UX Metrics And KPIs

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

✅ 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 ↓]

How far are we from having competent AI co-workers that can perform tasks as varied as software development, project management, administration, and data science? In our new paper, we introduce TheAgentCompany, a benchmark for AI agents on consequential real-world tasks. Why is this benchmark important? Right now it is unclear how effective AI is at accelerating or automating real-world work. We hear statements like: > AI is overhyped, doesn’t reason, and doesn’t generalize to new tasks > AGI will automate all human work in the next few years This question has implications for: - Companies: to understand where to incorporate AI in workflows - Workers: to get a grounded sense of what AI can and cannot do - Policymakers: to understand effects of AI on the labor market How can we begin on it? In TheAgentCompany, we created a simulated software company with tasks inspired by real-world work. We created baseline agents, and evaluated their ability to solve these tasks. This benchmark is first of its kind with respect to versatility, practicality, and realism of tasks. TheAgentCompany features four internal web sites: - GitLab: for storing source code (like GitHub) - Plane: for doing task management (like Jira) - OwnCloud: for storing company docs (like Google Drive) - RocketChat: for chatting with co-workers (like Slack) Based on these sites, we created 175 tasks in the domains of: - Administration - Data science - Software development - Human resources - Project management - Finance We implemented a baseline agent that can web browse and write/execute code to solve these tasks. This was implemented using the open-source OpenHands framework for full reproducibility (https://lnkd.in/g4VhSi9a). Based on this agent, we evaluated many LMs, Claude, Gemini, GPT-4o, Nova, Llama, and Qwen. We evaluated both success metrics and cost. Results are striking: the most successful agent w/ Claude was able to successfully solve 24% of the diverse real-world tasks that it was tasked with. Gemini-2.0-flash is strong at a competitive price point, and the open llama-3.3-70b model is remarkably competent. This paints a nuanced picture of the role of current AI agents in task automation. - Yes, they are powerful, and can perform 24% tasks similar to those in real-world work - No, they can not yet solve all tasks or replace any jobs entirely Further, there are many caveats to our evaluation: - This is all on simulated data - We focused on concrete, easily evaluable tasks - We focused only on tasks from one corner of the digital economy If TheAgentCompany interests you, please: - Read the paper: https://lnkd.in/gyQE-xZG - Visit the site to see the leaderboard or run your own eval: https://lnkd.in/gtBcmq87 And huge thanks to Fangzheng (Frank) Xu, Yufan S., and Boxuan Li for leading the project, and the many many co-authors for their tireless efforts over many months to make this happen.

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

𝗠𝗖𝗣-𝗘𝗡𝗔𝗕𝗟𝗘𝗗 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧𝗦 𝗙𝗔𝗜𝗟 40-60% 𝗢𝗙 𝗧𝗛𝗘 𝗧𝗜𝗠𝗘 𝗢𝗡 𝗥𝗘𝗔𝗟-𝗪𝗢𝗥𝗟𝗗 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪𝗦: 𝗛𝗘𝗥𝗘'𝗦 𝗪𝗛𝗬 My daily work on LLM's workflow architectures (MCP-driven agent workflows) pushes me to the frontier of how 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 (𝗠𝗖𝗣𝘀) can be reliably exploited at scale. The 𝗟𝗶𝘃𝗲𝗠𝗖𝗣-101 study (arXiv:2508.15760) offers valuable insights into this challenge. 𝗕𝗘𝗡𝗖𝗛𝗠𝗔𝗥𝗞 - LiveMCP-101, a benchmark of 101 carefully curated real-world 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽 queries 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗮𝘀𝗸𝘀 (average 5.4 steps, up to 15) stress-test MCP-enabled agents across web, file, math, and data analysis domains. - 18 𝗺𝗼𝗱𝗲𝗹𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱: OpenAI, Anthropic, Google, Qwen3, Llama. 𝗞𝗘𝗬 𝗙𝗜𝗡𝗗𝗜𝗡𝗚𝗦 - 𝗚𝗣𝗧-5 𝗹𝗲𝗮𝗱𝘀 with 58.42% Task Success Rate, dropping to 39.02% on "Hard" tasks - 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗹𝗮𝗴𝘀 𝗯𝗲𝗵𝗶𝗻𝗱: Qwen3-235B at 22.77%, Llama-3.3-70B below 2% - 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗽𝗹𝗮𝘁𝗲𝗮𝘂: Closed models plateau after ~25 rounds; open models consume more tokens without proportional gains 𝗖𝗢𝗡𝗖𝗥𝗘𝗧𝗘 𝗧𝗔𝗦𝗞 𝗘𝗫𝗔𝗠𝗣𝗟𝗘𝗦 - 𝗘𝗮𝘀𝘆: Extract latest GitHub issues - 𝗠𝗲𝗱𝗶𝘂𝗺: Compute engagement rates on YouTube videos - 𝗛𝗮𝗿𝗱: Plan an NBA trip (team info, tickets, Airbnb constraints) with consolidated Markdown report 𝗙𝗔𝗜𝗟𝗨𝗥𝗘 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦 - 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝗿𝗿𝗼𝗿𝘀: Skipped requirements, wrong tool choice, unproductive loops - 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗲𝗿𝗿𝗼𝗿𝘀: Semantic (16.83% for GPT-5, up to 27.72% for other models) and syntactic (up to 48.51% for Llama-3.3-70B) - 𝗢𝘂𝘁𝗽𝘂𝘁 𝗲𝗿𝗿𝗼𝗿𝘀: Correct tool results misinterpreted 𝗧𝗔𝗞𝗘𝗔𝗪𝗔𝗬𝗦 𝗙𝗢𝗥 𝗠𝗖𝗣 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪 𝗗𝗘𝗦𝗜𝗚𝗡 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, is the main bottleneck. Reliability requires: • External planning • Tool selection, ranking and routing (RAG-MCP, ...) • Variable passing between MCP & memory (Variables Chaining) • Schema validation • Trajectory monitoring • Efficiency policies, Budget-aware execution 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: The path forward isn't adding more tools, but engineering robust orchestration layers that make MCP chains dependable. What's your experience with AI agent workflows at scale? Have you experienced similar failure patterns? Many of these orchestration issues are ones I’ve needed to tackle in practice — always happy to compare notes with others working on advanced solutions. Link to the paper: https://lnkd.in/g8bbNK6E #AI #MachineLearning #Workflows #MCP #AIAgents #Productivity #Innovation   

