User Experience in Cross-Platform Applications

𝐎𝐧𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐔𝐒 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬: Convenience sounds like a win… But in reality—control builds the trust that scales. 𝐋𝐞𝐭 𝐦𝐞 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 👇 We were working on improving product adoption for a US-based platform. Most founders would instinctively look at cutting down clicks and removing steps in the onboarding journey. Faster = Better, right? That’s what we thought too—until real usage patterns showed us something very different. Instead of shortening the journey, we tried something counterintuitive: -We added more decision points -Let the user customize their flow -Gave options to manually choose settings instead of setting defaults And guess what? Conversion rates went up. Engagement improved. And most importantly—user trust deepened. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐫𝐞𝐚𝐥𝐢𝐬𝐞𝐝: You can design a sleek 2-click journey…  …but if the user doesn’t feel in control, they hesitate. Especially in the US market, where data privacy and digital autonomy are hot-button issues—transparency and control win. 𝐒𝐨𝐦𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐬𝐭𝐨𝐨𝐝 𝐨𝐮𝐭 𝐭𝐨 𝐦𝐞: → People often disable auto-fill just to manually type things in.  → They skip quick recommendations to do their own comparisons.  → Features that auto-execute without explicit confirmation? Often uninstalled. 💡 Why? It’s not inefficiency. It’s digital self-preservation. It’s a mindset of: “Don’t decide for me. Let me drive.” And I’ve seen this mistake firsthand: One client rolled out a smart automation feature that quietly activated behind the scenes. Instead of delighting users, it alienated 15–20% of their base. Because the perception was: "You took control without asking." On the other hand, platforms that use clear confirmation prompts (“Are you sure?”, “Review before submitting”, toggles, etc.)—those build long-term trust. That’s the real game. Here’s what I now recommend to every tech founder building for the US market: -Don’t just optimize for frictionless onboarding. -Optimize for visible control. -Add micro-trust signals like “No hidden fees,” “You can edit this later,” and clear toggles. -Let the user feel in charge at every key point. Because trust isn’t built by speed. It’s built by respecting the user’s right to decide. If you’re a tech founder or product owner: Stop assuming speed is everything. Start building systems that say, “You’re in control.” That’s what creates adoption that sticks. What’s your experience with this? Would love to hear in the comments. 👇 #ProductDesign #UserExperience #TrustByDesign #TechForUSMarket #DigitalAutonomy #businesscoach #coachishleenkaur Linkedin News LinkedIn News India LinkedIN for small businesses

The hardest part of building Agentic SaaS isn’t the model, the agent, or the workflow. It’s trust! LLMs and agent frameworks will commoditize. Workflow design will stop being the differentiator. What will separate the Agentic SaaS winners from the rest? Trust. Adoption won’t come from capability. It will come from belief that the software will act - consistently (e.g., a reconciliation agent that always balances invoices correctly), invisibly (e.g., a support agent that escalates only when necessary) and ethically (e.g., a recruiting agent that never screens out candidates based on gender or ethnicity). Think of it like autopilot in aviation. Pilots don’t trust it because it’s a clever software - they trust it because it’s reliable, bounded, and accountable. That’s the bar for Agentic SaaS! For Agentic SaaS leaders, designing software that inspires confidence at every step means focusing on three pillars of trust: 1\ Accountability before action: Users don’t need agents to be always right. They need to know that when in doubt, the agent will surface decisions for review instead of bluffing through. How: Flag uncertainty, escalate edge cases for human review, and leave an auditable trail. 2\ Transparency after action: Trust comes from knowing you could intervene if needed. How: Make actions reviewable, explainable, and interruptible. Offer clear “undo/override” options and maintain an audit log. 3\ Set clear boundaries: Trust is earned more by what an agent refuses to do than by what it can do. How: Show upfront what data the agent can access, block sensitive information by default, and explicitly define actions the agent will never take (e.g., sending messages or making changes without approval). Designing for trust isn’t easy, and even the best teams are still figuring it out. But it’s worth prioritizing. Models will get cheaper and workflows will get replicated, but the strategic moat for breakout Agentic SaaS companies will be trust! Features can be copied, but trust can’t. Disclaimer: Views personal.

