Driven by widespread adoption across scientific and industrial sectors, global spending on artificial intelligence (AI) is growing rapidly and is projected to reach $632 billion by 2028. Yet, despite its promise to transform scientific R&D, more than two-thirds of organizations struggle to scale AI initiatives and convert significant investment into measurable returns. As a result, AI efforts often remain stuck in early experimentation or pilot phases rather than delivering meaningful, enterprise-wide impact. The challenge is not AI itself, but the data foundation underpinning models.
Organizations often attempt to deploy sophisticated AI models on data infrastructures that were never designed for interoperability or machine learning readiness. For R&D teams constantly merging legacy datasets with new scientific outputs, outdated infrastructure makes it even harder to prepare data for AI initiatives, stalling scalability and limiting ROI.
To support high-scale AI initiatives and unlock long-term value, R&D organizations must shift from static, storage-centric architectures to dynamic, AI-ready data ecosystems built for scientific intelligence.
AI can’t succeed on outdated data foundations
AI doesn’t just need more data; it needs better backbones. Scientific data infrastructure has historically been optimized for static storage rather than intelligence. Conversely, AI requires interconnected systems that allow data to flow seamlessly across workflows.
Traditional scientific data environments introduce several obstacles:
- Rigid formats and inconsistent inputs fragment data: ELNs, LIMS, and homegrown systems often rely on non-standard formats and lack structured input protocols. Without harmonization, AI models must navigate incompatible datasets, which limits their ability to recognize patterns and deliver accurate insights.
- Disconnected systems prevent AI from accessing full scientific context: R&D workflows span multiple platforms, but many were built as standalone tools without interoperability in mind. As a result, data becomes siloed across disciplines and departments. AI then cannot interpret the full experimental or organizational context, reducing the quality, reliability, and relevance of its outputs.
- Missing governance mechanisms limit trust and reproducibility: Older platforms often lack lineage tracking, audit trails, and version control. When teams cannot verify and trace the provenance of data, confidence in AI-generated results erodes. This slows adoption and increases the risk of model drift, misinterpretation, or hallucination.
These issues break the flow of data between scientists, systems, and AI models. Even the most advanced algorithm cannot perform well if the environment around it isn’t built for intelligent data exchange.
Three data infrastructure upgrades your AI models need
A robust AI strategy relies on data quality and structure. For R&D teams, three upgrades are essential to prepare R&D systems for scalable, high-performing AI:
1. Connect systems to unlock data flow
When ELNs, LIMS, registries, and legacy databases operate in isolation, AI receives only partial views of the entire scientific picture. Organizations must unify siloed platforms into an interoperable data environment so AI models can gain a holistic view of the existing scientific landscape.
Why it matters:
When systems communicate, AI can fill in information gaps and avoid data bias, which improves overall performance and prediction accuracy for better insights and informed decisions.
2. Structure data at the source
Unstructured or inconsistently captured inputs force teams to invest time in intensive data preprocessing, which slows AI deployment and reduces model efficiency. Organizations must standardize data capture across functions to maximize AI-readiness from the point of entry.
Why it matters:
Standardizing data collection protocols and systems organization-wide not only alleviates the preprocessing burden from IT and R&D teams but also improves data interoperability for faster AI deployment.
3. Scale AI without sacrificing trust
As AI models expand across R&D, data inconsistencies, missing lineage, and a lack of governance increase the risk of data drift, degraded model performance, and compliance issues. Teams must integrate governance frameworks, including lineage tracking, auditability, and version control, to ensure reliability as data volumes and model complexity grow.
Why it matters:
Strong governance protects scientific integrity and builds internal trust in AI, giving AI models the foundation to scale initiatives beyond isolated pilots.
Fast-track infrastructure upgrades to maximize digital investment with CAS Custom Services℠
R&D teams already operate under tight bandwidth, often lacking the time and capacity to upgrade infrastructure while supporting day-to-day operations. Unfortunately, off-the-shelf tools typically solve only a portion of this problem while leaving the most intensive knowledge management tasks—scientific data cleaning, curation, and mapping—to internal teams. Yet, unlocking ROI from AI investments requires organizations to move quickly with an infrastructure designed for scientific speed and scale.
This is where CAS Custom Services makes the difference. Our experts blend together scientific, knowledge management, and advanced digital expertise to help organizations build robust, scalable infrastructures that prepare data for AI. Unlike off-the-shelf tools, CAS Custom Services delivers tailored support to overcome integration barriers, harmonize fragmented systems, and transform disconnected data into reliable, insight-ready assets. The result is a data foundation designed for science-smart AI, enabling faster AI deployment, smarter scientific decisions, and a maximized return on digital investment.
A data foundation built for the future of scientific innovation
AI is reshaping how R&D operates, but its impact depends on the quality and agility of the data ecosystem beneath it. When infrastructure is connected, structured, and governed, AI can deliver faster insights, greater reproducibility, and more reliable scientific outcomes. Conversely, outdated systems limit visibility, slow decision-making, and restrict the value organizations can extract from their digital investments.
Future-ready data infrastructure transforms AI from an isolated experiment into an everyday engine for scientific intelligence. By modernizing the way data flows, is captured, and is managed, organizations can lay the groundwork for scalable, trusted AI that accelerates discovery and strengthens their competitive edge.