AI is changing test and measurement in two directions at once: engineers must test increasingly complex devices, while test-instrument companies are exploring AI inside their own design and engineering work. An October 1, 2024 EE Times interview with Kevin Schultz, NI’s chief technology officer for test and measurement, examines both pressures. The article presents a high-level summary rather than a technical description of a particular AI system, workflow or benchmark.
What Schultz’s interview is about
The EE Times interview frames AI as relevant to both sides of engineering: the requirements imposed on test teams by more complicated devices under test (DUTs), and NI’s own use of AI in test-and-measurement work.
That framing matters. As products combine more processors, radios, sensors, software and high-speed interfaces, a test organization has to manage more signals, operating states and possible failure modes. The interview’s central question is therefore broader than whether AI can automate a single measurement: it asks how intelligent techniques may affect the way engineers define, run and interpret tests, as well as how they design the systems that perform those tests.
What the published summary establishes—and what it does not
Established themes
- AI is discussed in relation to changing test requirements as DUTs become more complex.
- The conversation includes NI’s internal adoption of AI, not only the use of AI by NI customers.
- Test and design are treated as connected engineering activities rather than isolated product categories.
Details the article does not provide
- No specific AI model, algorithm, software architecture or deployment procedure is described.
- No test strategy, benchmark, productivity figure or measured quality improvement is reported.
- The accessible article does not reproduce a verbatim Schultz quotation; it links to the video interview instead.
Consequently, the interview is useful for understanding direction and priorities, but it should not be read as a case study proving that a particular AI method works in a particular production environment.
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Why device complexity raises the stakes for test engineering
More capable DUTs increase the amount of information a test system must acquire and the number of conditions under which it must make a reliable decision. That can affect instrumentation, synchronization, test sequencing, data analysis and the way limits are maintained over a product’s life.
AI is relevant to this problem because it can potentially help engineers extract patterns from large test datasets or manage complicated decision processes. The published interview, however, stops at that strategic level. It does not identify which tasks NI has assigned to AI, how engineers validate outputs, or where human review remains mandatory.
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AI in design as well as in measurement
The title’s reference to “test and design” signals a feedback loop: design choices determine what must be measured, while measurement results can influence later design decisions. In principle, AI could be considered anywhere that engineers must handle large datasets, explore alternatives or detect unusual behavior. The source does not say that NI has implemented any particular design automation flow, nor does it report a specific result from doing so.
For readers evaluating AI projects, that distinction is important. A broad statement that AI is being embraced does not by itself establish which data are used, how models are trained, how false positives and false negatives are controlled, or how results are incorporated into an approved engineering process.
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NI and Schultz: the historical context reported in 2024
EE Times identified Kevin Schultz as NI’s CTO for test and measurement and reported that he had been with NI for 34 years at the time of the October 2024 article. The same coverage described NI as having 45 years of experience in test and measurement and said Emerson had acquired the company for $8.2 billion about a year earlier.
Those figures are historical statements attributed to the 2024 coverage, not independently refreshed company-history or transaction data. They provide context for why Schultz’s comments are presented as coming from a long-established test-and-measurement organization.
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NI mioDAQ: the concrete product mentioned
The only specific hardware named in the article is NI’s mioDAQ, described as a USB-C bus-powered data-acquisition device for desktop use or automated test benches. The following figures are the specifications reported by EE Times in October 2024; confirm current configurations and guarantees in NI’s documentation before using them for a purchase or design decision.
| Capability | Reported detail | Qualification |
|---|---|---|
| Connection and power | USB-C, bus-powered | As described in the October 2024 EE Times coverage |
| Analog acquisition | Up to 16 channels, 20-bit resolution | Reported historical specification; current model details were not independently refreshed |
| Sampling | Up to one million simultaneous samples per second | Reported historical specification; verify the applicable configuration and conditions |
| Analog outputs | Four high-speed analog outputs | Reported for the described mioDAQ configurations |
| Digital I/O | 16 flexible digital lines | Reported for the described mioDAQ configurations |
| Counters | Four counters | Reported for the described mioDAQ configurations |
| Calibration | Guaranteed specifications for two- and 10-year calibration cycles | Reported by EE Times; confirm the exact guarantee and model eligibility with NI |
These capabilities make the device a relevant example of compact instrumentation that can sit on a desk or in an automated bench. The article does not compare mioDAQ with competing data-acquisition products, establish that it is the best choice for a given application, or confirm current availability and pricing.
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Questions to ask when applying the interview’s ideas
For test organizations
- Which test decisions are constrained by DUT complexity rather than by instrument hardware?
- What data are available, labeled and traceable enough for an AI-assisted decision?
- How will engineers validate model outputs against known limits and safety requirements?
- What audit trail is required when an AI recommendation changes a test sequence or disposition?
For design teams
- Can test data be returned to design in a form that engineers can reproduce and review?
- Which design choices could be evaluated faster without weakening verification?
- Where must deterministic rules remain in control even if AI is used for analysis or prioritization?
The interview supplies the rationale for asking these questions, not their answers. Specific implementation claims require separate technical documentation or a detailed case study.
Bottom line
Schultz’s message, as summarized by EE Times, is that AI belongs in the conversation about both the tests engineers must perform and the systems they design to perform them. The source establishes that strategic direction and provides mioDAQ as a concrete NI product example, but it does not document a particular AI workflow, performance result or deployment recipe. Treat the interview as context for evaluating those questions—and verify current hardware specifications before making an engineering decision.
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