UX Design For Cloud-Based Solutions

🔬 How To Run UX Research In B2B and Enterprise. Practical techniques of what you can do in strict environments, often without access to users. 🚫 Things you typically can’t do 1. Stakeholder interviews ← unavailable 2. Competitor analysis ← not public 3. Data analysis ← no data collected yet 4. Usability sessions ← no users yet 5. Recruit users for testing ← expensive 6. Interview potential users ← IP concerns 7. Concept testing, prototypes ← NDA 8. Usability testing ← IP concerns 9. Sentiment analysis ← no media presence 10. Surveys ← no users to send to 11. Get support logs ← no security clearance 12. Study help desk tickets ← no clearance 13. Use research tools ← no procurement yet ✅ Things you typically can do 1. Focus on requirements + task analysis 2. Study existing workflows, processes 3. Study job postings to map roles/tasks 4. Scrap frequent pain points, challenges 5. Use Google Trends for related search queries 6. Scrap insights to build a service blueprint 7. Find and study people with similar tasks 8. Shadow people performing similar tasks 9. Interview colleagues closest to business 10. Test with customer success, domain experts 11. Build an internal UX testing lab 12. Build trust and confidence first In B2B, people buying a product are not always the same people who will use it. As B2B designers, we have to design at least 2 different types of experiences: the customer’s UX (of the supplier) and employee’s UX (of end users of the product). In customer’s UX, we typically work within a highly specialized domain, along with legacy-ridden systems and strict compliance and security regulations. You might not speak with the stakeholder, but rather company representatives — who regulate the flow of data they share to manage confidentiality, IP and risk. In employee’s UX, it doesn’t look much brighter. We can rarely speak with users, and if we do, often there is only a handful of them. Due to security clearance limitations, we don’t get access to help desk tickers or support logs — and there are rarely any similar public products we could study. As H Locke rightfully noted, if we shed the light strongly enough from many sources, we might end up getting a glimpse of the truth. Scout everything to see what you can find. Find people who are the closest to your customers and to your users. Map the domain and workflows in service blueprints and . Most importantly: start small and build a strong relationship first. In B2B and Enterprise, most actors are incredibly protective and cautious, often carefully manoeuvring compliance regulations and layers of internal politics. No stones will be moved unless there is a strong mutual trust from both sides. It can be frustrating, but also remarkably impactful. B2B relationships are often long-term relationships for years to come, allowing you to make huge impact for people who can’t choose what they use and desperately need your help to do their work better. [continues in comments ↓] #ux #b2b

Do you sometimes feel frustration, as you are building a product to get the management off your back, rather than address the users? Here are 6 ways to become user-centric again: 1) Prioritize in a transparent way This is a great place to start. If your backlog is prioritized based on data and potential opportunity, risk, and cost, it will be easier to put forth user-centric initiatives ahead of those that came from upstairs. At the very least, you will have a good basis for an educated discussion. 2) Utilize users' perspective using user stories and personas If your team understands the users and their problems, it will be easier to craft something great that will later appeal to the same users. Just keep up the empathy of creating something by people for other people, and not get some metric magically go up! 3) Make user feedback public If everyone in the company can see the themes that come from user feedback, it will be way harder to ignore it in favor of some corporate nonsense. Let those voices be heard by everyone! 4) Have the NPS and user ratings at the forefront The same goes for a single metric representing the general product sentiment. If the number is low or, worse, is going down and everyone can see that, the responsible Product Manager has to react. 5) Focus on your product goals Now, upstairs mandates might not be the only distraction you face when trying to improve your product. To survive them all, focus on one thing: your product goals. This will allow you to demonstrate you are doing what you are asked for and you can use user feedback and points 1-4 to pursue those goals. Thus, it's like killing 2 birds with 1 stone. However, you can also simply: 6) Have the confidence to say "No" Not all company/legal/management requests can be ignored. Sometimes changing the law or a wider company initiative will require you to comply and that is OK! However, there will also be times when someone will try to force your compliance. This is where you need to be confident, and exercise your Product Manager's independence, especially when there is no data to support a specific request. There you go! My 6 ways you can become a user-centric Product Manager. How about you? Do you address your users or your management first and foremost when developing your product? Sound off in the comments! #productmanagement #productmanager #usercentricity

