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As AI transforms hardware and network infrastructures, 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 on from its pass-fail era where it could merely detect deviations of predefined thresholds to a more mature one – powered by AI itself – where it can score how well a system is performing.
Vendors are embracing AI to modernize legacy test processes, gain operational efficiency, and save time and resource. Based on our observation, here are three ways vendors in the space are tapping into AI to elevate their test and measurement processes.
AI as the test co-pilot
If used right, AI can be a great productivity enhancer. T&M vendors in different industries are increasingly leveraging this to support their workforce.
One of the ways they are doing it is by embedding GenAI chatbots into their day-to-day processes and workflows. The chatbots are enabling engineers without deep T&M specialization to detect and debug issues, helping organizations overcome a growing expertise gap. Chatbots trained on internal datasets can also help non-technical personas search and find information on proprietary test systems, how they work, new features and upgrades, etc. without getting into the technicalities.
Generative AI can not only spit out responses on prompts, but when programmed to do so, it can even make recommendations, lowering the bar even further. For test engineers, this is valuable for a number of reasons. For example, the chatbots can translate intents into test cases, help find the optimal test instruments for use cases.
AI-powered automation is another way to amp up workforce productivity without compromising on quality or accuracy. A great example is AI-powered splicing machines where AI is helping the machines determine the most optimal way to perform the splicing and fusing.
All of these significantly cut down time and effort, and reduce error in repeated tasks.
Test data analysis
One of AI’s superpowers is to parse through large volumes of data faster than any human can. Good AI models can make sense of data and translate it into actionable intelligence in real-time. That capability is proving increasingly valuable for test data analysis as vendors deal with extraordinary volumes of input data.
They use AI/ML models to process noisy datasets coming in from a variety of systems and make correlations quickly. The built-in models can compress time-consuming steps like, data cleaning, tokenization, processing and visualization, into one swift action, serving high-quality analytics through the other end in a fraction of time. At their end, users can even query the information in natural language for research and analysis.
The analytics are then used to inform next generation test equipment or solutions for enhanced quality.
Anomaly detection
AI models possess superior pattern recognition capabilities which are useful for catching testing issues in customer environments.
Vendors are now embedding AI locally in their hardware to detect hard-to-see anomalies. As test data comes in from the customers’ ecosystems, the algorithms kick off, analyzing the information and spotting silent errors in electronics and AI systems before they escalate. These defects are often missed by traditional anomaly detection tools.
AI can also prevent expensive failures in equipment used in aerospace, defense and utilities by predicting future issues by learning from past data. This makes predictive maintenance possible, especially for systems operating in mission-critical environments.