Lead with impact: How life sciences leaders are transforming their most critical processes

Every life sciences leader we engage with has a critical process that needs to transform, whether that’s a statistical computing environment built around legacy tooling, a clinical data pipeline that hasn’t kept pace, or a real-world evidence workflow that hasn't scaled. The pressure to act is real and the cost of waiting is growing.

Three forces are converging to make that urgency impossible to ignore.

• Regulatory green light. The FDA recently updated its eCTD Technical Conformance guide (December 2025) to approve the use of R-specific files. The R Consortium has since run five successful submission pilots. Open-source languages are now fully viable for regulatory submission.
• Talent expectations have shifted. Deloitte reports that 83% of life sciences companies struggle to find skilled talent. The current generation is trained on R and Python. Environments that only support SAS make it harder to attract and retain them.
• A new governance framework. The FDA finalized Computer Software Assurance (CSA) in September 2025, replacing the documentation-heavy CSV approach with a risk-based one that includes AI and ML tools. Organizations implementing it are reducing validation times from months to weeks.

Together, these shifts define four imperatives that life sciences leaders are navigating right now, from the statistical programming teams responsible for regulatory-ready analyses to the data science and IT leaders scaling new capabilities across the enterprise. The organizations making the most progress on all four are doing it from a common foundation: a standardized data science platform that gives every team a consistent, governed environment to work from.

The tooling and process imperative: More than a technology upgrade

Statistical computing environments carry years of accumulated decisions around tooling, validation processes, QC, and reproducibility standards. Modernizing them means more than introducing new languages. It means rebuilding the process layer that makes those environments trustworthy in a regulated context across the full clinical and regulatory lifecycle.

The core challenges this kind of transformation has to solve include:

• Validated environments across languages. Running R, Python, and SAS in a unified environment where all outputs are traceable, auditable, and reproducible.
• QC at scale. Automating checks that have historically been manual and applying them coherently across languages.
• Lineage and reproducibility. Making every analysis, data transformation, and output traceable to source to comply with FDA, EMA, and PMDA requirements.
• Inspection readiness. Designing processes and systems so that documentation is an outcome of the work, not a separate exercise.

The governance imperative: Accelerating outcomes instead of slowing down work

Regulated environments have always required governance. The question is whether it functions as infrastructure or friction. The paradox in this industry is that the fastest-moving organizations aren't the ones with the least governance. They're the ones who embedded it so deeply it stopped being a bottleneck.

Three shifts define what that looks like in practice:

• Leaving "defensive" governance behind. Leading organizations have moved from governance as a checkpoint to governance as infrastructure, where it’s built into the workflows rather than layered on top.
• The agentic opportunity. As agentic applications take on tasks like literature review and clinical data validation, governance can't be an afterthought. It has to be built in from the start to be credible and scalable.
• The FDA credibility framework. Organizations aligning early with the FDA's proposed seven-step credibility assessment framework are better positioned for regulatory engagement, building in the evidence and documentation requirements from the start rather than retrofitting them.

The application imperative: From models to decisions

For years, organizations have invested in models for clinical trial optimization, real-world evidence analysis, and patient stratification across R&D. Yet the promised value often goes unrealized because these models don't reach the people making decisions. Researchers, biostatisticians, and clinical teams need applications built on these models to act on what the data is telling them.

What that requires in practice:

• Delivering insights at scale. Dedicated, secure, governed, and scalable applications put insights in the hands of the people who need them at the speed and scale of the business.
• From one-off to repeatable. Building a pilot is straightforward. Delivering standardized, reproducible workflows that scale across R&D is where the real enterprise value lies, and where a common platform makes the difference.
• Putting the right people in the loop. The most valuable decisions in drug development require human judgment informed by robust data. Applications that bring scientists and AI together with the right guardrails are where that happens.

The leadership imperative: Transformation driven by mindset

Technology and governance alone don't drive transformation. Leadership does. The organizations making the most progress have leaders who committed early, set ambitious targets, and created the cultural conditions for change to stick. Open-source adoption at scale, for instance, is rarely a technical problem. It's an organizational one, solved by leadership decisions about tooling, training, and what teams are empowered to say yes to.

The conversation happening on May 12 in Philadelphia

Rev Philadelphia brings together leaders from across life sciences who are navigating these imperatives and sharing what they've learned, including what they'd do differently.

A former FDA Commissioner opens the day. Novartis's Global Head of Quantitative Sciences closes it. That should tell you about the level of conversation in the room. Speakers from GSK, AstraZeneca, BMS, Merck, and UCB will share real-world experiences across all four imperatives.

Whether you're modernizing how your organization runs statistical computing or scaling data science and AI across the enterprise, the conversations at Rev are designed to shape your strategy, sharpen your execution, and give you an honest picture of what actually works.

Rev Philadelphia is May 12 in Philadelphia. Seating is limited and reserved for data science, statistical programming, and IT leaders in life sciences. View the agenda and register now at https://rev.domino.ai/philadelphia.