Task Relevance Analysis

Ever wonder how we can better understand user tasks and create designs that truly support users in accomplishing their goals? Task analysis is a powerful technique that can help us break down tasks, understand user goals, and inform design decisions early in the product design lifecycle. In UX projects, task analysis helps us evaluate how effectively an interface enables task completion by breaking down user actions into steps. This process allows us to uncover hidden complexities, even in simple tasks, and identify where users might make mistakes or face challenges. So, how do you conduct a task analysis? Here’s a quick overview: 1. Collect information about the task: Understand your users, their goals, and how they currently accomplish the task. 2. Describe the user’s goal: Identify the start and end points, and place the goal at the top of the hierarchy. 3. Split the user’s goal into sub-goals: Break the task into actionable sub-goals. 4. Break each sub-goal into a sequence of steps: Include mental and physical actions required to complete each sub-goal. 5. Inspect the hierarchy of the task analysis for design opportunities: Look for errors, inefficiencies, and opportunities for improving the design. By integrating task analysis early in your design process, you can define user goals, evaluate task completion, and identify design opportunities that improve user efficiency and effectiveness. How do you use task analysis in your UX projects?

One of the challenges with introducing AI into users’ workflows and decision support systems is how people work. 🧠 𝐖𝐡𝐞𝐧 𝐚𝐧𝐚𝐥𝐲𝐳𝐢𝐧𝐠 𝐡𝐨𝐰 𝐩𝐞𝐨𝐩𝐥𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐰𝐨𝐫𝐤 𝐭𝐚𝐬𝐤𝐬, 𝐈'𝐯𝐞 𝐪𝐮𝐢𝐜𝐤𝐥𝐲 𝐫𝐞𝐚𝐥𝐢𝐳𝐞𝐝 𝐨𝐧𝐞 𝐬𝐢𝐳𝐞 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐟𝐢𝐭 𝐚𝐥𝐥. A key challenge…The gap between descriptive task analysis (how work is 𝒂𝒄𝒕𝒖𝒂𝒍𝒍𝒚 done) and prescriptive task analysis (how work 𝒔𝒉𝒐𝒖𝒍𝒅 be done). 👩💻 Descriptive analyses reveal what I like to call the “𝐦𝐞𝐬𝐬𝐲 𝐦𝐢𝐝𝐝𝐥𝐞.” The real-world intricacies of user behavior, shortcuts, and workarounds. 📋 Prescriptive analyses focus on ideal workflows, often shaped by organizational goals or best practices. Both are essential. But the mismatch can lead to: ❌ Misaligned AI functionality that ignores real user needs. ❌ Disruption of workflows, causing frustration rather than efficiency. 🎯 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐟𝐨𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐫𝐬 1️⃣ 𝐁𝐥𝐞𝐧𝐝 𝐭𝐡𝐞 𝐭𝐰𝐨 𝐭𝐡𝐞𝐧 𝐭𝐞𝐬𝐭: Use descriptive insights to ground prescriptive goals in reality. Let actual user behavior guide refinement. Test AI tools in live settings and continuously adjust for unexpected user behaviors. It’s not one-and-done. You have to iterate. 2️⃣ 𝐌𝐚𝐩 𝐭𝐚𝐬𝐤 𝐭𝐲𝐩𝐞 𝐚𝐧𝐝 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: Identify tasks AI can augment (data analysis, recommendations) versus tasks it might automate (routine, repetitive steps). Where AI is a partner in decision making, incorporate explainability into its design. Help users understand the recommendations. Support user trust. 3️⃣ 𝐔𝐬𝐞𝐫-𝐜𝐞𝐧𝐭𝐞𝐫𝐞𝐝 𝐀𝐈 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Create adaptive AI that learns from user input and gets better over time. 𝐓𝐚𝐬𝐤 𝐚𝐧𝐚𝐥𝐲𝐬𝐞𝐬 𝐚𝐫𝐞 𝐚 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐟𝐢𝐫𝐬𝐭 𝐬𝐭𝐞𝐩 𝐢𝐧 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭𝐧𝐞𝐫, 𝐧𝐨𝐭 𝐚 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Have you accounted for task analyses in your AI implementation strategy that help avoid costly rework and churn? #AI #taskanalysis #humanfactors #humancentereddesign #workflowoptimization #industrialengineering #systemsdesign

TQSolutions #skills research has taken a detour into the complementary world of #taskintelligence and #workanalysis and #workdesign. As I have recently shared, it appears that task intelligence is a critical ingredient for a successful #skillsinformed workforce strategy. Rather than relying on #hrtech vendors and other market commentators I have chosen to review some recent academic papers on the topic to see what evidence-based research can show us. This morning my Google Notebook LM has ingested three great papers on the topic and I found this one particularly insightful. A team from Boston Consulting Group (BCG) and representatives from key academic schools including Harvard Business School, MIT Sloan School of Management and The Wharton School undertook a study of the work being performed by knowledge workers, in this case management consultants (2023). There were plenty of key insights and conclusions but I particularly valued these: Concept of 'Jagged Technological Frontier' that shows that Ai is not uniformally effective across tasks, even if tasks are similar in complexity i.e. the frontier is jagged. This highlights the need for task-by-task analysis when considering Ai and automation. There is risk that performance is degraded if Ai is applied to tasks outside of the 'frontier' without this in-depth task analysis. The paper suggests businesses that can successfully navigate this jagged frontier and integrate AI strategically will gain a significant competitive advantage. They will be more productive, more innovative and more responsive to customer needs. Implications for HR: *Task Deconstruction: Deconstruct roles into specific tasks and identify which are most suitable for AI and which require human skills. *Skills Mapping: Map current skills and identify any gaps in relation to the new demands of human-led tasks. *Training: Provide training that focuses on using AI effectively, including prompt engineering and critical analysis of AI output. *Work Design: Redesign workflows to encourage new forms of human-AI collaboration, such as Centaur or Cyborg approaches. *Change Management: Manage the change process by communicating the benefits of AI, and addressing any concerns and anxieties from employees. *Performance Management: Create new metrics that measure the outcomes of human and AI collaboration. Definitely worth exploring if you are working on a Skills strategy or have a passion for #skillsbased, #skillspowered, #workforceplanning, #workforcestrategy, #talentstrategy, #talentmanagement, #hrtech, #taskintelligence, #futureofwork #ai, #automation Link to research is in the comments: