Life sciences 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 AI and 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 Organizations commonly navigate outdated systems that make collaboration harder and breakthroughs slower, causing delays, missed insights, and costly rework.
According to Gartner, life science organizations are actively preparing for a reality where the drug discovery cycle (from preclinical through commercialization) takes less than 7 years. Some companies already report that using AI-driven approaches they have compressed the discovery phase down to from 9 to 18 months. It’s a critical moment for technology investments against the backdrop of rapid change and continued fiscal pressures.
What are the 6 essential priorities for life sciences CIOs in 2025—and how should they address them?
Priority #1: Eliminate Data Fragmentation to Enable AI & 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. In fact, Gartner predicts that 40% of AI initiatives will fail due to such non-technical causes.
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, an environment where data is a first class citizen. They need the power and flexibility of a platform like Dotmatics Luma that harmonizes and structures data, enabling traceable, context-rich data from molecule design through testing. Luma supports FAIR protocols to enable interoperability and support AI readiness. That means with Luma, teams can maintain data lineage and traceability to catalyze AI-driven discovery,
Priority #2: Close the Gap Between Wet and Dry Labs
The Problem: Scientific workflows are still linear and disconnected.
Static resources like 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. Luma creates 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.
Gartner estimates that 75% of life science orgs will move to composable cloud architectures by 2027. 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. Platforms like Dotmatics Luma provide flexibility for purpose-built integrations and rapid deployment.
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 percent of development work is repeated due to data integrity and accessibility issues. 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.
The Solution: Dynamic adaptive workflow engines.
Luma’s adaptive workflow system represents a significant departure from rigid, process-centric approaches. Instead of imposing a linear, step-by-step structure, Luma empowers 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.
Luma supports 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. Plus, Luma is designed to maximize the value of Dotmatics' suite of industry-standard applications, including Geneious, GraphPad Prism, SnapGene, OMIQ, FCS Express, BioGlyph, and Protein Metrics. Each tool is developed by Dotmatics, ensuring in-depth understanding of use cases and user needs, and seamless integration into the Luma platform. Rather than trying to replace the tools that scientists require for innovation, Dotmatics is making it easy to bring these tools together and to integrate the data they create and analyze.
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. Dotmatics Luma tracks molecular lineage, experimental decisions, and digital signatures, plus it supports IP generation and regulatory readiness natively. Luma enables the start of that digital thread across the entire Design-Make-Test-Decide lifecycle.
Designing the Intelligent R&D Ecosystem of Tomorrow
By 2027, Gartner says that 40% of life science organizations will make strategic decisions primarily through advanced analytics platforms. And in its 2025 Gartner 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.
And align scientific, data, and IT teams under a shared vision for the future.
Dotmatics Luma is built specifically to bridge the gaps that traditional systems leave behind. This flexibility empowers scientists to explore multiple paths simultaneously, pivot based on emerging data, and standardize best practices across programs and teams. It’s also designed to interoperate seamlessly with any existing system, from ELN to LIMS to SDMS, enhancing their capabilities through real-time data connectivity, intelligent workflow orchestration, and cross-functional insights.
With Luma, CIOs can protect their existing investments while unlocking new levels of efficiency, collaboration, and discovery—at their teams’ own pace and on their own terms.
Learn more about Dotmatics Luma and how the scientific intelligence platform enables AI capabilities to accelerate scientific discovery.