How to Build Trust Through Standardized Measurement

𝔼𝕍𝔸𝕃 field note (2 of 3): Finding the benchmarks that matter for your own use cases is one of the biggest contributors to AI success. Let's dive in. AI adoption hinges on two foundational pillars: quality and trust. Like the dual nature of a superhero, quality and trust play distinct but interconnected roles in ensuring the success of AI systems. This duality underscores the importance of rigorous evaluation. Benchmarks, whether automated or human-centric, are the tools that allow us to measure and enhance quality while systematically building trust. By identifying the benchmarks that matter for your specific use case, you can ensure your AI system not only performs at its peak but also inspires confidence in its users. 🦸♂️ Quality is the superpower—think Superman—able to deliver remarkable feats like reasoning and understanding across modalities to deliver innovative capabilities. Evaluating quality involves tools like controllability frameworks to ensure predictable behavior, performance metrics to set clear expectations, and methods like automated benchmarks and human evaluations to measure capabilities. Techniques such as red-teaming further stress-test the system to identify blind spots. 👓 But trust is the alter ego—Clark Kent—the steady, dependable force that puts the superpower into the right place at the right time, and ensures these powers are used wisely and responsibly. Building trust requires measures that ensure systems are helpful (meeting user needs), harmless (avoiding unintended harm), and fair (mitigating bias). Transparency through explainability and robust verification processes further solidifies user confidence by revealing where a system excels—and where it isn’t ready yet. For AI systems, one cannot thrive without the other. A system with exceptional quality but no trust risks indifference or rejection - a collective "shrug" from your users. Conversely, all the trust in the world without quality reduces the potential to deliver real value. To ensure success, prioritize benchmarks that align with your use case, continuously measure both quality and trust, and adapt your evaluation as your system evolves. You can get started today: map use case requirements to benchmark types, identify critical metrics (accuracy, latency, bias), set minimum performance thresholds (aka: exit criteria), and choose complementary benchmarks (for better coverage of failure modes, and to avoid over-fitting to a single number). By doing so, you can build AI systems that not only perform but also earn the trust of their users—unlocking long-term value.

Green CSAT can still hide red flags. The momentum metric your CFO actually feels: Trust Delta™. A 4.7 CSAT score looks great on a dashboard. But it only shows how someone felt once — in a single interaction. It doesn’t tell you how much confidence they lost after an outage. It doesn’t show if trust recovered after a rocky release. And it doesn’t prove whether they’ll believe in you next time. That’s where Trust Delta™ comes in. It tracks how trust changes before and after key events — outages, incidents, major changes — so you can see the direction of the relationship, not just the temperature of a moment. 📈 +18% jump in confidence after a seamless change window. 📉 –27% dip following a poorly communicated outage. 🔁 +42% climb over three months as automation reduced ticket noise. Those aren’t satisfaction numbers. They’re momentum signals — and momentum is what executives actually fund. Because here’s the truth: CSAT tells you how you did yesterday. Trust Delta™ shows whether they’ll believe in you tomorrow. How to Use Trust Delta™ in Practice 1. Measure trust before and after every major event ↳ Add a simple confidence question after outages, changes, or releases: 2. “How much do you trust IT to deliver reliably?” Track the change, not just the score ↳ A shift from 62% to 80% is more valuable than a flat 78%. Momentum matters more than the moment. 3. Link trust movement to business decisions ↳ Use positive deltas to support funding conversations, and negative deltas to guide communication or process changes. Trust Delta™ isn’t about replacing CSAT. It’s about turning satisfaction into strategic insight — the kind that shapes decisions, earns influence, and moves IT from support to leadership. You can ONLY track CSAT or Trust Delta™ next quarter. Which one — and why? 📘 Trust Delta™ is one of the Grove Metrics I explore in The Grove Method for ITSM Excellence™ — where IT evolves from reactive service to trusted business partner. ♻️ Repost this if you’re ready to move beyond snapshots and start measuring momentum Follow Bob Roark for more Grove Metrics that turn IT from background noise into a business driver. #ITSM #CIO #Metrics #Trust #GroveMethod

For years, marketers have been forced to analyze performance in silos—evaluating Facebook in Ads Manager, Google in GA, TV through post-campaign lift reports. Each platform tells a different story, leaving teams to stitch together a fragmented view of performance. The problem? Siloed measurement doesn’t reflect how consumers actually move through the funnel. A purchase isn’t usually the result of a single channel—it’s the product of multiple touchpoints working together. Relying on platform-specific attribution ignores this complexity, leading to misallocated budgets and missed opportunities. This is where unified measurement comes in. By combining methodologies like Multi-Touch Attribution (MTA), Marketing Mix Modeling (MMM), and incrementality testing, marketers can move beyond siloed analysis and see the full picture. A unified approach ensures: -More accurate decision-making—by accounting for both granular, user-level data and broader, market-level trends. -Better budget allocation—understanding the true impact of each channel instead of over-relying on the last-click or individual platform metrics. -More trust in marketing data—giving finance and leadership a clear, consistent framework for investment decisions. The days of optimizing channels in isolation are over. Marketers who embrace unified measurement gain the clarity and confidence needed to drive real business outcomes. How is your team thinking about breaking down silos in measurement?

Measurement System Analysis (MSA): The Key to Reliable Data In quality management, decisions are only as good as the data we rely on. But what if our measurement system itself is flawed? That’s where Measurement System Analysis (MSA) comes in. What is MSA? MSA is a structured method used to evaluate the reliability of a measurement system by assessing its accuracy, precision, and consistency. It helps ensure that variations in data come from the process—not from the instruments or operators measuring it. Key Elements of MSA: ✅ Bias – The difference between the true value and the measured value. ✅ Linearity – Variation in bias across the measurement range. ✅ Stability – Consistency of measurements over time. ✅ Repeatability – Variation when the same person measures the same part multiple times. ✅ Reproducibility – Variation when different people measure the same part using the same method. Why is MSA Important? 🔹 Ensures reliable data for decision-making. 🔹 Identifies and reduces measurement errors. 🔹 Improves process control and product quality. 🔹 Supports Lean & Six Sigma initiatives. 🔹 Enhances confidence in inspections and audits. 🔹 Reduces rework and scrap caused by measurement variations. 🔹 Helps standardize measurement methods across teams. 🔹 Ensures regulatory compliance in manufacturing and automotive industries. 🔹 Strengthens supplier-customer relationships through data-driven quality control. 🔹 A poor measurement system can lead to costly mistakes—MSA helps prevent them. A robust measurement system builds trust , enhances process stability, and drives continuous improvement. ✅ #QualityAssurance #MeasurementSystemAnalysis #SixSigma #ManufacturingExcellence #LeanManufacturing #ProcessImprovement #GRRandMSA #DataDriven #QualityMatters