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

Traditional usability tests often treat user experience factors in isolation, as if different factors like usability, trust, and satisfaction are independent of each other. But in reality, they are deeply interconnected. By analyzing each factor separately, we miss the big picture - how these elements interact and shape user behavior. This is where Structural Equation Modeling (SEM) can be incredibly helpful. Instead of looking at single data points, SEM maps out the relationships between key UX variables, showing how they influence each other. It helps UX teams move beyond surface-level insights and truly understand what drives engagement. For example, usability might directly impact trust, which in turn boosts satisfaction and leads to higher engagement. Traditional methods might capture these factors separately, but SEM reveals the full story by quantifying their connections. SEM also enhances predictive modeling. By integrating techniques like Artificial Neural Networks (ANN), it helps forecast how users will react to design changes before they are implemented. Instead of relying on intuition, teams can test different scenarios and choose the most effective approach. Another advantage is mediation and moderation analysis. UX researchers often know that certain factors influence engagement, but SEM explains how and why. Does trust increase retention, or is it satisfaction that plays the bigger role? These insights help prioritize what really matters. Finally, SEM combined with Necessary Condition Analysis (NCA) identifies UX elements that are absolutely essential for engagement. This ensures that teams focus resources on factors that truly move the needle rather than making small, isolated tweaks with minimal impact.

💡How UX maturity level of organization impacts design outcomes UX maturity refers to how well an organization understands, values, and integrates human-centered design practices into its culture, processes, and decision-making. The level of UX maturity in an organization has a direct impact on the quality of design outcomes. The higher the level of UX maturity, the more likely an organization is to deliver designs that are both user-centered and aligned with business goals. NNGroup identifies the following six levels of UX maturity (https://lnkd.in/dxut4sMD) 1️⃣ Absent Design decisions are often made based on assumptions, stakeholder opinions, or short-term goals. Key attributes: ✔ UX is ignored or nonexistent ✔ No user-centered thinking or planned UX efforts ✔ Lack of awareness about UX benefits and processes 2️⃣ Limited Some awareness of UX exists, but it is not deeply integrated into processes. Key attributes: ✔ Sporadic, siloed UX efforts driven by legal needs or individual initiatives ✔ No systematic processes, roles, or budgets for UX 3️⃣ Emergent Business understands the importance of UX and introduces some design practices, but does it in an inconsistent manner Key attributes: ✔ Growing UX awareness; some teams engage in UX planning ✔ UX roles exist but are insufficient; efforts remain inconsistent ✔ Value of UX still needs to be proven 4️⃣ Structured UX is well-embedded into the organization's strategy and decision-making, but there is room for optimization Key attributes: ✔ Dedicated UX teams with leadership support ✔ Systematic processes exist but face resource allocation and strategy challenges ✔ UX is widespread but not fully optimized 5️⃣ Integrated Deep integration of UX with business strategy, driven by data and user insights. Key attributes: ✔ UX is pervasive, efficient, and tied to business goals ✔ Innovation in methods; UX-focused success metrics in place ✔ Process-focused, with potential gaps in user-centered outcome metrics 6️⃣ User-driven UX is at the core of the organization's culture, influencing every decision. Key attributes: ✔ UX is habitual and central to strategy and culture. ✔ Organization prioritizes user needs and contributes to UX industry standards. Based on my experience, most organizations tend to get stuck at stages 3 (Emergent) or 4 (Structured) in UX maturity. Advancing beyond these stages often demands: ✔ Strong leadership buy-in and UX prioritization at the strategic level. ✔ Alignment of UX goals with key business metrics. ✔ Investment in scaling UX operations across the organization. Without these efforts, organizations settle for being "just good enough" instead of striving for higher maturity levels. 🖼️ Stages of UX Maturity by Nielsen Norman Group #UX #UI #design #productdesign #design

