User Task Analysis Techniques

💎 Overview of 70+ UX Metrics Struggling to choose the right metric for your UX task at hand? MeasuringU maps out 70+ UX metrics across task and study levels — from time-on-task and SUS to eye tracking and NPS (https://lnkd.in/dhw6Sh8u) 1️⃣ Task-Level Metrics Focus: Directly measure how users perform tasks (actions + perceptions during task execution). Use Case: Usability testing, feature validation, UX benchmarking. 🟢 Objective Task-Based Action Metrics These measure user performance outcomes. Effectiveness: Completion, Findability, Errors Efficiency: Time on Task, Clicks / Interactions 🟢 Behavioral & Physiological Metrics These reflect user attention, emotion, and mental load, often measured via sensors or tracking tools. Visual Attention: Eye Tracking Dwell Time, Fixation Count, Time to First Fixation Emotional Reaction: Facial Coding, HR (heart rate), EEG (brainwave activity) Mental Effort: Tapping (as proxy for cognitive load) 2️⃣ Task-Level Attitudinal Metrics Focus: How users feel during or after a task. Use Case: Post-task questionnaires, usability labs, perception analysis. 🟢 Ease / Perception: Single Ease Question (SEQ), After Scenario Questionnaire (ASQ), Ease scale 🟢 Confidence: Self-reported Confidence score 🟢 Workload / Mental Effort: NASA Task Load Index (TLX), Subjective Mental Effort Questionnaire (SMEQ) 3️⃣ Combined Task-Level Metrics Focus: Composite metrics that combine efficiency, effectiveness, and ease. Use Case: Comparative usability studies, dashboards, standardized testing. Efficiency × Effectiveness → Efficiency Ratio Efficiency × Effectiveness × Ease → Single Usability Metric (SUM) Confidence × Effectiveness → Disaster Metric 4️⃣ Study-Level Attitudinal Metrics Focus: User attitudes about a product after use or across time. Use Case: Surveys, product-market fit tests, satisfaction tracking. 🟢 Satisfaction Metrics: Overall Satisfaction, Customer Experience Index (CXi) 🟢 Loyalty Metrics: Net Promoter Score (NPS), Likelihood to Recommend, Product-Market Fit (PMF) 🟢 Awareness / Brand Perception: Brand Awareness, Favorability, Brand Trust 🟢 Usability / Usefulness: System Usability Scale (SUS) 5️⃣ Delight & Trust Metrics Focus: Measure positive emotions and confidence in the interface. Use Case: Branding, premium experiences, trust validation. Top-Two Box (e.g. “Very Satisfied” or “Very Likely to Recommend”) SUPR-Q Trust Modified System Trust Scale (MST) 6️⃣ Visual Branding Metrics Focus: How users perceive visual design and layout. Use Case: UI testing, branding studies. SUPR-Q Appearance Perceived Website Clutter 7️⃣ Special-Purpose Study-Level Metrics Focus: Custom metrics tailored to specific domains or platforms. Use Case: Gaming, mobile apps, customer support. 🟢 Customer Service: Customer Effort Score (CES), SERVQUAL (Service Quality) 🟢 Gaming: GUESS (Game User Experience Satisfaction Scale) #UX #design #productdesign #measure

