How We Use AI in a Calibration Laboratory
As the capabilities of artificial intelligence continue to advance, so does its potential as a practical tool in fields where precision and accountability are paramount—including the metrology lab. Modern AI models can be configured to follow specific instructions, operate within defined boundaries, and produce consistent, repeatable outputs. It is precisely these characteristics that make AI a viable and valuable asset in a calibration environment.
ISO 17025:2017 requires that every tool used in a calibration laboratory be verified before it is permitted for use in daily operations. This requirement applies equally to AI. When the outputs of an AI model are systematically reviewed and validated, the tool can be integrated into laboratory workflows in a manner consistent with ISO 17025:2017 guidelines—specifically the requirements for ensuring the validity of results under Clause 7.7 and the control of documented information under Clause 8.3.
In the calibration laboratory where I serve as Quality Manager, I have configured an AI model using our ISO 17025:2017 quality system requirements, internal procedure templates, datasheet formats, and established file naming conventions. From this foundation, I built a workflow in which a technician uploads instrument specifications, and the AI generates a draft calibration procedure and corresponding datasheet. These drafts are then reviewed and verified through use by trained calibration technicians. Any errors or ambiguities are corrected before the documents are formally approved—resulting in usable, verified procedures in a fraction of the time previously required.
The AI-generated datasheets are built from pre-established templates and are verified concurrently with their associated procedures. Once verified, measurement uncertainty budgets are developed and appended to the datasheet. In addition, we have leveraged AI assistance in the development of quality management system documentation, including nonconformance reports, corrective action analyses, and supporting quality forms—all reviewed and approved through our standard document control process. Python scripting has further extended this capability, enabling automated population of datasheet templates with calculated values, formatted for print-ready output without manual reformatting.
The result is a workflow that allows us to generate calibration procedures, datasheets, and uncertainty budgets more quickly, more consistently, and with less time lost away from the bench. The analytical work is completed efficiently without sacrificing the accuracy and traceability that our accreditation demands. And as AI capabilities continue to mature, these tools will only become more effective. We are not redefining metrology. We are improving how we practice it.
It is understandable that many professionals remain cautious about adopting AI in technical environments. That caution is well-founded. AI models do make mistakes—and in a field governed by measurement traceability and accreditation requirements, unverified outputs are not acceptable. The key is to treat AI as any other unvalidated tool: establish its capabilities, define its limitations, verify every output before use, and document the process. When implemented with that discipline, AI becomes not a replacement for technical expertise, but a force multiplier for it.
The path forward for metrology is not to resist these tools, but to apply them with the same rigor we bring to everything else in the lab.