By Joe Zarycki
If you build or maintain automated test equipment, the biggest LabVIEW news in years landed in 2026. Emerson, which now owns NI, released the LabVIEW+ Suite 2026 Q1 in February with AI-powered code completion, then announced at NI Connect 2026 that its Nigel AI technology will extend across the whole NI software stack — LabVIEW, TestStand, FlexLogger, InstrumentStudio, and SystemLink — later this year.
As a LabVIEW consultant in the Cleveland area, our team has been following these announcements closely. Here’s a quick rundown of what’s actually new, and — more importantly — the questions we think test stand owners should be asking before anyone gets swept up in the headlines.
What’s actually new
Three things stand out in the 2026 releases:
- Nigel AI code completion and generation. Nigel now offers code suggestions inside LabVIEW with awareness of your project and connected hardware, can review TestStand sequences, and prompt-based code generation is on the roadmap. Emerson says its own engineers have cut some development and troubleshooting tasks from days to minutes.
- Modernized interfaces and debugging. LabVIEW and TestStand are getting updated user interfaces, improved debugging workflows, and better source code control integration.
- Docker support for CI/CD. Container support on Windows makes automated build-and-test pipelines — long standard elsewhere in software — much more practical for LabVIEW projects.
The non-AI items may end up being the sleeper hits. For anyone maintaining a large LabVIEW codebase, better source control integration and real CI/CD support are unambiguous wins.
Our honest take on the AI piece
Vendor demos are impressive, but “days to minutes” describes best-case internal results, not the average project. In our experience, writing code is only one slice of what it takes to deliver a working test stand — and often not the biggest one.
The rest is engineering, on both sides of the software:
- On the hardware side: selecting the right sensors for the operating range, fixturing the unit under test, and confirming measurement uncertainty actually supports the tolerance you’re testing against.
- On the software side: architecting code so it’s maintainable for a decade, handling instrument timing and communication quirks, building error handling that behaves sensibly on a production floor at 2 a.m., and integrating with the systems a plant already runs.
- On the quality side: validating that the system measures what it claims to measure and documenting it so the results hold up to an audit — the discipline behind ISO 9001 and ISO 17025 quality systems.
An AI assistant can suggest code. It can’t take responsibility for any of the above. For test systems that support certification work — like the ENERGY STAR appliance certification stations our team has built — trust in the data is the entire product. That trust is earned through engineering rigor, and we don’t see that changing.
How much AI actually shortens real-world development time is, frankly, still an open question. We suspect it will help with some routine work. Whether that moves the needle on total project cost and schedule — when integration, debugging on real hardware, and validation dominate the effort — remains to be seen.
Questions worth asking
Rather than predictions, here are the questions we’re asking — and that we’d encourage any manufacturer with LabVIEW test stands to ask too:
- How do you validate AI-suggested code in a regulated test environment? If a sequence was partially machine-generated, what does your review and documentation trail look like?
- Does faster code writing actually shorten projects? Or does the schedule still hinge on hardware integration, debugging, and validation?
- How will these tools handle the systems that matter most — the aging, lightly documented test stands that have quietly run for 10+ years?
- What would it take for you to trust test data from a system built with AI assistance? That’s a question every test lab and every equipment supplier is going to have to answer for their customers.
We don’t claim to have all the answers yet. We do think the honest position right now is measured curiosity: watch the tooling mature, welcome the genuine improvements (source control, CI/CD, modern debugging), and keep the engineering standards exactly where they’ve always been.
Who we are
Dynamic Engineering LLC is an automation and controls engineering firm serving the Cleveland, Ohio area and beyond since 2008. We design and build custom automated test stands, data acquisition systems, and machine-vision inspection systems using LabVIEW, cRIO and cDAQ platforms, and PLC integration. The firm is led by Joe Zarycki, who holds MS and BS degrees in Electrical Engineering from Case Western Reserve University and is an ISO 9001 and ISO 17025 certified lead internal auditor and LEAN leader.
Have thoughts on any of the questions above — or a test stand that needs attention regardless of what AI does next? Contact Dynamic Engineering — we’d be glad to compare notes.