We’re leaving “assistants in apps” behind and entering the era of autonomous systems that perceive, reason, act, and learn. The research from University of Oxford is clear: UX must shift from guiding users through procedures to systems that take a goal, execute safely, and return a verified result with an audit trail. Agents can plan, coordinate tools/APIs, adapt from feedback, and carry memory forward. Our job moves from arranging screens to specifying goals, guardrails, and governance. ↳What to design next? ➤ Delegation over steps: Users set objectives and constraints; agents handle multi-step execution. ➤ Receipts for autonomy: Preview the plan, explain actions, expose confidence/provenance. ➤ Reversibility: Approve/modify before execution; one-tap rollback after. ➤ Safety as telemetry: Adversarial tests, shadow runs, and thresholds treated like uptime SLOs. ➤ Inspectable memory: Show what was learned; let users review, edit, or forget it. ↳Forward-thinking questions: ➤ Where can users delegate an outcome instead of a procedure? ➤ How do we surface confidence fast enough for oversight? ➤ What’s the rollback path for every action? ➤ How is memory exposed and controlled? Design the contract well, and autonomy becomes usable, trustworthy, and shippable. Trust is currency. 💰 ---- Inspired by: Michael Negnevitsky (author)

Trust in technology is not about making systems look friendly or adding more explanations. It is about how people decide to rely on something when there is uncertainty. In human computer interaction, trust is a judgment users make. It is shaped by expectations, experience, social cues, perceived control, and context. The same system can be trusted in one situation and distrusted in another. That is why trust is so hard to design and so easy to break. Research shows that users do not trust systems for a single reason. Sometimes trust comes from reasoning. Does this system behave consistently? Does it do what I expect? Other times trust comes from feeling. Does this interface feel human, present, or socially responsive? In many cases trust is social. If people I trust rely on this system, I am more likely to trust it too. There are also moments where trust collapses. When users feel forced, manipulated, or stripped of control, distrust appears even if the system is accurate. When early experiences violate expectations, trust erodes fast and rarely recovers on its own. One of the most important insights is that trust is dynamic. It builds slowly through repeated positive interactions and can disappear quickly after a single negative one. Designing for trust is not about maximizing trust. It is about supporting appropriate trust. Helping users know when to rely on a system and when not to. For AI, automation, and complex digital products, this matters more than ever. Overtrust is just as dangerous as distrust. Good design respects user agency, supports understanding, and stays honest about limitations. Trust is not a feature you add at the end. It is an outcome of how the entire system behaves over time.