You must know these 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 as an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. If you are building Agentic Systems in an Enterprise setting you will soon discover that the simplest workflow patterns work the best and bring the most business value. At the end of last year Anthropic did a great job summarising the top patterns for these workflows and they still hold strong. Let’s explore what they are and where each can be useful: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗖𝗵𝗮𝗶𝗻𝗶𝗻𝗴: This pattern decomposes a complex task and tries to solve it in manageable pieces by chaining them together. Output of one LLM call becomes an output to another. ✅ In most cases such decomposition results in higher accuracy with sacrifice for latency. ℹ️ In heavy production use cases Prompt Chaining would be combined with following patterns, a pattern replace an LLM Call node in Prompt Chaining pattern. 𝟮. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: In this pattern, the input is classified into multiple potential paths and the appropriate is taken. ✅ Useful when the workflow is complex and specific topology paths could be more efficiently solved by a specialized workflow. ℹ️ Example: Agentic Chatbot - should I answer the question with RAG or should I perform some actions that a user has prompted for? 𝟯. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Initial input is split into multiple queries to be passed to the LLM, then the answers are aggregated to produce the final answer. ✅ Useful when speed is important and multiple inputs can be processed in parallel without needing to wait for other outputs. Also, when additional accuracy is required. ℹ️ Example 1: Query rewrite in Agentic RAG to produce multiple different queries for majority voting. Improves accuracy. ℹ️ Example 2: Multiple items are extracted from an invoice, all of them can be processed further in parallel for better speed. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: An orchestrator LLM dynamically breaks down tasks and delegates to other LLMs or sub-workflows. ✅ Useful when the system is complex and there is no clear hardcoded topology path to achieve the final result. ℹ️ Example: Choice of datasets to be used in Agentic RAG. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: Generator LLM produces a result then Evaluator LLM evaluates it and provides feedback for further improvement if necessary. ✅ Useful for tasks that require continuous refinement. ℹ️ Example: Deep Research Agent workflow when refinement of a report paragraph via continuous web search is required. 𝗧𝗶𝗽𝘀: ❗️ Before going for full fledged Agents you should always try to solve a problem with simpler Workflows described in the article. What are the most complex workflows you have deployed to production? Let me know in the comments 👇

✅ How To Run Task Analysis In UX (https://lnkd.in/e_s_TG3a), a practical step-by-step guide on how to study user goals, map user’s workflows, understand top tasks and then use them to inform and shape design decisions. Neatly put together by Thomas Stokes. 🚫 Good UX isn’t just high completion rates for top tasks. 🤔 Better: high accuracy, low task on time, high completion rates. ✅ Task analysis breaks down user tasks to understand user goals. ✅ Tasks are goal-oriented user actions (start → end point → success). ✅ Usually presented as a tree (hierarchical task-analysis diagram, HTA). ✅ First, collect data: users, what they try to do and how they do it. ✅ Refine your task list with stakeholders, then get users to vote. ✅ Translate each top task into goals, starting point and end point. ✅ Break down: user’s goal → sub-goals; sub-goal → single steps. ✅ For non-linear/circular steps: mark alternate paths as branches. ✅ Scrutinize every single step for errors, efficiency, opportunities. ✅ Attach design improvements as sticky notes to each step. 🚫 Don’t lose track in small tasks: come back to the big picture. Personally, I've been relying on top task analysis for years now, kindly introduced by Gerry McGovern. Of all the techniques to capture the essence of user experience, it’s a reliable way to do so. Bring it together with task completion rates and task completion times, and you have a reliable metric to track your UX performance over time. Once you identify 10–12 representative tasks and get them approved by stakeholders, we can track how well a product is performing over time. Refine the task wording and recruit the right participants. Then give these tasks to 15–18 actual users and track success rates, time on task and accuracy of input. That gives you an objective measure of success for your design efforts. And you can repeat it every 4–8 months, depending on velocity of the team. It’s remarkably easy to establish and run, but also has high visibility and impact — especially if it tracks the heart of what the product is about. Useful resources: Task Analysis: Support Users in Achieving Their Goals (attached image), by Maria Rosala https://lnkd.in/ePmARap3 What Really Matters: Focusing on Top Tasks, by Gerry McGovern https://lnkd.in/eWBXpCQp How To Make Sense Of Any Mess (free book), by Abby Covert https://lnkd.in/enxMMhMe How We Did It: Task Analysis (Case Study), by Jacob Filipp https://lnkd.in/edKYU6xE How To Optimize UX and Improve Task Efficiency, by Ella Webber https://lnkd.in/eKdKNtsR How to Conduct a Top Task Analysis, by Jeff Sauro https://lnkd.in/eqWp_RNG [continues in the comments below ↓]

