The promise of artificial intelligence (AI) in test and validation is compelling: faster insights, automated pattern recognition, and predictive capabilities that can improve how engineering teams work. Beneath that excitement lies a hard truth.

AI success doesn’t start with algorithms. It starts with data—structured, governed, and context-rich data that can actually fuel meaningful results.

Walk into most test environments today, and you’ll encounter a familiar scene: engineers drowning in data but starving for insights. CSV files scattered across servers. Inconsistent naming conventions. Missing context that renders otherwise perfect measurements useless for analysis.

From semiconductor fabs to automotive test floors, engineering teams are discovering that terabytes of test data don’t automatically translate to AI readiness. In fact, it often reveals just how unprepared their infrastructure really is.

Based on insights from engineering leaders, data scientists, and AI experts working in real-world test environments, these five metrics offer a roadmap for moving from data collection to measurable results. Where does your team stand? 

What to measure: Percentage of test records that include the full context, not just the primary measurement

Data completeness in test environments goes far beyond capturing the primary measurement. It’s about preserving the entire context of each test execution, including device state, environmental conditions, instrument configurations, firmware versions, calibration status, and more.

Charles Schroeder, NI Fellow, Emerson puts it perfectly: “If I can store all the information...everything you would need to unpack or find a needle in the haystack...that’s what we mean by data.”

Teams are discovering that AI models trained on incomplete data sets often fail when deployed in real-world scenarios because they lack the context needed to make accurate predictions. Seemingly minor details—like temperature drift or instrument warm-up time—can be the difference between a robust model and an unreliable one. 

What to measure: How much of your data follows a shared schema or consistent naming structure across teams and tools

Inconsistent data labeling and structures create a scaling nightmare. When different teams use “voltage_reading,” “V_meas,” or “V” to describe the same measurement type, the resulting data set frustrates both human analysts and machine learning algorithms alike.

“You can have test data that is absolutely correct—and yet completely impossible to analyze,” explains Terry Duepner, NI Chief Test Engineer, Emerson. It’s a paradox that’s frustrating teams worldwide: technically accurate data that’s practically worthless for AI applications.

To eliminate this hurdle, organizations are adopting schema-first approaches. Rather than retrofitting structure onto existing data, teams are implementing standardized, extensible schemas from the ground up. These frameworks provide consistency without rigidity, allowing for evolution while maintaining compatibility across teams, tools, and time.

What to measure: Percentage of AI recommendations that include traceable metadata and confidence scores

Engineers are skeptical by nature, and when it comes to AI recommendations, that skepticism becomes a superpower. In high-stakes engineering environments, trusting AI recommendations without understanding their basis is risky. How do you know the AI interpreted your data correctly? What if it flagged the wrong issue or missed a critical pattern?

The AI systems that succeed in engineering environments show their work. What data did they look at? What patterns did they identify? How confident are they in their conclusions? AI should be like having a junior engineer who not only gives you the answer but explains how they got there.

It isn’t about making AI perfect. It’s about making it auditable, reviewable, and trustworthy enough so that engineers can review the reasoning, understand the basis for recommendations, and identify potential issues or limitations in the analysis.

What to measure: What is the average time between data collection and a decision or action?

AI’s most immediate value proposition in test environments isn’t sophistication, it’s speed. The ability to move rapidly from data collection to action can dramatically impact product development cycles and manufacturing efficiency. But current workflows often involve significant delays as engineers and data scientists spend substantial time preparing data for analysis rather than generating insights or solving problems. Today, more than 80 percent of time is spent simply preparing data for analysis—not generating insights, not solving problems, just dealing with format inconsistencies and missing metadata.

As Charles Schroeder asks, “What if instead of looking for needles in a haystack, AI could just bring you the needles?”

When you reduce the lag between collection and insight, you unlock huge advantages: faster product development cycles, quicker decision-making, and more time available for engineering activities rather than data processing tasks.

What to measure: What are the measurable improvements across yield, cost reduction, test cycle time, and engineering productivity?

The ultimate validation of AI implementation comes through tangible business metrics. Theoretical improvements in algorithm performance mean little if they don’t translate to real-world benefits.

The good news? Teams that get the foundation right are already seeing results. Alon Malki, NI Senior Director of Data Science, Emerson reports some impressive numbers with NI OptimalPlus GO users: “We’ve seen 15 to 25 percent cost reduction, up to two percent yield improvement in high-volume manufacturing, and up to 10 percent in NPI.”

These improvements occur through several mechanisms: reduced test execution time, faster root cause analysis, better defect prevention, and decreased engineering overhead. The cumulative impact can be significant for organizations that implement AI systems properly or find the right partner who can do the heavy lifting.

These five metrics don’t just define what success should look like, they’re already shaping how we approach our own AI implementation.

• Schema-first by design—Tools like NI TestStand and NI LabVIEW embed standardized data structures into test workflows, ensuring consistency from the start. 
• Built-in transparency—From data logging through model outputs, NI tools are developed to make every insight traceable and verifiable, so engineers stay in control. 
• Real-time insight—The Nigel™ AI Advisor lets engineers ask questions and receive fast, actionable answers with no scripting or manual analysis required. 
• Proven business impact—NI OptimalPlus GO enables manufacturers to take real-time actions at the edge, producing measurable improvement in yield, time to market, and cost reduction.

The transition from traditional test methodologies to AI-enhanced workflows represents more than a technological upgrade. It marks a shift in mindset from capturing data as an afterthought to treating it as a strategic asset.

Teams that invest in data quality, structure, and accessibility are setting themselves up for AI success—not someday, but today. The future of test and validation isn’t just about smarter algorithms. It’s about smarter data that makes those algorithms possible. Learn more about leading AI adoption in test and measurement.