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Test and measurement companies are adapting their test playbooks, slotting in AI at the most strategic points
As every large company rallies behind the single, ambitious goal of integrating AI in every corner of their business, and supporting industries follow suit, a question that is occupying everybody’s mind is how are companies actually implementing AI — and more importantly, what is the extent to which they are using it.
RCR asked test and measurement suppliers in the frontlines about how they’re adopting AI in their test pipelines, and what are some of the most developed use cases today.
AI has made inroads into test and measurement, with traditional AI/ML laying the foundation for automation and efficiency enhancements for years. Most companies insisted that adoption is aimed at making complex processes simple for engineers and uplevelling experience for customers. A clear set of use cases emerged from their responses: search, data analysis, anomaly detection — and test automation.
For Synopsys, a supplier of semiconductor IP, test is not a point solution. “It is a critical continuous, end‑to‑end discipline that spans the entire silicon lifecycle from early design and design‑for‑test insertion, through high‑volume manufacturing, and extending into system bring‑up and in‑field operation,” said Srikanth Venkat Raman, director of product management.
AI is sprinkled across this entire pipeline, specifically at three intersections, Raman said. First, AI is used to efficiently look up optimal solutions in large test search spaces, reducing time and costs; second, it is used to mine silicon and manufacturing data from internal databases and turn them into quick actionable insights; and third, generative AI — and soon agentic AI — is used to manage and automate increasingly complex test workflows.
“As designs scale from single‑die to multi‑die and 3D architectures, the volume of data and system complexity increase. That makes AI essential,” Raman emphasized. The company released test space optimization (TSO.ai) as part of a larger AI suite to allow engineers to search for optimal test solutions in large stacks to save cost and accelerate time-to-market.
At Keysight, one of the largest T&M suppliers in the U.S., the focus of AI adoption is to transform densely complex processes into seamless continual workflows. That makes AI an integral part of most test processes, where it is used to automate test setups and execution, turn massive volumes of measurement data into insights, and separate signal from noise. The goal is straight-forward: minimize frictions and expedite results.
“[Use cases] include machine‑learning-driven optimization of test parameters, AI‑assisted generation of test sequences and scripts, use of surrogate models to speed performance and advanced analytics to detect rare anomalies and correlate events across signals, protocols, and system telemetry,” said Jeff Smith, director of AI Labs at Keysight.
“Ultimately, we are using AI to allow engineers to spend less time managing test complexity and more time solving the problems that differentiate their designs,” he added.
As for where Keysight sees the most impact, it is in the burgeoning space of AI infrastructure validation where the company boasts a sprawling portfolio of test solution. It is throwing AI at test and validation of AI data center infrastructure components, high‑speed interconnects, and advanced wireless systems, for faster and more reliable results. “AI enables faster root‑cause analysis, improved coverage, and more repeatable results,” Smith said.
Thomas Braunstorfinger, senior director of power meters and software signal generators at Rohde & Schwarz, a German provider of test solutions, said the company is harnessing AI to capture its customers’ most granular needs, and enhance solution design, delivery, and experience.
The R&S QPS security scanner series for example now leverages intelligent algorithms for faster, more accurate object detection. Additionally, two new solutions unveiled at MWC 2026 are powered by AI: TechAssist, a feature that uses natural language to control the CMX500 radio communication tester for 5G NR, enabling rapid test-scenario setup and status and configuration queries, and ScriptAssist that simplifies scripting for protocol and application testing and instrument automation.
“A user having a conversation with its oscilloscope is not far off,” said Braunstorfinger.
Outside of products, R&S is also embedded AI in its software development processes and across the engineering workflow.
The result is a win-win for both the company and its consumers. Combined AI-supported development and AI-driven product capabilities is enabling R&S to bring to market smarter solutions that are faster to deploy and easier to use. “The result is a seamless experience where customers benefit from increased efficiency, enhanced performance, and shorter time-to-market,” Braunstorfinger said.
Part 2 to follow soon.
