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Life sciences chief information officers (CIOs) face a paradox. On one hand, they are at the threshold of harnessing the future of scientific innovation with modern-day tools powered by artificial intelligence (AI) and machine learning (ML); on the other hand, they are still working with legacy software and data that hold their scientists and researchers back.

Organizations commonly navigate outdated systems that make collaboration harder and breakthroughs slower, causing delays, missed insights, and costly rework. It’s a critical moment for technology investments against the backdrop of rapid change and continued fiscal pressures. What are the six essential priorities for life sciences CIOs in 2025 and how should they address them?

Priority 1: Eliminate Data Fragmentation to Enable AI and ML

• The Problem: Disconnected tools and siloed data cause a host of issues. Legacy systems create fractured, incomplete workflows. Manual handoffs between wet and dry labs lead to inefficiency and rework. Inconsistent, unstructured data blocks AI’s potential. The day-to-day struggle for many teams is navigating poor ontology, with no harmonization, and a frustrating lack of scientific context for their data.
• The Solution: Establish a data-first architecture. With the appropriate infrastructure and environment, AI is already transforming research approaches through advanced algorithms that support novel target identification and rapid candidate screening. In the future it will enable whole new approaches, including novel therapeutic modalities previously too impractical to explore. To get there, life science teams need a unified, connected data environment, one in which data is a first-class citizen. They need the power and flexibility of a platform that harmonizes and structures data, enabling traceable, context-rich data from molecule design through testing.

Priority 2: Close the Gap Between Wet and Dry Labs

• The Problem: Scientific workflows are still linear and disconnected. Static resources such as spreadsheets, emails, and inconsistent annotations along with siloed data capture platforms hinder collaboration. In early-stage discovery, this disconnect can cause candidate compounds to be prioritized based on outdated models or incomplete assay data. Experimental insights often fail to make it back to the modeling stage in time to influence the next design cycle, resulting in missed opportunities and slower optimization.
• The Solution: Implement a Lab-in-a-Loop model. A lab-in-a-loop is an AI-powered system that links real-time experiments with machine learning to accelerate drug discovery. Data from each test guides the next step, creating a fast, self-improving cycle. This closed-loop approach streamlines design, testing, and optimization—cutting costs, saving time, and increasing success rates. Create a continuous feedback loop to accelerate decision-making, empowering a bidirectional flow of information between computational and experimental teams.

Priority 3: Adopt a Composable Cloud-First Architecture

• The Problem: Inflexible IT architecture limits innovation and scalability for life science R&D. Restrictive IT architecture traps drug discovery teams in rigid, outdated systems that can’t adapt to fast-paced scientific workflows. As research complexity grows, especially in areas like novel biologics, conjugates, and materials science, the tools, data, and workflows needed to innovate have outpaced the infrastructure that supports them. It limits integration with emerging tools, slows down data sharing, and creates bottlenecks that stifle innovation. Rather than enabling scientists, these fragmented and compromised workflows hinder scientific processes. As new therapeutic approaches evolve, legacy infrastructure simply can’t scale to meet modern R&D demands.
• The Solution: Embrace composable, cloud-native infrastructure. Organizations that successfully implement digital architectures gain the foundation for next-generation technologies, like digital twins. CIOs should proactively evaluate whether their current vendor enables a composable business model or merely provides basic infrastructure. It will likely also be necessary to update organizational capabilities and frameworks to scale composable approaches most effectively.

Priority 4: How to Enable Real-Time Adaptive Workflows

• The Problem: Rigid workflows can't keep up with iterative R&D. Static systems don’t adapt to scientific variability or complexity, and unfortunately that doesn’t align with how scientists work in the lab during the scientific discovery process. It has long been estimated that scientists lose upwards of 50 days per year owing to inefficient processes, and on average 10% to 20% of development work is repeated due to data integrity and accessibility issues.¹ In a recent study, 80% of scientists said that the workarounds currently required to get data into meaningful outputs are negatively impacting their work and almost 70% reported compromised decision-making because of this.²
• The Solution: Dynamic adaptive workflow engines. Adaptive workflow systems represent a significant departure from rigid, process-centric approaches. Instead of imposing a linear, step-by-step structure, they empower teams to dynamically assign and adjust tasks as the needs of the research evolve. Users can flexibly string tasks together in any order (provided input validation criteria are met) allowing research teams to remain agile and responsive to new insights and shifting priorities. This action-centric approach aligns with how research actually progresses in real-world settings. Teams can adapt to new insights or experimental pivots without requiring IT reconfiguration.

Priority 5: Support Multimodal R&D Across Therapeutic Areas

• The Problem: Most platforms aren’t built to handle multimodal research. Across many organizations, drug discovery is shifting from single-modality research to multimodal strategies. While this evolution holds immense potential, it’s difficult to support in environments historically siloed by domain. Multimodal R&D generates highly varied data types—often incompatible by nature—as are the tools used to create and interpret them. These disconnects disrupt workflows, impede cross-functional collaboration, burden scientists with manual data handling, and obscure critical insights hidden across fragmented datasets.
• The Solution: Deploy a system purpose-built for flexibility with modalities. Innovative next-generation platforms support R&D in incredibly diverse areas including protein therapeutics, gene editing, cell therapy, vaccines, and oligos. Users can choose which modalities they want to work with, and they can explore and modify that data on their own. This self-service approach lets scientists and researchers modify processes mid-experiment without disruption, ensuring workflows are as dynamic as the discoveries.

Priority 6: Simplify Compliance and IP Management

• The Problem: Legacy systems often fail to handle the complexity of modern biologic formats. This leads to imprecise data representation and fragmented workflows. These systems struggle with tracking molecular structures like multispecific antibodies (msAbs), creating gaps in traceability, which can cause miscommunication, delays, and costly errors.
• The Solution: Real-time traceability and exportable records. Scientists need more than just data—they must also track the evolution of scientific thinking, hypotheses, and iterative analyses. They need a “digital thread” that begins by connecting all data sources that can be used in R&D. A digital thread is a connected workflow of data across the entire lifecycle involved in developing new therapeutics, from early research and development to full scale production. It connects traditionally siloed functions—like design, development, testing, manufacturing, and maintenance—into a single, cohesive data flow. It's the digital backbone that ensures traceability and consistency of data from end to end, across the entire Design-Make-Test-Decide lifecycle.

Designing the Intelligent R&D Ecosystem of Tomorrow

In the next five years, experts predict that life science organizations will increasingly make strategic decisions primarily through advanced analytics platforms. In fact, in Gartner’s 2025 CIO and Technology and Executive Survey, 83% of life sciences organizations reported increasing funding in this area.

As CIOs invest in the future, it’s critical to evaluate gaps in their current R&D infrastructure:

• Focus on connectivity, traceability, and flexibility.
• Create a roadmap for transitioning to a Multimodal Scientific Intelligence Platform.
• Pilot high-impact areas (e.g. protein therapeutics, cell therapy).
• Drive adoption by integrating tools scientists already trust.
• Align scientific, data, and IT teams under a shared vision for the future.

About the Author

Alister Campbell, VP of Science at Dotmatics.

References

1. “Making the Most of Drug Development Data.” Pharmaceutical Manufacturing. 01 December 2005.

2. Optimizing Outsourcing in Early Small Molecule Drug Discovery.” Dotmatics survey. 21 March 2022.