2 — Solving Goal & Priority Misalignment with Is/Is Not + Perspective Circle.  SOLVING THINGS with SYSTEMS THINKING (STwST) — a series of mini, real-world applications of DSRP. When a team says, “We’re working hard but not pulling in the same direction,” it’s usually not a motivation problem. And it’s rarely a communication problem. It’s a distinction + perspective problem. Different people are carrying different mental pictures of what the goal is and is not, and different perspectives on what actually counts as a priority. So even when everyone uses the same words, they’re not aiming at the same thing. They might be reading the same page but interpreting it differently. Two simple thinking moves fix this. The first is an Is / Is Not list. Take the goal and the priorities and make them explicit: what this goal is, what it is not; what matters now, and what does not. This forces clarity where assumptions usually hide. The second is a Perspective Circle. You don’t need everyone to think the same way—but you do need everyone looking at the same picture. Different roles, levels, and functions can keep their own viewpoints, as long as they’re all anchored to the same shared view. Then keep that shared model on the table. Revisit it at the start of meetings. Use it when tradeoffs show up. Let people argue with it, stress-test it, and refine it. Don’t laminate it. Put it to work. Alignment doesn’t come from hearing the right words once. It comes from people rebuilding their own internal picture until it matches the shared one. When that happens, language cleans up, decisions get faster, resources line up, and the friction fades—because action always follows the mental model. If you listen carefully, misalignment announces itself in sentences that shouldn’t exist if the goal were truly shared. Those sentences are the signal. #STwST #SystemsThinking #CabreraLabPodcast #SystemsThinkingStandardsInstitute

The whole point of agentic systems is not just about solving but automating complex workflows. Agentic workflows are quickly becoming the dominant paradigm for AI applications. Agentic workflows commonly coordinate multiple models and tools with complex control logic. What happens when you have to coordinate more complex processes that go beyond a single agent’s scope? This is where agentic workflows come into the picture. An agentic workflow is a multi-step, dynamic process that orchestrates multiple API calls, AI tasks, agents, and even human-in-the-loop steps within a dynamic control graph. The workflow can branch, loop, or change course based on AI-driven evaluations, allowing it to adapt in real time. Rather than embedding all logic inside a single agent, the workflow externalizes decision points and coordinates agents and services. Agentic workflows enable output validation, decision overriding, human oversight, and other observability features out-of-the-box. This is crucial for enterprise uses where governance over autonomous agents is needed. Example use cases: ➟ Threat detection pipelines ➟ Fraud or claims processing ➟ Research assistants coordinating search, summarization, and synthesis. Key elements: ➟ Task Nodes: AI agents, LLM tasks, API calls, database queries, manual review steps ➟ Decision Nodes: AI-driven logic for routing control flow. ➟ Working Memory: Shared state across workflow steps. ➟ Flexible Control Flow: Branching, looping, and fallback paths for dynamic control. Essentially, the workflow provides a structure within which the AI agent can choose different paths or repeat steps as needed. Know more about agentic workflows: https://lnkd.in/gKrJ3ddK Here is my practical guide on building agentic applications/systems: https://lnkd.in/gh5S8KiH Here is my hands-on guide on building agentic workflows: https://lnkd.in/ggCaDm7z

How do you align an entire company around the same goals? It’s something we consider very important at Thinkific especially as the team has grown. Recently, we started rolling out V2MOM to help bring more structure and clarity to that process. For anyone unfamiliar, V2MOM is a goal-setting framework created by Marc Benioff at Salesforce. It stands for Vision, Values, Methods, Obstacles and Measures — a simple but powerful way to clarify what you’re trying to achieve, how you’ll get there and what might stand in your way. We’ve used a few goal setting frameworks over the years (OKRs, Rockefeller Habits) but something always felt like it was missing. I felt we had room for improvement in how we identified obstacles and anchored goals in guided principles. What I like about V2MOM is the structure. It’s not just about setting a vision and defining success, it also forces you to think through the values that guide your work, the potential obstacles and the specific methods you'll use to get there. Another shift for us is in how we cascade goals. My V2MOM connects directly to my direct reports’, and theirs to their teams. There’s still room for team-level priorities, but everything ties back to the company’s broader vision. That level of alignment brings a lot more clarity: on what we’re doing, what we’re not and how each person contributes to the big picture. So far, I’m a fan and I’ve also heard positive feedback from our team who’ve said V2MOM is helping reinforce a stronger sense of unity, shared goals and collective impact. It’s not a silver bullet, but it’s helping us be more intentional about both what we’re working toward and how we get there. Always curious — what frameworks or tools have you found most effective for aligning goals across your team or company?