Hi Everyone 😁, As a UX designer, I'm always exploring concepts from various fields that can enrich our understanding of user experience. Recently, I came across the "Lemon Game" from economics, and it struck me how relevant it is to UX design. 🍋 📖 The Story: The "Lemon Game," introduced by economist George Akerlof, explains how quality uncertainty in a market can lead to adverse selection, where bad quality products ("lemons") drive out good quality products. This happens because buyers cannot accurately assess product quality before purchase, leading to a breakdown in trust and market efficiency. In UX design, this concept is incredibly relevant. When users encounter poorly designed interfaces or unreliable products, their trust in the brand diminishes, leading to a preference for competitors. Let's delve into the UX lessons from the Lemon Game. ⚠️ Potential Misuse by Companies: While the Lemon Game can build trust and improve UX, some companies might misuse it to deceive users. For example: ❌ Fake Reviews: Creating fake positive reviews to mislead users into purchasing low-quality products. ❌ Hidden Information: Obscuring important #product information or terms of service, leading to #user dissatisfaction. ❌ Manipulative Design: Using dark patterns to trick users into making unfavorable decisions. 🧠 Key UX Lessons: ✅ Build Trust: Ensure that users have confidence in the quality and reliability of your product. Transparent communication and consistent performance are key. ✅ Minimize Uncertainty: Provide clear information, #intuitive #design, and positive user feedback to reduce uncertainty and enhance user experience. ✅ Continuous Improvement: Regularly update and improve your product based on user feedback to maintain high standards and user satisfaction. 🔍 The Application: In the #digital world, trust is paramount. #Users abandon products if they encounter issues or inconsistencies. By understanding the Lemon Game, #UXdesigners can prioritize building trust and minimizing uncertainty. 📱 Examples: 👉 E-commerce Platforms: Trust & Reviews: Amazon uses reviews to build trust, highlighting high-quality products and flagging "lemons." 👉 Subscription Services: Free Trials & Money-Back Guarantees: Spotify and Netflix offer free trials to reduce uncertainty, building trust and confidence. 👉 #Mobile Apps: #App Store Ratings: Google Play and Apple App Store use ratings to identify high-quality apps and avoid "lemons." 👉 Online Marketplaces: Verified Sellers: eBay and Etsy verify sellers, reducing uncertainty and encouraging positive transactions. This concept resonates with me because it highlights the importance of #trust and quality in #UX . Our goal is to ensure our products stand out for reliability and excellence. We must also address unethical practices to protect users. Have you encountered instances where quality uncertainty affected UX? . . . #productthinking #designthinking #behaviourdesign #design #usercentereddesign #uiux

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

Designing UX for autonomous multi-agent systems is a whole new game. These agents take initiative, make decisions, and collaborate, the old click and respond model no longer works. Users need control without micromanagement, clarity without overload, and trust in what’s happening behind the scenes. That’s why trust, transparency, and human-first design aren’t optional — they’re foundational. 1. Capability Discovery One of the first barriers to adoption is uncertainty. Users often don't know what an agent can do, especially when multiple agents collaborate across domains. Interfaces must provide dynamic affordances, contextual tooltips, and scenario-based walkthroughs that answer: “What can this agent do for me right now?” This ensures users onboard with confidence, reducing trial-and-error learning and surfacing hidden agent potential early. 2. Observability and Provenance In systems where agents learn, evolve, and interact autonomously, users must be able to trace not just what happened, but why. Observability goes beyond logs; it includes time-stamped decision trails, causal chains, and visualization of agent communication. Provenance gives users the power to challenge decisions, audit behaviors, and even retrain agents, which is critical in high-stakes domains like finance, healthcare, or DevOps. 3. Interruptibility Autonomy must not translate to irreversibility. Users should be able to pause, resume, or cancel agent actions with clear consequences. This empowers human oversight in dynamic contexts (e.g., pausing RCA during live production incidents), and reduces fear around automation. Temporal control over agent execution makes the system feel safe, adaptable, and co-operative. 4. Cost-Aware Delegation Many agent actions incur downstream costs, infrastructure, computation, or time. Interfaces must make the invisible cost visible before action. For example, spawning an AI model or triggering auto-remediation should expose an estimated impact window. Letting users define policies (e.g., “Only auto-remediate when risk score < 30 and impact < $100”) enables fine-grained trust calibration. 5. Persona-Aligned Feedback Loops Each user persona, from QA engineer to SRE will interact with agents differently. The system must offer feedback loops tailored to that persona’s context. For example, a test generator agent may ask a QA to verify coverage gaps, while an anomaly agent may provide confidence ranges and time-series correlations for SREs. This ensures the system evolves in alignment with real user goals, not just data. In multi-agent systems, agency without alignment is chaos. These principles help build systems that are not only intelligent but intelligible, reliable, and human-centered.