AI Prototyping 101: If I had to teach someone how to actually build usable products with AI, this is where I’d start. Here's the step-by-step workflow that feels like magic: — ONE - THE UNIVERSAL AI PROTOTYPING WORKFLOW No matter which tool you’re using — v0, Bolt, Replit, or Lovable — this is the backbone of a solid AI build process: 1. Start with Context AI works way better when it knows what you're working with. Figma files are ideal, they give structure and design language. If you don’t have those, use screenshots of your product. Worst case? A hand-drawn wireframe is still better than nothing. Without visual context, AI makes blind guesses. And you’ll spend more time correcting its “creativity” than building useful stuff. 2. Write a PRD (Yes, Even for AI) A simple .md file with a few bullet points on what you’re building goes a long way. Include: - What the customers want - What the feature does - Key user flows - Must-have functionality You can even ask Claude or GPT to write the first draft. But the better your input, the stronger your first output. 3. Get to Building Now open up your tool of choice. Start with a big-picture command. Then zoom in. Don’t say “Build me a dashboard.” Say: “Build a dashboard with 3 sections: recent activity, user goals, and notifications. Each should have X, Y, and Z.” Also, AI can handle technical stuff. So don’t hold back. Use real terms: auth flow, API call, state logic, it gets it. 4. Iterate Like a Builder, Not a Perfectionist Make one change at a time. Test it fast. Roll it back if it doesn’t work. This isn’t “prompt once and ship.” This is real prototyping. AI is just helping you move 100x faster. — TWO - TOOL-BY-TOOL BREAKDOWN (Complete walkthrough of the tools with screenshots, real examples, and tool setups is linked at the end.) So, let’s talk interfaces here. Here’s what each platform does best: 1. v0 - Figma import is seamless - Template gallery = instant jumpstart - Chat interface bottom left, live preview on right - Exports clean code and deploys fast 2. Bolt - Same vibe as v0, but more technical - Built-in Supabase integration with a terminal access - Deploys to Netlify in one click 3. Replit - This one feels like a real IDE - You get an “AI agent” to plan everything - Built-in chat, live console, multiplayer mode - Ships to a live URL, complete with CDN 4. Lovable - The most design-friendly of the bunch - Visual editing > code editing - Figma support, Supabase, live preview, it’s all there - Great for teams who want to stay out of code — I broke it all down - with screenshots, working examples, and use cases - in this full walkthrough: https://lnkd.in/eJujDhBV — All of these tools are powerful. But none of them matter if you don’t understand the workflow behind how to use them. Once you’ve got that down, you can ship real products in hours, not weeks.

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

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 👇 #LLM #AI #MachineLearning

"The current approach to the employee experience simply doesn’t work." Employee experience often gets treated as a comp exercise, fixes to onboarding, maybe a revised perf process -- often built "one size fits all." Companies invest heavily in customer experience. Research, data science, human-centered design and product management creates massive results through personalization, loyalty and deep understanding of emotional drivers. What if we applied a lot of the same tools to employee experience? What if we thought of employee experience through the lens of product management? Great #EX boosts retention and drives great #CX. Visibly in retail: Trader Joe's and Costco have excellent EX, strong financials and very loyal customers. This movement is growing. It will get larger because the demographics of labor are changing: slow growth, more diversity, tradeoffs well beyond comp and benes. Here's some faves, 🔗 in comments: follow them all if you don't already! ⭐ Samantha Gadd and Kalyn (KP) Ponti have been leading with their EX Manifesto and focus on human-centered design. How to work directly with employees to understand needs, and build together. Which leads to... ⭐ The top quote from "Reimagining Work as a #Product" by Dart Lindsley & Eric Anicich brings product design thinking to life with stories from Eli Lilly, Shopify and Dropbox where the Melanie / Alastair / Allison trifecta are re-imagining #EX. They share a framework that breaks down a job into tasks, understanding what's rewarding vs drudgery. Work that ... ⭐ Debbie Lovich & Rosie Sargeant quantified: increasing joy boosts retention by almost 2X, and that you can't make assumptions about what brings someone joy: a break where you write notes might be joyful. Debbie and I just talked about how Clay Christensen's "Jobs to Be Done" (JTBD) framework applies to employees. Why the job someone takes often has less to do with comp and title than a broader set of tradeoffs. "JTBD" shows in Dart & Eric's work, and is central to... ⭐  Ethan Bernstein, Michael Horn & Bob Moesta's "What Companies Get Wrong About Employee Experience" which breaks down four typical quests of people taking on a new role -- what are they "hiring" that role to do? Understanding the emotional context of leaving or taking a new job and the desires and tradeoffs of individuals boosts outcomes -- especially retention. Emotional context also shows up in #AI adoption... ⭐ Christina Janzer & Lucas Puente's work on AI personas, which I recently shared -- how might those personas overlap with someone's "JTBD" of their role? ⭐ Rodney Evans knows this is a major transformation, and one group who needs help changing how they work is #HR. Rodney and team do work with people who want to do the work... Which leads to one last quote: ⭐ "Let the work do the work" Iain Roberts, taking a design-centered approach to improving EX by working on it directly with the team. Who am I missing? #FutureOfWork #retention

Salesforce was losing 8% users every month back in 2005. They were growing fast, but bleeding users faster. Their solution? They completely overhauled their onboarding process. The result? They doubled their user base in just one year. This wasn't luck. It was strategy. Here's the thing about customer onboarding that most SaaS companies miss: A customer lost during onboarding is often lost for life. Yet only 37% of users ever reach an "aha moment" in self-serve onboarding without help. That's a massive growth opportunity hiding in plain sight. Four critical insights to fix your onboarding processes. 1 - Segmentation isn't optional, it's crucial SMBs need automation and quick wins. Mid-market requires hybrid approaches. Enterprise demands white-glove service. One size fits none. 2 - Time to Value is everything I watched one tech company slash implementation from 6+ months to 30 days by streamlining onboarding. Another accelerated TTV by 20% and saw corresponding gains in retention. Speed to value = Speed to growth. 3 - What gets measured gets improved Track completion rates (benchmark: 40-60% for B2B SaaS), product adoption, and 30/60/90-day retention. These metrics don't lie about onboarding ROI. 4 - Personalization drives results, even at scale HubSpot's approach of customizing dashboards based on initial surveys significantly improved engagement and conversion. Users don't want an onboarding experience. They want THEIR onboarding experience. The companies winning at this aren't treating onboarding as a checkbox. They see it as a proactive, evolving program that combines technology with human touchpoints. In today's hyper-competitive SaaS landscape, stellar onboarding isn't a nice-to-have—it's essential for sustainable growth. What's your company's approach to customer onboarding? Are you treating it as a strategic growth lever or just another task to complete? Let me know in the comments 👇 __ ♻️ Reshare this post if it can help others! __ ▶️ Want to see more content like this? You should join 2238+ members in the Tidbits WhatsApp Community! 💥 [link in the comments section]

