Task Performance Indicators

From Noise to Signals: Making KRIs, KCIs, and KPIs Work Together in Real Risk Management In risk management, we often talk about data, dashboards, and monitoring - but what we don’t talk about enough is how easily it can all turn into noise. You’ve seen it. Endless Excel sheets. Power BI dashboards glowing red. Weekly reports full of charts - but no real insight. Activity ≠ Awareness. Reporting ≠ Risk oversight. Indicators ≠ Intelligence. So how do we go from noise to signals? Let’s decode the three key instruments on the risk radar and more importantly, how to make them work together. KPI: The Speedometer of Business KPIs (Key Performance Indicators) measure how well the business is performing. Think of them like the speedometer in your car. Revenue growth, customer churn, conversion rates - these are about how fast and how well you're moving forward. But driving fast without control? That’s not progress. That’s risk. KCI: The Health Check of Your Controls KCIs (Key Control Indicators) tell you if the brakes are working. They monitor whether critical controls - the ones that stop bad things from happening - are healthy and effective. Too often, businesses assume “no incidents” means “controls are working.” That’s like assuming your fire alarm works because the house hasn’t burned down. Examples? % of overdue access reviews Failed system patches over time % of customer complaints unresolved within SLA These tell you: Are your safeguards asleep at the wheel? KRI: The Storm on the Horizon KRIs (Key Risk Indicators) are your early warning system. They don’t measure performance or controls - they forecast potential threats to your objectives. A rising fraud attempt rate… An increase in staff attrition in critical teams… A regulatory backlog building up… Think of KRIs like the storm alert on your windscreen. You may be driving fast (KPI) with working brakes (KCI), but if there’s a storm ahead - you need to know now. Here’s the real insight: Alone, each one gives a piece of the story. Together, they tell you what matters. 🚗 KPI tells you how well you’re doing. 🛑 KCI tells you if your defences are holding. 🌩️ KRI tells you what’s coming around the bend. Real risk management isn’t about having hundreds of metrics. It’s about having the right mix of signals that speak to risk, resilience, and readiness. And here’s the punchline: If your KPIs are green but your KCIs and KRIs are red, you’re not winning - you’re just coasting toward a crash. In Closing Moving from noise to signal isn’t about more data - it’s about smarter insight. A well-tuned risk radar helps your organisation: Steer confidently through uncertainty Avoid preventable failures And make better, faster, risk-aware decisions. So the next time you see a dashboard… Ask: Are we measuring what matters- or just what’s easy?

When we run usability tests, we often focus on the qualitative stuff — what people say, where they struggle, why they behave a certain way. But we forget there’s a quantitative side to usability testing too. Each task in your test can be measured for: 1. Effectiveness — can people complete the task? → Success rate: What % of users completed the task? (80% is solid. 100% might mean your task was too easy.) → Error rate: How often do users make mistakes — and how severe are they? 2. Efficiency — how quickly do they complete the task? → Time on task: Average time spent per task. → Relative efficiency: How much of that time is spent by people who succeed at the task? 3. Satisfaction — how do they feel about it? → Post-task satisfaction: A quick rating (1–5) after each task. → Overall system usability: SUS scores or other validated scales after the full session. These metrics help you go beyond opinions and actually track improvements over time. They're especially helpful for benchmarking, stakeholder alignment, and testing design changes. We want our products to feel good, but they also need to perform well. And if you need some help, i've got a nice template for this! (see the comments) Do you use these kinds of metrics in your usability testing? UXR Study

When evaluating AI agents, accuracy alone is a poor proxy for performance. An agent’s goal isn’t to produce a correct answer, it’s to complete a task. And how reliably it does that depends on more than just model precision. Three metrics matter most: 1. Task Success Rate (TSR) Measures the percentage of end-to-end tasks completed correctly. This captures real-world reliability – can the agent consistently finish what it starts? 2. First-Try Success (FTS) Tracks how often the agent succeeds on its first attempt. This reflects reasoning quality and prompt grounding – whether it understands the task context accurately before acting. 3. Recovery Speed Captures how quickly, or in how many steps, the agent self-corrects after a mistake. This is the best signal of adaptability and robustness, which are critical for agents operating in dynamic environments. In complex, multi-step workflows, these metrics often tell a more complete story than accuracy or BLEU scores. An agent that can self-correct and adapt is far more valuable than one that only performs well under static test conditions. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

 Accuracy alone is a poor proxy for how well an AI agent actually performs. When you evaluate agents, ask yourself: Is the agent just getting the right answer, or is it finishing the job you gave it? The difference shows up in three key metrics: ☑ Task Success Rate (TSR)   Measures the percentage of end‑to‑end tasks completed correctly.   It tells you whether the agent can reliably finish what it starts in the real world. ☑ First‑Try Success (FTS)   Tracks how often the agent succeeds on its first attempt.   A high FTS means the agent understands the context and reasons well before it acts. ☑ Recovery Speed   Captures how quickly the agent self‑corrects after a mistake, measured in steps or time.   Fast recovery is the strongest signal of adaptability and robustness in dynamic environments. In multi‑step workflows these numbers paint a far richer picture than raw accuracy or BLEU scores. An agent that can self‑correct and keep moving forward is far more valuable than one that only shines in static tests. I’m Shrey & I share daily AI insights.  If this helped, hit the ♻️ reshare button so someone else can evaluate agents smarter too.  

Your AI agents might be fast, but are they efficient and accurate? Here's how to evaluate your AI Agents... Evaluating AI agents is crucial for building effective agentic applications. Given their complex architecture, different aspects require specific metrics and tools for evaluation. Let’s dive into key metrics to track: 📌 Technical Performance (For Engineers): Track how efficiently your agents handle tasks at the technical level: ↳ Latency per Tool Call - Measures the time taken for tool interactions ↳ API Call Frequency - Tracks the number of external API calls ↳ Context Window Utilization - Examines how well LLMs manage their context. ↳ LLM Call Error Rate - Evaluates the frequency of failures in model responses to address issues like limits or misaligned prompts. 📌 Cost and Resource Optimization (For Business Leaders) Evaluate cost efficiency and resource usage to ensure scalability: ↳ Total Task Completion Time - Tracks the overall time required for task completion, highlighting bottlenecks ↳ Cost per Task Completion - Measures financial resources spent per task ↳ Token Usage per Interaction - Monitors token consumption to optimize payloads and lower costs 📌 Output Quality (For Quality Assurance Teams) Ensure the outputs generated meet the required standards: ↳ Instruction Adherence - Validates compliance with task specifications to reduce errors. ↳ Hallucination Rate - How often an AI generates incorrect, irrelevant, or nonsensical outputs. ↳ Output Format Success Rate - Ensures the structure of outputs (e.g., JSON, CSV) is accurate, preventing compatibility ↳ Context Adherence - Assesses if responses align with input context 📌 Usability and Effectiveness (For Product Owners) Measure how well your agents meet user needs and achieve goals. ↳ Agent Success Rate - Tracks the percentage of Agentic tasks completed successfully. ↳ Event Recall Accuracy - Measures the accuracy of the agent's episodic memory recall ↳ Agent Wait Time - Measures the time an agent waits for a task, tool, or resource. ↳ Task Completion Rate - Monitors the ratio of tasks started versus completed. ↳ Steps per Task - Counts steps needed for task completion, highlighting inefficiencies. ↳ Number of Human Requests - Measures the frequency of user intervention to address gaps in automation. ↳ Tool Selection Accuracy - Assesses if agents choose appropriate tools for tasks. ↳ Tool Argument Accuracy - Validates the correctness of tool input parameters. ↳ Tool Failure Rate - Monitors tool failures to identify and fix unreliable components. Note: Not all metrics are necessary for every use case. Select those aligned with your specific objectives. What metrics are you prioritizing when evaluating AI agents? Let me know in the comments below 👇 Please make sure to, ♻️ Share 👍 React 💭 Comment to help more people learn © Follow this guide if you want to use our content: https://lnkd.in/gTzk2k4b

