.

Ben Price, VP, Head of Information Solutions Go-to-Market at IQVIA, outlined increasing disruption across data supply and distribution and reinforced the importance of stronger integration and governance among both areas. Organizations were encouraged to centralize those operations where they matter, establish a community of practice, standardize brand frameworks, and invest in continuous data cataloguing supported by lifecycle management.

These foundational capabilities will become even more important as organizations prepare for growth and experimentation with new data assets.

Multisource data strategies are becoming essential for visibility

Driven by evolving launch settings and the need for broader visibility into increased patient and claims coverage, organizations are expanding beyond single-source data acquisition approaches and moving toward data stacking which involves layering similar data from multiple partners to improve coverage and interoperability.

Summit sessions demonstrated how these multi-source strategies are maturing into operational expectations while reviewing the challenges and considerations inherent in following this methodology.

For example, tokenization must remain a central pillar that provides consistent patient identifiers to enable longitudinal analytics while preserving patient privacy, backed by robust governance frameworks that keep suppliers aligned and reduce duplication risk.

In a Top 30 Pharma example, aligned stacking increased usable data by 25%, improving visibility through disciplined tokenization, supplier coordination, and lifecycle management.

Barbara Somlo, Managing Principal, EBP Analytics, and Hari Rayapudi, Head of Channel and Specialty Data Solutions at IQVIA, reinforced that incremental data must be assessed based on utility, cost, duplication risks, metric alignment, and provenance. They emphasized that integrated inputs drive opportunities to identify undiagnosed patients and guide commercial planning, targeting, and measurement.

These presentations indicated that as teams move into 2026, multi-source strategies will strengthen the accuracy, consistency, and actionability of analytics across functions.

How AI is moving from experimentation to practical enablement

Artificial Intelligence adoption is no longer conceptual. Organizations are designing AI capabilities for practical, embedded applications that align with governance, interoperability, and real-time decision-making needs, and focusing on what it takes to operationalize AI at scale.

With that in mind, summit conversations on day 2 shifted from frameworks to practical application. Topics explored how teams embed AI into daily workflows, strengthen data foundations, and scale governance so insights are usable, repeatable, and quantifiable.

Rohit Vashisth, VP, Products and Platforms at IQVIA, described how AI is now enabling analytics that were once unimaginable. He emphasized that adoption depends on making AI seamless. “If we really want AI to go mainstream and make adoption happen at scale, it has to be invisible. Think about Google Maps; you are already using AI without even realizing it.”

Other speakers noted that many organizations overestimate their readiness. AI-ready data requires more preparation, more structure, and stronger governance than traditional analytics. AI policy teams are expanding and guardrails are becoming essential.

Practical enablers are now including real-time data access, model-agnostic platforms, and strong governance and security with tokenization, role-based access, and audit trails. Use cases presented at the Summit demonstrated improvements in literature review, commercial analytics, and unstructured data interpretation.

As organizations scale AI in 2026, these discussions indicate that success will depend on embedding AI into workflows with clarity, consistency, and governance rather than treating it as an isolated innovation effort.

Why analytic ecosystems are becoming critical for interoperability

Organizations are shifting from isolated analytics to ecosystems that are transparent, governed, and interoperable. This trend reflects a need for greater trust and shared understanding across functions.

Summit topics on analytic ecosystems emphasized cataloguing data with clarity on biases, strengthening referential integrity, and designing systems for interoperability. Speakers highlighted the value of building calculation libraries with parameter variations that reflect real-world definitions.

A rare disease example revealed how analytical improvements make it possible to identify small populations once considered undetectable, provided the ecosystem is governed and designed for scale.

These sessions provided evidence that moving into 2026, organizations will focus on designing ecosystems that support repeatable, traceable analysis across teams and time horizons.

Master Data Management practices that support more agile, business-aligned operations

MDM is shifting toward flexible, maturity-based approaches that support faster deployment, cleaner integration, and more adaptable governance. Conversations during the Summit highlighted how organizations are modernizing their approach.

The MDM session reframed Master Data Management as a stepwise journey supported by cloud flexibility. Teams can begin by cleaning foundational accounts, establishing reference data, and unifying applications with consistent hierarchies.

A case example demonstrated rapid progress with a fit-for-purpose MDM foundation implemented in under 90 days. The system is integrated with Salesforce to deliver cleaner data to field teams and set the stage for AI-enabled stewardship.

Session content indicated that in 2026, organizations will continue advancing MDM with modular capabilities that support brand, field, and key account needs without fragmenting shared truth.

Why human capability and data literacy are becoming competitive advantages

The demand for people who can connect business questions to data products is growing across the life sciences. As systems and models become more sophisticated, talent and literacy trends are accelerating.

The Summit’s closing panel emphasized the human side of transformation. Organizations need people who translate business problems into data products, connect marketing and technology teams, and build knowledge management that strengthens literacy.

Roadmaps should account for deeper partnerships and responsible first-party data growth, supported by feedback loops that build trust and improve data quality.

Indicators show that in 2026, teams that invest in literacy, cross-functional alignment, and user- focused operating models will be better positioned to activate insights consistently.

2026 Outlook: Connected Strategies, AI Enablement, and Enterprise Foundations

These themes reinforce the capabilities that matter most in 2026. Organizations are prioritizing connected strategies, strong governance, practical AI adoption, and enterprise foundations that support scale and measurable outcomes. These priorities will guide how teams strengthen operations, make decisions, and drive impact across commercial, medical, and analytical functions in the year ahead.