🔎 UX Metrics: How to Measure and Optimize User Experience? When we talk about UX, we know that good decisions must be data-driven. But how can we measure something as subjective as user experience? 🤔 Here are some of the key UX metrics that help turn perceptions into actionable insights: 📌 Experience Metrics: Evaluate user satisfaction and perception. Examples: ✅ NPS (Net Promoter Score) – Measures user loyalty to the brand. ✅ CSAT (Customer Satisfaction Score) – Captures user satisfaction at key moments. ✅ CES (Customer Effort Score) – Assesses the effort needed to complete an action. 📌 Behavioral Metrics: Analyze how users interact with the product. Examples: 📊 Conversion Rate – How many users complete the desired action? 📊 Drop-off Rate – At what stage do users give up? 📊 Average Task Time – How long does it take to complete an action? 📌 Adoption and Retention Metrics: Show engagement over time. Examples: 📈 Active Users – How many people use the product regularly? 📈 Churn Rate – How many users stop using the service? 📈 Cohort Retention – What percentage of users remain engaged after a certain period? UX metrics are more than just numbers – they tell the story of how users experience a product. With them, we can identify problems, test hypotheses, and create better experiences! 💡🚀 📢 What UX metrics do you use in your daily work? Let’s exchange ideas in the comments! 👇 #UX #UserExperience #UXMetrics #Design #Research #Product

Streamline Your New Hire Journey with SAP SuccessFactors Onboarding SAP SuccessFactors Onboarding is more than just an orientation tool—it's a pivotal solution that connects seamlessly with other modules to ensure new hires feel supported from day one. Here's how it integrates with key modules: 👉 Recruiting: Automatically transition candidates into the onboarding process directly from their application. 👉 Employee Central: Facilitate smooth conversion of candidates into employees, whether or not they're sourced via Recruiting. 👉 Learning: Assign courses to new hires even before their first day, ensuring they hit the ground running. 👉 Performance & Goals: Empower employees by setting goals as part of their onboarding journey with templates for New Hire Goal Management. 👉 DocuSign: Enable digital signatures for forms, ensuring compliance and ease across devices. 👉 Qualtrics Employee Lifecycle: Collect actionable feedback through automated surveys triggered upon program completion. Opportunities for Enhanced Integrations: There are potential areas to amplify the onboarding experience: 👉 ITSM tools like ServiceNow: Automate provisioning of equipment and systems access for new employees. What integrations do you think are essential for a next-gen onboarding process? Share your thoughts below! 👇 #SAPSuccessFactors #Onboarding #HRTech #EmployeeExperience #Integration

Brands used to broadcast. Now they respond. ✅ Think of a B2B SaaS platform where every interaction flexes to the person in front of it. A procurement officer logs in and the dashboard emphasizes compliance, audit trails, and control. A developer logs in and the experience surfaces APIs, sandbox access, and speed. A CFO sees ROI models, forecasts, and financial clarity. Same product. Same brand. Different resonance. This is the rise of responsive brand experience. Not a gimmick, but a strategy: making every layer of identity—UI, UX, content, and even tone of voice—adaptive, intelligent, contextual.❤️ The contrast is striking. Legacy enterprises still design for the average user. They ship one interface, one story, one pathway. Digital-first players design for each user, building systems that adjust like living organisms—changing not only logos, but dashboards, help content, and even microcopy to meet the user where they are. There’s philosophy behind it. Customers don’t just want “software that works.” They want “software that gets them.” Adaptive design—whether in visual identity, navigation, or communication—signals empathy. It says: we see you, we know what matters to you, and we’ll clear the clutter so you can move faster. But the danger is real. Adapt too much and you lose coherence. A CFO may welcome tailored insights but won’t trust a brand whose tone, design, or values feel inconsistent. Responsiveness must orbit around a strong, immutable core: trust, reliability, transparency. What shifts is the expression; what stays firm is the essence. So, the real question for technology brands is not can you adapt? It’s why and how much?💯 The opportunity is profound. Responsiveness is not decoration. Not novelty. It’s a signal of intelligence. The same principle behind great products—turning complexity into clarity—should govern the brand experience itself. When UI, UX, and content stop shouting and start listening, the brand doesn’t just “look” intelligent. It feels intelligent. That’s when technology stops being a tool and starts being a partner. #futureofmarketing #thoughtleadership #thethoughtleaderway