How scientific organizations turn AI investment into results that support business goals.
AI now plays a central role in many scientific and R&D strategies. However, achieving measurable, consistent outcomes remains a challenge.
A recent survey by Roland Berger found that 67 percent of R&D leaders are dissatisfied with the speed of AI implementation, indicating that current investments are not yielding the expected returns. Even as budgets increase and tools advance, many teams still struggle to translate AI activity into results that matter.
Effective programs share a consistent pattern. They define their goals early and organize workflows around reliable information, leading to faster progress and a greater operational impact.
How cleaner data accelerated discovery for a global chemical company
Unstructured or inconsistent data creates delays that often go unnoticed until timelines begin to slip. Files from instruments or collaborators may arrive with formatting gaps or transcription errors that block analyses. Teams lose time correcting these issues when the focus should be on interpretation. Standardization helps mitigate these setbacks and keeps projects on track.
A global chemical company encountered this issue across multiple data sources. Every day, hundreds of new substances entered its systems from CROs and suppliers, each using different identifiers and naming rules that caused duplication and confusion.
CAS helped bring everything together using CAS Registry Numbers® (CAS RN®) as the verified control reference. All records were standardized, and an API was implemented to verify new entries automatically. Manual corrections dropped sharply, and data accuracy improved across the board. Scientists gained back time for discovery instead of data cleanup, and the company reduced its administrative costs.
Read the full case study for more details on how CAS helped the chemical company resolve its data challenges and improve day-to-day performance.
Clear priorities guided AI toward reliable, quantifiable success
AI initiatives work best when success is defined in measurable terms, rather than general goals like “improve efficiency” or “accelerate discovery”.
The National Institute of Industrial Property (INPI) of Brazil, set a clear goal: to reduce a backlog of 150,000 patent applications within two years without increasing staff.
CAS collaborated with INPI to create an AI-supported workflow that combined similarity searches with examiner oversight. The approach integrated data from the CAS Content CollectionTM directly into existing systems, allowing examiners to locate prior art quickly and maintain review quality.
Within two years, the backlog dropped by roughly 80 percent. Average review times were cut in half, and the office met its target ahead of schedule. Clear objectives kept the project focused and made its impact easy to measure.
Learn more about our work with INPI Brazil and see how clear goals drove a significant improvement in patent examination.
Curated scientific datasets enhanced prediction accuracy and experimental reliability
Prediction accuracy determines whether AI improves discovery or introduces errors. When models rely on incomplete or generic data, their results become unreliable. Curated datasets that accurately reflect real chemical behavior yield stronger and more consistent predictions.
ChemLex, an AI-based research organization, encountered this problem while developing a regioselectivity (the choice of where a new chemical bond forms) model for metal-catalyzed reactions. The open-source reaction data it used contained too many examples of simple, well-studied reactions and too few complex ones. This imbalance made the model perform well on easy cases but fail under real experimental conditions.
CAS worked with ChemLex to develop a tailored training dataset drawn from the CAS Content Collection. The steps taken included:
- Reviewing and standardizing all reaction records.
- Adding missing mechanistic details and experimental parameters.
- Verifying data accuracy and completeness for model readiness.
- Enabling retraining that improved accuracy and model stability.
Predictions began to align with laboratory results, providing teams with the insight to refine experiments more quickly and streamline development.
See the full ChemLex case study to understand more about how tailored datasets helped the team align predictions with laboratory results.
How structured data management created repeatable progress and strategic continuity
After improving accuracy, the next challenge is scaling success across programs. When data and workflows are organized consistently, results from one project can be carried forward to support the next, rather than being recreated each time.
A global materials manufacturer applied this approach while developing next-generation electronic components. Its R&D teams collected years of data scattered across different systems, often buried in lab files and reports. Much of the key knowledge was held by senior scientists, making it hard to document or share across projects. As a result, progress slowed, and teams often found themselves redoing experiments unnecessarily.
CAS helped the company organize this knowledge into structured, AI-ready data. It brought order to thousands of material records, connecting what each material was made of, how it was processed, and how it performed.
CAS scientists incorporated missing parameters from the CAS Content Collection to fill data gaps and align terminology across groups. The curated dataset served as a foundation for predictive models that could reproduce expert reasoning and identify material candidates that met precise design targets.
View the full case study for more information on how CAS helped the materials manufacturer create structured data that improved the flow of development.
Building measurable R&D impact through scalable, data-driven Science-Smart AI
AI is changing the way scientific research teams think, test, and make decisions. The programs that see real progress start with data they can trust and goals that are easy to measure. From there, success hinges on building systems that can grow and adapt as new discoveries emerge.
At CAS, we help organizations make their AI truly science-smart. Our curated scientific data and expert validation provide Science-Smart AI with the context it needs to perform accurately in real-world research environments, leading to improved R&D and stronger business outcomes.
Want to make AI a consistent source of business growth? Connect with CAS specialists to explore strategies that can strengthen decision-making and increase the reliability of your predictive models.