Measure maintenance performance = unlock reliability + reduce cost That's the purpose of Maintenance KPIs. Maintenance KPIs give you a clear, data-driven view of how well your assets—and your maintenance process—are performing. They turn everyday activity into measurable performance, enabling teams to: ➡️Diagnose ➡️Prioritize ➡️Improve Here’s how the core KPIs work: 1️⃣ MTBF — Mean Time Between Failures • Measures average run time between equipment failures. • Formula: MTBF = Total Operating Time ÷ Number of Failures • Higher MTBF = more reliable equipment and more stable processes. • Reflects design quality, operating conditions, and maintenance effectiveness. 2️⃣ MTTR — Mean Time To Repair • Measures how long it takes to detect, troubleshoot, repair, and restore equipment. • Formula: MTTR = Total Downtime ÷ Number of Breakdowns • Lower MTTR = faster recovery, better skill levels, fewer production interruptions. • Includes fault detection, parts replacement, and functional testing. 3️⃣ Availability • Indicates the percentage of time equipment is ready for use. • Formula: Availability = MTBF ÷ (MTBF + MTTR) • High availability combines strong reliability (MTBF) with fast recovery (MTTR). • A core measure for stable, predictable operations. 4️⃣ PM Compliance — Preventive Maintenance Completion Rate • Tracks how many scheduled PM tasks are completed on time. • Formula: PM Compliance = Completed PMs ÷ Scheduled PMs • High compliance reduces unplanned downtime and extends asset life. • Low compliance signals risk exposure and rising corrective maintenance costs. Related Analysis Tools • Fault Tree Analysis (FTA) • Root Cause Analysis (RCA) • Failure Modes & Effects Analysis (FMEA) CMMS = the backbone of modern maintenance A CMMS (IFS, CosWin, etc.) centralizes every maintenance activity—work orders, equipment history, schedules, spare parts, and performance metrics. It automatically calculates MTBF, MTTR, availability, and PM compliance. With a CMMS, teams shift from reactive to predictive and reliability-centered maintenance. The result: lower downtime, better asset health, and data-driven decisions that support Industry 4.0 operations. Why track Maintenance KPIs? ✅ Improve overall maintenance performance ✅ Increase reliability and availability ✅ Reduce downtime and maintenance costs ✅ Optimize spare parts and resource planning ✅ Enable smarter, evidence-based decisions

How do you include metrics in your case study? Especially if it’s not a real-world case study. Here are some ideas. 👇 1. Task Success Rate How often did the user complete a specific task (that you indicated) using your prototype? This gauges the usability of your design. For example, if you had 5 users try this task and 3 of them completed the task, you had a 60% Task Success Rate. (You really want it to be higher!). But maybe you go back and re-iterate your design based on user feedback and now you find you get a 90% task success rate. That’s worth noting in your case study, including the results of both tests so hiring managers know you improved the task rate because you observed user feedback and you re-iterated your design. 2. Time on Task How long does it take to complete a given task? This indicates the efficiency of your design. It’s best if you can compare the first time someone tries to complete the task (using one of your first iterations) to how long it takes after your final design. This shows improvement as you re-iterated your design based on user feedback. Another idea is to compare your time against industry standards or to how long it takes to complete the same task on a competitor’s site. 3. User Error Rate How many mistakes does a user make when completing a certain task? This determines how user-friendly your design is and helps define pain points. To use this rate, you must first define the total number of possible errors when completing the task. It may be just one (ie. enter your username) or several (enter username and password). (This is a very simplified example.) To find the user error rate: total number of errors occurred / total number of possible errors So, if 2 errors are possible in the task, and 5 people attempted the task and there were a total of 3 errors made. The user error rate is: 30% (3 / (2 x 5). The lower the number the better. 😊 4. Customer(User) Satisfaction Score This measures how happy your customer or user is about a specific product or feature and their user experience. The typical question is, “Did you find our app [or specific feature/task] do what you needed it to today?" And, offer a Yes/No response. PS. It’s best to plan for these ‘tests’ as you start the project. However, hindsight is always better than foresight. If you realize after the fact (which happens a lot with beginning UX/UI’ers), you won’t be able to show how these rates improved throughout your design process, but you can share the end results. And, you can potentially compare that against competitor results or industry standards. PSS. Share these metrics on your resume, too. Recruiters and hiring managers like to see impact. 😊 What other metrics do you suggest for showing impact in your case studies? #uxdesign #metrics #results #casestudies #designportfolios