ASAM has published a new interview in its Fun with Standards series, and it opens with the question every test organization is currently asking. Marius Dupuis, CEO of ASAM, asks Dr. Ralf Noerenberg of HighQSoft whether generative AI makes a data standard like ASAM ODS obsolete. The answer runs in the other direction. The standard amplifies what AI can do with test data, and AI amplifies the value of every standardized dataset. The conversation is on ASAM's YouTube channel, and HighQSoft has published a written companion page carrying the same arguments.

Watch the interview on ASAM's YouTube channel. The written version, Standardization and AI: ASAM ODS, is published on highqsoft.com and works through the argument section by section.

Please find the link and additional content here: Standardization and AI: HighQSoft in ASAM's Fun with Standards Series

The starting point is that AI does not remove the need for a data contract. A measurement still has to carry what it means, which channel is which, in what unit, and under which conditions it was recorded. In regulated engineering work, where a result has to stay traceable years after the test that produced it, that contract is what makes an answer checkable rather than merely plausible.

What AI does change is the consumption layer. Conversational interfaces and agents increasingly take the place of the classic rich client, and standardized interfaces are what keep the tool use underneath them deterministic. The layer below is commoditizing at the same time, which moves ASAM ODS from being only a query target toward being the federation layer across data lakes, object stores, and databases.

The interview is direct about what happens without a descriptive layer. An organization that stores measurements in a lake and skips the semantic model has to rebuild that model itself, constrain which tools can read the result, and accept long-term dependence on a single provider. One top-tier OEM has done exactly that at petabyte scale with its own engineering team. For most organizations, adopting the standard costs less than rebuilding it.

The closing argument is about ownership. Open standards keep the meaning of measurement data with the customer rather than with a vendor, which matters most to mid-sized companies that cannot fund a semantic layer of their own. It is also what lets automation and AI-assisted analysis start immediately instead of after a pre-processing project.

*#ASAM #ODS #AI #TestData #Standardization #HighQSoft*