AI pilots in R&D-intensive industries tend to follow a pattern. Strong early results drive enthusiasm from leadership, a budget is approved, then the project loses momentum before it ever reaches production.
These initiatives stall on data quality, not on the AI itself. While the outputs might appear reasonable, if they can't be verified, scientists and engineers might not trust them, which can lead to delays in the compliance review process. Often, the underlying data was fragmented, inconsistently captured, and never designed with AI in mind.
A fragile data foundation leads to issues in AI
Scientists and engineers in most industrial R&D environments describe a familiar situation. Their records are saved using various or inconsistent formats, typically vendor-specific, and their results are scattered across spreadsheets, instrument exports, and PDFs that are disconnected from each other (Figure 1). Furthermore, the context that matters most (i.e., why a specific test was run or which question it was supposed to answer), tends to remain in someone’s memory or their lab book.
Figure 1. Without harmonization, each instrument generates data in a different format with no shared structure or context, making it impossible to connect results across the R&D lifecycle.
The data exists. However, data that wasn’t harmonized, structured, and contextualized from the beginning is very difficult for AI to use, leading to subpar outputs on which scientists and engineers don’t want to stake important decisions.
Orchestrating scientific data to achieve the digital thread
The digital thread is a useful framework for understanding why data quality determines AI outcomes. A continuous, connected flow of data across a product or process lifecycle, from early R&D through formulation and manufacturing, is an important aspect of making AI useful at scale. Beyond linking systems, it crucially preserves the context that gives data meaning as it travels from one stage to the next.
Most organizations have the ambition without the foundation. A thread can't run through data that has no consistent structure and no record of why it was created. These inconsistencies lead to broken threads, which limits the quality of the AI outputs generated from this data.
More data isn't the answer. The focus should be on determining whether the data you already have carries enough meaning and context to support key decisions that depend on it.
Structure best happens at the source
Structured data means data organized around a defined schema. This can include consistent formats, clear labels, explicit relationships between files, and traceable connections to specific process steps. This structure must be incorporated during data creation, not after the fact.
Retroactively structured data is a reconstruction. When restructuring data, the original context is typically lost, and any ambiguities must be resolved by the person performing the data cleanup, not the person who ran the initial experiment. This process is time-consuming and is another example of scientists and engineers wasting precious hours on data organization instead of research.
Why cleaning up old data rarely solves it
Remediation projects rarely reach completion. This is especially true when the workflows creating the problem are still running. Retroactively structuring years of scientific research data is a large undertaking that competes directly with active work. Conversely, a better data foundation isn't necessarily a project with an end date, but a decision about how results get captured and stored going forward.
What good looks like in practice
The organizations getting lasting value from AI in industrial R&D aren't necessarily the ones with the most data. They're the ones whose data is sufficiently structured and connected to be useful at every stage. Getting there requires the right foundation from the start.
Luma by Dotmatics addresses this by embedding structure at the point of capture. It captures instrument outputs continuously and automatically, bringing data into a harmonized, structured record in real time (Figure 2). Because structure is incorporated at the point of capture, the relationships between a process parameter and its downstream outcome are explicit from the start. Scientists and engineers can trust the record, the data is ready for AI applications the moment it arrives, and when a question arises about how a result was produced, the answer is already there.
Figure 2. Dotmatics Luma transform fragmented instrument outputs into a continuous, connected pipeline of structured, AI-ready scientific data.
The outcome is faster, more reliable insight. Scientists spend less time reconstructing results and more time advancing the work that matters. That's the foundation Dotmatics is built to provide.
The thread only holds if the data does.
Interested in how Dotmatics approaches structured data capture across R&D and process workflows? See how the Luma platform is built for AI from the ground up.