Powering Progress: An AI-ready Data Strategy to Drive More Efficient Innovation

Digital Adoption Reaches New Heights

Digital transformation has moved from a buzzword to a boardroom imperative in R&D organizations. According to Nature’s survey of over 1600 researchers, more than half of the respondents expected AI tools to be ‘very important’ or ‘essential’ in the next decade. 

Substantial investments back this momentum. ABI Research forecasts that chemical companies will spend  $7.4 billion on digital transformation technologies by 2031—driven largely by the Asia-Pacific region. These investments target end-to-end digitization of the value chain, from supply chain optimization to R&D acceleration.

According to McKinsey, digital transformation in resource-heavy sectors like chemicals can potentially double EBITDA, increasing the performance gap to laggards by up to tenfold. An integrated digital strategy—spanning R&D, marketing, operations, and manufacturing—could improve EBITDA margins by 8.5 to 16 percentage points.

While most companies acknowledge the importance and potential of AI, a striking 84% still lacked a defined AI strategy for R&D, according to a Roland Berger survey among R&D decision-makers. Additionally, 67% of R&D leaders voiced frustration with the pace of AI adoption in their teams and nearly half (48%) reported that they have yet to implement AI in their R&D workflows. Key obstacles reported include limited understanding, uncertainty about potential impacts, and insufficient expertise and resources. In fact, 65% of respondents were unclear on the capabilities and tools required to drive AI transformation. 

This disconnect is unsurprising given that while many scientific organizations have embraced digital transformation in some form, few have fully addressed the foundational work required for a cogent data strategy.

Digitization, automation, and AI in scientific R&D workflows are often discussed in terms of technical capabilities. However, industry experts emphasize that the real key to transformation lies in a more strategic approach to  data infrastructure— providing individuals and applications across the organization efficient access to clean, reliable data.

The future depends not just on new tools, but on the right information architecture and quality data to support them. As Molly Strausbaugh, Director of Scientific Content Management at CAS explains, “Our role increasingly involves ‘AI enablement.’ As artificial intelligence and machine learning become more embedded in chemistry and manufacturing, high-quality, structured information is absolutely essential.” In other words, AI tools are only as good as the data they’re trained on. Molly has seen growing global demand for structured, contextualized scientific content— data that is "AI-ready" from the start. “The models may get most of the spotlight, but the data is just as critical. And that’s where CAS is uniquely positioned to help,” Molly says.

This perspective underscores a central truth: the foundation of digital transformation is not just about technology, algorithms, and automation. It's about how people, data, and technology work together to unlock new possibilities. Organizations need to rethink data not just as an asset, but as a key element of the infrastructure needed for strategic innovation.

Building a Strategic Data Foundation

A modern scientific data infrastructure hinges on three interconnected imperatives:

1. Clean and structure

Scientific data—whether from disparate external databases and suppliers, internal lab notebooks and instruments, or even legacy document archives—holds enormous value for innovation. But this data frequently also reflects disparities in how the data were collected and entered and may contain errors, making it non-ideal for multi-source search. Cleaning this existing data and building a foundational data management structure for future data assets is critical to ensure that relevant information is set up to successfully fuel downstream initiatives for faster innovation, more comprehensive IP protection, streamlined audits and regulatory compliance, and many more applications.

2. Harmonize and connect

Scientific data is only useful when it is aligned. Unify terminologies, chemical structures, identifiers, and metadata across fragmented sources, empowering analysis and driving cohesion across teams, tools, and workflows.

3. Curate for AI/ML algorithm development

To build effective AI and ML algorithms, the real work starts long before model training. Raw scientific data is often inconsistent, unstructured, and siloed across systems, making it unsuitable for AI and machine learning without significant cleaning, normalization, and contextualization. A strategic data foundation focuses on preparing data to be "AI-ready" from the outset—structured, standardized, and enriched with the metadata and relationships algorithms need to learn effectively. This reduces time spent on manual data wrangling, ensures more accurate outputs, and accelerates the path from concept to insight. By curating high-value information tailored to your unique  needs, your AI initiatives can ultimately provide more accurate predictions and fuel informed decisions across the organization. 

In many cases, this level of data management isn’t a core competency for sci-tech organizations, which is why partnering with an expert, like CAS, can accelerate progress and boost ROI. Just as the most innovative organizations rely on partners for technology, they also need a trusted partner to help them source and manage the data required to enable their strategy.

Accelerating Progress, Measurable Impact at Scale

These capabilities are not theoretical. Better data and investment in the underlying data strategy to enable it are proven to unlock measurable ROI:

Health-Tech Organization: From 8 Hours to Minutes 
(Read the case study)

A global health-tech organization had decades of valuable research buried in unstructured legacy files, making it nearly impossible to locate and reuse prior insights. CAS developed a custom knowledge management system that enabled full-text search, semantic tagging, and concept-based retrieval. As a result, what once took a scientist eight hours to find could now be located in minutes—reducing redundant work, accelerating innovation, and ensuring institutional knowledge was no longer out of reach.

Diversified Chemical Company: $800K Saved via Data Harmonization 
(Read the case study)

A global chemical company was adding over 800 new chemical substances to its internal databases every day—across inventory systems, compound registries, and knowledge platforms. However, without standardized naming or structure, the data quickly became fragmented and unreliable. Partnering with CAS, the company harmonized its substance data, resulting in over $800,000 in annual savings, thousands of hours saved, and hundreds of thousands of dollars saved in data correction costs. This shows that with the right team, investing in foundational data integrity doesn’t just support science—it drives real business results.

Global Chemical Manufacturer: Enabling Predictive Models that Deliver Efficiency & a Competitive Advantage 
(Read the case study)

At a global chemical company, fragmented data practices were slowing innovation. Scientists spent excessive time manually collecting and formatting materials data, often without the tools or training to manage it effectively. With CAS, the organization adopted a structured approach to data curation and strategy, including hands-on training tailored to their R&D workflows. This shift enabled teams to validate ideas using predictive models before entering the lab—freeing up time for higher-value experimentation. As one senior researcher noted, the change allowed them to move from "collecting everything" to collecting only the most relevant data, unlocking a more efficient and insight-driven path to discovery.

Diversified Chemical Company: Curated Training Sets for AI Predictions & Faster R&D
(Read the case study)

A diversified chemical company set out to apply machine learning to accelerate the design of next-generation electronic materials. However, their internal datasets lacked the specificity and structure required to train effective predictive models. By partnering with CAS, the company received custom training sets built from high-quality, human-curated scientific content. With these enhanced inputs, the model generated precise recommendations about materials and process conditions, leading to a 50% acceleration in R&D timelines. The collaboration helped transform AI from a theoretical initiative into a business driver, unlocking new potential for materials innovation.

From Data Challenge to Competitive Advantage

Today’s innovators possess a wealth of scientific and commercial knowledge, but many lack the infrastructure, expertise, or capacity to leverage emerging technologies to efficiently transform it into strategic insight. With measurable ROI, increased speed to market, and smarter decision-making on the table, modernizing your data infrastructure isn’t a digital initiative—it’s a strategic imperative.