The pharmaceutical industry faces immense pressure to accelerate innovation, mitigate development\u00a0risks\u00a0and maximize research and development (R&D) returns\u00a0at scale. While advanced technologies like artificial intelligence (AI) and automation offer powerful\u00a0solutions,\u00a0fragmented tools,\u00a0disconnected environments, siloed data and legacy R&D stacks complicate R&D workflows and hinder innovation.<\/p>\n\n\n\n
The full potential of AI and automation technologies is, therefore, not realized.<\/p>\n\n\n\n
To be sustainable in the long run, organizations must rethink their current research infrastructure. This includes everyone from the individual scientist all the way to the broader enterprise.<\/p>\n\n\n\n
The core challenge is no longer generating insight; it\u2019s translating insight into reproducible, scalable impact. However, scientific data remains scattered across instruments, applications and teams, and critical information like experimental intent, material lineage and decision rationale is rarely captured structurally as projects progress. Instead of accelerating discovery, fragmented data has become a hidden drag. Tools proliferate without coordination, data becomes increasingly difficult to trust or reuse and workflows fracture across organizational and technical boundaries.<\/p>\n\n\n\n
Scientists spend more time navigating infrastructure than advancing research. Without structured scientific workflows and lifecycle continuity:<\/p>\n\n\n\n
What holds organizations back is the absence of a connected foundation that unites scientific reasoning with coordinated development and production. This fragmentation means knowledge is lost at critical handoffs, scale-up becomes reactive and downstream teams must often reconstruct earlier findings.<\/p>\n\n\n\n
Dotmatics, which is now part of Siemens, provides a unifying R&D platform designed to connect diverse tools, data and teams, empowering faster innovation by:<\/p>\n\n\n\n
Luma <\/a>unifies research systems and data to accelerate innovative outcomes in drug discovery and development.<\/p>\n\n\n\n Scientific discovery has evolved dramatically, especially in areas like novel biologics and conjugates. However, the infrastructure supporting it hasn\u2019t kept pace. The tools, data and workflows needed to innovate have outpaced the infrastructure that supports them as many R&D organizations still operate on systems designed for a far simpler era. <\/p>\n\n\n\n As scientific ambition grows, the gap between what researchers need and what legacy environments can support widens, creating friction at every stage of innovation. <\/p>\n\n\n\n Most organizations feel this strain in three critical ways:<\/p>\n\n\n\n Over time, R&D organizations accumulate hundreds of applications, each solving a narrow problem that is tied to a specific domain or function. While these tools may work well in isolation, they rarely work well together. Limited interoperability and inconsistent standards force scientists to jump between systems for experiment tracking, data analysis, visualization and reporting. The result is cognitive overload, manual workarounds and delayed insights, all of which undermine collaboration and productivity.<\/p>\n\n\n\n Fragmented tools inevitably lead to fragmented data<\/a>. When information is scattered across vendors, formats and repositories, scientists must rely on manual,\u00a0error-prone\u00a0processes to collect, prepare,\u00a0move\u00a0and reuse data. Without consistent standards for structure, quality, governance and security, data loses its value, insights are missed, reproducibility\u00a0suffers\u00a0and the promises\u00a0of advanced analytics and AI remain\u00a0out of reach.<\/p>\n\n\n\n Modern R&D requires platforms that can span disciplines, adapt to new science and support enterprise\u2011wide decision making. Legacy architectures struggle to deliver this cohesion, custom-built integrations are expensive and difficult to maintain and narrowly-focused off\u2011the\u2011shelf solutions fail to support cross\u2011domain workflows. These constraints make it difficult to scale innovation, leverage AI or generate meaningful enterprise insights.<\/p>\n\n\n\n Luma is different. The enterprise R&D platform connects multi-domain science, data and decision making, helping R&D teams work more efficiently and innovate like never before.<\/p>\n\n\n\n Optimizing modern R&D is no longer about improving isolated workflows or speeding up individual steps. Breakthroughs increasingly depend on collaboration across disciplines, seamless handoffs between teams and the ability to connect data and technologies across multi\u2011stage, multi\u2011domain processes. To support this reality, especially in AI\u2011aided R&D, a solution must strike a careful balance: empower individual scientists while providing a unified foundation that is essential for cross-domain workflows and dataflows.<\/p>\n\n\n\n The AI\u2011native Luma data management and workflow automation platform connects people, data and technology in a single, unified environment. The result is an R&D ecosystem where enterprise data is harmonized, workflows span domains without friction and teams can collaborate more effectively.<\/p>\n\n\n\n This unified multimodal context transforms fragmented scientific work into a structured, connected foundation for scalable innovation, supporting individual scientists, cross\u2011domain teams and the broader organization on a single foundation. This enables scientific work to scale without sacrificing usability or rigor.<\/p>\n\n\n\n This approach streamlines cross\u2011domain workflows, reduces reliance on manual data handling and eliminates brittle custom integrations. By capturing and modeling relationships across entities, samples, assays and instruments within a single contextual framework, organizations can preserve scientific intent and continuity from discovery through development.<\/p>\n\n\n\n Luma modernizes R&D architecture to support:<\/p>\n\n\n\n The platform governs and automates data across complex workflows, normalizing and structuring multimodal data from internal systems as well as third-party instruments, equipment, software and databases. It can adapt to workflow variability and user preferences, delivering personalized, scalable scientific intelligence that accelerates discovery across the enterprise and pushes science forward.<\/p>\n\n\n\n This structured scientific foundation makes AI meaningful and scalable, ensuring that AI investments deliver on their promise.<\/p>\n\n\n\n With Luma, R&D organizations can fundamentally reshape the discovery funnel by modernizing their research and data processes. Digital transformation moves beyond incremental productivity gains, replacing fragmented research and data processes with a connected, intelligent foundation that accelerates progress from early discovery through development.<\/p>\n\n\n\n Scientists gain trusted cross-modal reasoning, reproducibility and automation-ready workflows, while leaders gain confidence that knowledge won’t be lost at critical handoffs.<\/p>\n\n\n\n Luma drives efficiencies at every step, letting experts:<\/p>\n\n\n\n This shift reduces costly late-stage failures and sharpens organizational focus on the most viable, high-impact opportunities. The result is a smarter, faster R&D engine that brings safer, more effective and higher-value products to market in less time. <\/p>\n\n\n\n Luma provides confidence that research and discovery efforts are optimized for speed, accuracy and innovation, empowering pharmaceutical companies to remain at the forefront of scientific discovery. By adopting a unified therapeutic candidate research and design solution, pharmaceutical companies gain unparalleled clarity and control over their R&D processes.<\/p>\n\n\n\nWhy legacy systems are incapable of keeping up with modern science<\/h2>\n\n\n\n
Fragmented technology ecosystems slow scientific progress<\/strong><\/h3>\n\n\n\n
Siloed and inaccessible data breaks the scientific feedback loop<\/strong><\/h3>\n\n\n\n
Complex enterprise architecture limits innovation<\/strong><\/h3>\n\n\n\n
One platform built for scientists, teams and the entire enterprise <\/h2>\n\n\n\n
Enabling the strategic use of automation, modeling and AI<\/h2>\n\n\n\n
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The realized impact of improving R&D efficiency<\/h2>\n\n\n\n
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