As AI transforms the technology stack one small thing at a time, an equally profound change is taking place in test and measurement
With the advent of AI, test and measurement is moving beyond its pass-fail era where it could merely detect deviations from predefined thresholds to a more mature model powered by AI, that can score a system based on how well it is performing.
Vendors across the industry are embracing AI to modernize legacy test processes, gain operational efficiency, and save time and resources. Based on our observations, here are three ways they are tapping into AI.
AI as the test co-pilot
If used correctly, AI can be a great productivity enhancer. T&M vendors are increasingly leveraging this aspect to support their workforce.
One way they are doing it is by embedding generative AI chatbots into everyday tools and workflows, enabling engineers without deep T&M specialization to detect and debug issues in a short timespan. Chatbots trained on internal datasets can also help non-technical personas search and find information about proprietary test systems, how they work, new features, upgrades, and so on, without getting into the technicalities. This can help organizations overcome a growing expertise gap seen across the tech industry.
GenAI can not only provide fast responses to prompts, but when programmed, it can also make recommendations. For test engineers, this is valuable for a number of reasons. For example, the chatbots can help identify the optimal test instruments for an use case, provide quick troubleshooting steps for faster mean time to repair (MTTR), or just best practices for successful implementation of solution.
Other examples include generating ready-to-use test cases from plain-language intent, and guiding instruments like fiber splicers toward the optimal way to perform their work.
All of these cut down time and effort, while reducing errors common in repeated manual tasks.
Test data analysis
One of AI’s superpowers is parsing through large volumes of data faster than any human can. Good AI models can make sense of messy, unstructured data and translate it into actionable business intelligence all in minutes. That capability is proving increasingly valuable for test data analysis as vendors deal with extraordinary volumes of input data.
AI/ML models are helping teams cut through noisy data coming from disparate systems and drawing correlations that would otherwise take them a lot longer to perform. The models’ ability to compress steps, like data cleaning, tokenization, processing, visualization, all into a single workflow saves time, while allowing team members to query the results in natural language, pulling specific metrics without needing to dig through raw data themselves.
The same analytics can feed back into the development of next-generation test equipment, helping vendors build better tools.
Anomaly detection
AI’s superior pattern recognition capabilities are particularly useful for catching test-related issues in customer environments. Vendors are now embedding AI locally in their hardware where algorithms can analyze incoming test data from customer environments and surface silent errors before they escalate into something worse.
The same capability extends to predictive maintenance, whereby it can prevent expensive failures in equipment. By learning from historical data, the models can predict equipment failures before they occur. This has a meaningful advantage in mission-critical industries like aerospace, defense, and utilities where unexpected failures can lead to serious consequences.