If you’re building anything with LLMs, your system architecture matters more than your prompts. Most people stop at “call the model, get the output.” But LLM-native systems need workflows, blueprints that define how multiple LLM calls interact, how routing, evaluation, memory, tools, or chaining come into play. Here’s a breakdown of 6 core LLM workflows I see in production: 🧠 LLM Augmentation Classic RAG + tools setup. The model augments its own capabilities using: → Retrieval (e.g., from vector DBs) → Tool use (e.g., calculators, APIs) → Memory (short-term or long-term context) 🔗 Prompt Chaining Workflow Sequential reasoning across steps. Each output is validated (pass/fail) → passed to the next model. Great for multi-stage tasks like reasoning, summarizing, translating, and evaluating. 🛣 LLM Routing Workflow Input routed to different models (or prompts) based on the type of task. Example: classification → Q&A → summarization all handled by different call paths. 📊 LLM Parallelization Workflow (Aggregator) Run multiple models/tasks in parallel → aggregate the outputs. Useful for ensembling or sourcing multiple perspectives. 🎼 LLM Parallelization Workflow (Synthesizer) A more orchestrated version with a control layer. Think: multi-agent systems with a conductor + synthesizer to harmonize responses. 🧪 Evaluator–Optimizer Workflow The most underrated architecture. One LLM generates. Another evaluates (pass/fail + feedback). This loop continues until quality thresholds are met. If you’re an AI engineer, don’t just build for single-shot inference. Design workflows that scale, self-correct, and adapt. 📌 Save this visual for your next project architecture review. 〰️〰️〰️ 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

Could strategic misalignment be keeping you and your organization away from attaining maximum value? Executives and project managers are often rowing in different directions. The boat moves, but not necessarily toward value. From my doctoral research, and work with several clients, three pillars of strategic alignment consistently separate high-performing organizations from the rest: 1️⃣ Common Goals – A shared definition of success at both the strategic and operational levels. 2️⃣ Shared Language – Clear communication that bridges “executive speak” and project management terms. 3️⃣ Mutual Understanding – Executives gain insight into project realities, while PMs understand the strategic trade-offs leaders are balancing. The challenge? Most organizations talk about alignment but rarely make it a living system. That’s why I created the ALIGN™ Framework as a practical roadmap: 🪀 A – Assess the Value Chain → Define where value is created and lost. 🪀 L – Listen Across Levels → Build the “bilingual dictionary” across teams. 🪀 I – Integrate Strategy into Planning → Include PMs early in design, not just delivery. 🪀 G – Guide with Goals & Guardrails → Establish clarity with KPIs, OKRs, and constraints. 🪀 N – Navigate with Data & Confluence → Create mutual understanding with dashboards, forums, and collaboration tools. 🔑 ALIGN™ isn’t just an acronym. It’s the operating system for embedding the three pillars of Common Goals, Shared Language, and Mutual Understanding into everyday practice. When organizations apply it, strategy stops being a lofty document and becomes a lived reality. 📌 Question for you: In your organization, which of these three pillars: common goals, shared language, or mutual understanding requires the most urgent attention? Let's create the bride to ALIGN! ♻️Share to elevate others and follow🎙️Fola F. Alabi for more! #FolaElevates #StrategicLeadership #ProjectManagement #SPL #StrategicAlignment #Align #ExecutionExcellence #StrategicConfluenc

