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AI in coatings R&D: why data structure matters before AI tools
Artificial intelligence promises faster and more efficient coatings development, but fragmented data remains a major hurdle. Experts stress the importance of robust data infrastructure as the foundation for successful AI implementation in R&D.
Coatings R&D teams face increasing challenges as fragmented data across spreadsheets, shared drives and disconnected systems hampers collaboration and innovation. A survey conducted at the European Coatings Conference on digitalisation revealed that two thirds of respondents struggle with data accessibility and standardisation, directly impacting efficiency and decision-making.
This lack of structure leads to practical setbacks, such as difficulties in comparing formulations across projects or retrieving critical experimental data from previous trials. Additionally, when key personnel leave, valuable contextual knowledge often exits with them, leaving behind raw data that is difficult to interpret. In an environment driven by shorter development cycles, sustainability requirements and competition, the pressure to address these challenges is growing.
From fragmented data to structured workflows
Artificial intelligence (AI) is frequently seen as the solution to streamline coatings R&D, but experts warn that AI tools cannot overcome inconsistent or poorly organised data. Without a structured framework, machine learning models may produce faster results, but these will be unreliable or incorrect.
The foundational step before deploying AI is establishing a robust data structure, as outlined by the concept of Material Intelligence. This involves systematically organising raw material properties, formulation compositions, process parameters and test results. By ensuring standardisation and comparability across datasets, R&D teams can transform fragmented information into a cohesive resource. Workflows further support this structure by connecting laboratory processes, experimental sequences and projects, providing data in context rather than isolation.
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AI as a decision-making tool
With a solid data and workflow foundation, AI can deliver practical benefits in coatings development. Structured datasets allow AI tools, such as LabV’s Co-Engineer, to perform advanced analyses, including correlations, statistical summaries and visualisations. Beyond analysis, AI can optimise experimental planning through techniques like Bayesian optimisation, reducing the number of trials needed to achieve target properties.
However, the adoption of AI in coatings R&D also raises concerns about data security and intellectual property protection. For European manufacturers, strict compliance with the EU Data Act and GDPR ensures that sensitive formulation records remain within European jurisdictions. Confidential computing further safeguards data by encrypting it during processing, building trust in AI systems.
Conclusion: start with data
For coatings R&D teams aiming to integrate AI, the initial focus should be on assessing existing data infrastructure. Understanding how data is organised, identifying inconsistencies and addressing gaps are critical steps that lay the groundwork for successful AI implementation. As LabV emphasises, AI adoption is not about the tools themselves but about creating the conditions for them to function effectively.
Quelle: Jouanique, C. AI in Paint and Coatings R&D: Why Data Infrastructure Decides Everything. LabV. Retrieved from www.labv.io.