I am thrilled to share the most comprehensive and impactful research Project Management Institute has ever conducted on one of the profession’s most critical topics: #projectsuccess. This monumental study redefines how we understand and achieve success in the projects that shape our world. We began with an extensive review of 50 years of seminal literature, laying a foundation of knowledge and insights. Building on this, we conducted 90 in-depth interviews with a diverse range of voices: project professionals, sponsors, PMO leaders, executives, and intended beneficiaries. These conversations informed a robust global survey, engaging 9,500 project professionals, stakeholders, and beneficiaries across industries, who evaluated their recently completed projects. Our rigorous analysis and statistical modeling culminated in a groundbreaking new approach for understanding project success. This approach was further enriched through collaboration with a team of subject matter experts and 50+ interviews with #PMO leaders and community members, ensuring its relevance and applicability. This landmark report sets a new standard for what it means to deliver a successful project, offering transformative insights and actionable guidance for the profession. Here’s what you’ll discover: - > A Holistic Definition of Success: Establishes a shared perspective that aligns the priorities of diverse stakeholders, from practitioners to beneficiaries. - > A Universal Measurement Framework: Introduces a clear and consistent method for evaluating project success across industries and geographies. - > Key Success Drivers: Identifies and explains the factors that influence project outcomes, empowering practitioners and organizations to consistently deliver greater value. - > Global and Industry Insights: Provides a detailed measurement of project success rates worldwide, segmented by industry and project type, offering invaluable benchmarking data. - > Purpose-Driven Benefits: Highlights the profound impact of aligning projects with a higher purpose to achieve not just success, but significance. - > Practical Activation of Insights: Equips practitioners, executives, and the broader project management community with tools to activate success in real-world scenarios. - > A Vision for the Future: Guides the profession and its stakeholders toward outcomes that maximize success and elevate our world. Read the full report: https://lnkd.in/dv-387F7 Project Management Institute #thoughtleadership #projectsuccess #projectmanagementtoday

A designer's survival guide to proving impact... Every design decision we make has ripple effects, but if we can't communicate that impact, we're leaving career opportunities on the table. Reality check! 💥 Most of us struggle to get any business metrics. We can't prove our design changed anything. Frustrating? Absolutely. Career-limiting? Not if you know how to pivot! Let's do a mindset shift: The impact isn't just about metrics. It comes in many forms. (𝘐 𝘬𝘯𝘰𝘸 𝘴𝘰𝘮𝘦 𝘰𝘧 𝘵𝘩𝘦𝘮 𝘤𝘢𝘯 𝘴𝘵𝘪𝘭𝘭 𝘣𝘦 𝘩𝘢𝘳𝘥 𝘵𝘰 𝘨𝘦𝘵, 𝘣𝘶𝘵 𝘪𝘵 𝘮𝘪𝘨𝘩𝘵 𝘣𝘦 𝘦𝘢𝘴𝘪𝘦𝘳 𝘵𝘩𝘢𝘯 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯 𝘰𝘳 𝘳𝘦𝘷𝘦𝘯𝘶𝘦) → User-centric indicators • Reduction in user errors • Time saved per user flow • Decreased learning curve • User satisfaction scores from testing → Client relationship wins • Positive feedback in client meetings • Extended contracts/repeat business • Client referrals • Stakeholder testimonials • Increased trust (shown through autonomous decision-making) → Team efficiency gains • Faster design iteration cycles • Reduced revision rounds • Improved developer handoff efficiency • Better cross-functional collaboration • Streamlined documentation process → Brand & market impact • Positive social media mentions • Industry recognition • Design awards • Competitor analysis advantages • Brand consistency improvements Impact isn't just about numbers - it's about telling a compelling story of transformation through design. Start collecting "micro-wins" in every project. The client team's excitement, developer feedback, user testing insights. These stories became more powerful than any conversion rate could be. Remember: Lack of metrics isn't a roadblock. It's an invitation to tell a richer story! P.S. How do you showcase impact without direct access to metrics? Share your strategies below!