Building Trust in Agentic Experiences Years ago, one of my first automation projects was in a bank. We built a system to automate a back-office workflow. It worked flawlessly, and the MVP was a success on paper. But adoption was low. The back office team didn’t trust it. They kept asking for a notification to confirm when the job was done. The system already sent alerts when it failed as silence meant success. But no matter how clearly we explained that logic, users still wanted reassurance. Eventually, we built the confirmation notification anyway. That experience taught me something I keep coming back to: trust in automation isn’t about accuracy in getting the job done. Fast forward to today, as we build agentic systems that can reason, decide, and act with less predictability. The same challenge remains, just on a new scale. When users can’t see how an agent reached its conclusion or don’t know how to validate its work, the gap isn’t technical; it’s emotional. So, while Evaluation frameworks are key in ensuring the quality of agent work but they are not sufficient in earning users trust. From experimenting with various agentic products and my personal experience in building agents, I’ve noticed a few design patterns that help close that gap: Show your work: Let users see what’s happening behind the scenes. Transparency creates confidence. Search agents have been pioneer in this pattern. Ask for confirmation wisely: autonomous agents feel more reliable when they pause at key points for user confirmation. Claude Code does it well. Allow undo: people need a way to reverse mistakes. I have not seen any app that does it well. For example all coding agents offer Undo, but sometimes they mess up the code, specially for novice users like me. Set guardrails: Let users define what the agent can and can’t do. Customer Service agents do it great by enabling users to define operational playbooks for the agent. I can see “agent playbook writing” becoming a critical operational skill. In the end, it’s the same story I lived years ago in that bank: even when the system works perfectly, people still want to see it, feel it, and trust it. That small "job completed" notification we built back then was not just another feature. It was a lesson learned in how to build trust in automation.

Would you trust a real estate agent who gets kickbacks for every house they recommend? Then why design products that prioritize business goals over user needs? There must be balance. My SIMPLE 6-step framework for being a UX fiduciary: 1. Shield Your Users – Take Responsibility for Their Experience ↳ When you're building interfaces that dance between business goals and user needs, always lead with empathy. ↳ Even when I'm fatigued from remote work Zoom calls, I remember that users feel the same exhaustion. 2. Integrity in Design Decisions – Stand Firm on Ethical Principles ↳ Like I track my workouts strength training, track the ethical impact of every design decision. ↳ The easiest path is rarely the most responsible one. 3. Make Complexity Invisible – Do the Hard Work to Make Things Simple ↳ As a techie who builds AI tool stacks, complexity is inevitable. ↳ But your users shouldn't have to understand the system to use it effectively. 4. Privacy as Default – Protect What Matters Most ↳ Guard user data like it's yours, because someday it might be. ↳ Every piece of data collected should directly benefit the user first. 5. Listen Before Designing – Understand True User Needs ↳ Getting away from screens weekly reminds me that digital experiences should serve human needs. ↳ The best solutions come from observing behavior, not from confirming biases. 6. Educate Your Team – Be the Ethics Advocate ↳ Share your knowledge generously but stand firm on non-negotiable user protections. ↳ Test new tools and approaches, but never at the user's expense. Being a UX fiduciary means putting users' interests first—even when it means pushing back against business pressures. It's about creating trust through integrity, not conversion through manipulation. --- PS: If your design decisions were regulated like financial advice, would you still make the same choices? Follow me, John Balboa. I swear I'm friendly and I won't detach your components.

Innovation means existing technology necessarily changes over time, which results in design changes. Some of the population can easily handle technological change - most of the time, us geeks can readily adapt. But for a large portion of the population, design changes can be inconvenient, break existing workflows and mental models, and otherwise disrupt people's lives. When technology inevitably changes, we can help build user trust by helping them through the process: 1) Set expectations/WIIFM: tell users well in advance what will be changing, why it is happening, and how they may benefit 2) Provide a clear overview of changes: a well-designed information visualization that shows design changes can help draw users into the documentation; avoid large blocks of text that convey changes 3) Provide a repeatable walk-through: in the new design, show users primary interactions that have moved; allow them to repeat the walk-through 4) Allow a delay: software often updates at critical times for the user (giving a presentation, teaching a class, etc.); notify users of the upcoming change so they can set a convenient time for it to happen 5) Provide a preview: allow users to toggle between the existing and updated interface so they can get comfortable with the update over time