Stop Chasing AI Hype. Your Best Agentic AI Use Case Is Hiding in Your Biggest Bottleneck If you want to know where AI agents can create a 10x impact, don't look at the latest tech demos. Look for the places your teams can’t catch up — no matter how hard they work. I call this the "Bottleneck Test," a simple 3-step framework to find your best AI use cases. Step 1: IDENTIFY the Chronic Bottleneck Ask: "Where does the work never end?" At one of our clients, this was the engineering team's code review process. They were perpetually behind, not because they were bad at their jobs, but because they were outnumbered by the sheer volume of pull requests. The bottleneck was structural. This isn't just a tech problem. It happens everywhere: • Legal teams buried in standard contract reviews. • Finance departments manually reconciling thousands of invoices. • Marketing teams trying to qualify an endless flood of inbound leads. Step 2: QUALIFY the Use Case The best candidates for an AI agent are tasks that are repetitive, rules-based, and have clear success metrics. For our client, code review was perfect. It required checking against internal standards, security policies, and documentation—all data an AI agent could be trained on. Step 3: PILOT the Agent Our client introduced an AI code review agent as a pilot. It didn’t replace engineers. It augmented them. The agent handled the routine work—flagging common errors, checking for compliance, and summarizing changes—freeing up senior engineers to focus on complex architectural issues. The results were transformative: • Cycle times dropped by 40%. • Code quality and security posture improved. • Engineers could finally focus on meaningful work. Your roadmap for Agentic AI shouldn't be a list of technologies to try. It should be a list of your most critical business bottlenecks to solve. What is the biggest "work never ends" bottleneck in your organization? Share in the comments—let's discuss which ones are prime candidates for an AI agent. Zinnov Dipanwita Ghosh Namita Adavi ieswariya k Arpit Bhatia Amita Goyal Karthik Padmanabhan Mohammed Faraz Khan Komal Shah Ashveen Pai Hani Mukhey Anandhu Ajith Vyas Vandna Lal

It’s company offsite season! After flying coast to coast over the past few weeks facilitating leadership offsites, here are the biggest takeaways I've noticed: The highest-performing teams aren’t waiting for problems to invest in alignment, they’re doubling down before things break. And that’s not just a “nice to have.” Gallup consistently finds that highly engaged teams see 21% higher profitability and significantly lower turnover. Alignment is not fluff. It’s a performance lever. Here’s what my strongest clients are focused on: 1️⃣ Invest before there’s a fire The teams who brought me in weren’t floundering. They were flourishing. They knew ambitious goals require clarity, energy, and trust to sustain them. You can’t sprint a marathon. Momentum comes from recognizing the work and investing in people before burnout shows up. 2️⃣ Start with values, not tactics When I build an Ops Playbook with a team, we start by mapping values in behavior terms. It's not about typographic posters or slogans. It's about how we use values to make decisions. Aligned values reduce friction, and nothing is more expensive to a team than friction. 3️⃣ Connect every role to the bigger picture Whether it’s a team of freelancers or ten-year veterans, the shift is the same - task lists drive completion of to-do lists, whereas outcomes drive ownership. McKinsey research shows employees who understand how their work contributes to company goals are significantly more motivated and productive. The difference between “checking boxes” and “driving outcomes” is context. 4️⃣ Set KPIs tied to controllable inputs Of course revenue and profitability matter, but most team members don’t control revenue directly. With teams I work with, we focus on KPIs tied to what each function actually owns. The inputs that move the needle. Clear, controllable scoreboards create focus and accountability. These sessions are my favorite to lead because it’s the moment everything clicks. The team leader or business owner's vision translates into action and people see their role more clearly. You can literally see the energy shifting in the room. Whether you have two hours or two days, aligning around these four areas is rocket fuel for performance. If you’re planning an offsite this season, don’t just fill the agenda, use it to build the foundation your 2026 goals require.