Building trust in platform discovery systems

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. We were working to improve product adoption for a US-based platform. Most founders instinctively look at cutting clicks, shortening steps, making the onboarding as fast as possible. We did too — until real user patterns told a different story. 𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐰𝐞 𝐭𝐫𝐢𝐞𝐝 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐜𝐨𝐮𝐧𝐭𝐞𝐫𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞: -Added more decision points -Let users customize their flow -Gave options to manually pick settings -instead of forcing defaults -Conversions went up. -Engagement improved. Most importantly, user trust deepened. You can design a sleek two-click journey. But if the user doesn’t feel in control, they hesitate. Especially in the US, where data privacy and digital autonomy are non-negotiable — transparency and control win. Some moments that made this obvious: People disable auto-fill just to type things in manually. They skip quick recommendations to compare on their own. Features that auto-execute without explicit consent? Often uninstalled. It’s not inefficiency. It’s digital self-preservation. A mindset of: “Don’t decide for me. Let me drive.” I’ve seen this mistake cost real money. One client rolled out an automation that quietly activated in the background. Instead of delighting users, it alienated 20% of them. Because the perception was: “You took control without asking.” Meanwhile, platforms that use clear prompts — “Are you sure?” “Review before submitting” Easy toggles and edits — those build long-term trust. That’s the real game. 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 toggles that show choice. Make the user feel in charge at every key step. 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? 𝐋𝐞𝐭’𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬. #UserExperience #ProductDesign #TrustByDesign #TechForUSMarket #businesscoach #coachishleenkaur LinkedIn News LinkedIn News India LinkedIn for Small Business

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

Across my career, I have seen wave after wave of tools, platforms, and frameworks arrive with promise. ⚙️ ✨ Some were brilliant, some were overhyped, but most faded for the same reason: people could not fully trust what was behind them. The truth is this: the hardest part of building isn’t making something clever. It is making something trustworthy. Here is what trust-first systems do differently: 1️⃣ Transparency over opacity - users can see how decisions get made, not just accept them. 2️⃣ Predictability over surprise - behaviors and incentives remain stable when stress-tested. 3️⃣ Accountability over assumption - clear governance ensures responsibility isn’t left in the shadows. 4️⃣ Education over complexity - people understand what they are using, instead of relying on faith. Shiny tools will always come and go. But systems where trust is earned instead of demanded are the ones that outlast decades and cycles. 🔑 If you are building in Web3, ask yourself: are you shipping another tool, or are you building a system people will trust without blind faith? Drop your one-line definition of trust in technology below 👇 I will highlight the sharpest ones.

I strongly believe that technology can drive processes in a way that builds and strengthens trust between clients & vendors. Tech platform services have made processes in project procurement faster, data-driven, and transparent. Tasks like vendor scouting, assessments, and comparisons that once took weeks can now be done in days. Trust is built when decisions are backed by data and transparency—stakeholders understand why a vendor was chosen. Responsiveness is equally critical; when clients promptly address vendor queries, it fosters confidence on both sides. I remember we worked with a client struggling to find the right vendor for a specialized CapEx project. Through Venwiz, they: - Identified pre-verified vendors in a flash. - Assessed vendor capabilities with over 20+ custom data points. - Used the platform to share updates and ensure alignment with vendors. The result? A faster, more objective, and transparent process that strengthened trust on both sides. For me, the intersection of technology and trust makes decisions more objective and better informed. But these are my experiences, would love to hear your thoughts/additions. #Procurement #CapEx #Trust #Technology

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬: 𝗧𝗿𝘂𝘀𝘁 𝗮𝘀 𝘁𝗵𝗲 𝗖𝗼𝗿𝗻𝗲𝗿𝘀𝘁𝗼𝗻𝗲 𝗼𝗳 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 As Industry 5.0 takes shape, trust becomes the defining factor in securing the future of industrial ecosystems. With the convergence of AI, digital twins, IoT, and decentralized networks, organizations must adopt a structured trust architecture to ensure reliability, resilience, and security. 𝗪𝗵𝘆 𝗶𝘀 𝘁𝗿𝘂𝘀𝘁 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬? With the rise of AI-driven decision-making, digital twins, and decentralized networks, industrial ecosystems need a robust trust architecture to ensure reliability, security, and transparency. 𝗧𝗵𝗲 𝗧𝗿𝘂𝘀𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬 J. Mehnen from the University of Strathclyde defines six progressive trust layers : 𝗦𝗺𝗮𝗿𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 – The foundation of Industry 5.0 trust. This layer ensures secure IoT networks, smart sensors, and seamless machine-to-machine communication for industrial automation. 𝗗𝗮𝘁𝗮-𝘁𝗼-𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 – Moving beyond raw data, this layer integrates AI-driven analytics, real-time insights, and multi-dimensional data correlation to enhance decision-making. 𝗖𝘆𝗯𝗲𝗿 𝗟𝗲𝘃𝗲𝗹 – The backbone of digital security, incorporating digital twins, simulation models, and cyber-trust frameworks to improve system predictability and integrity. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗲𝘃𝗲𝗹 – AI-powered diagnostics, decision-making, and remote visualization ensure predictive maintenance and self-learning systems that minimize operational disruptions. 𝗦𝗲𝗹𝗳-𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – AI-driven systems that self-optimize, self-configure, self-repair, and self-organize, reducing dependency on human intervention. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – The highest level of trust, where decentralized computing, autonomous decision-making, and blockchain-based governance eliminate single points of failure and ensure system-wide resilience. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗔𝗜: 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 To achieve a trusted Industry 5.0 ecosystem, organizations must embrace a structured framework : 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 – Ensuring ethical AI, traceable decision-making, and accountable automation. 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 – Withstanding cyberattacks and operational disruptions. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 – Protecting data, IoT devices, and industrial networks from cyber threats. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 – Ensuring system performance across various conditions. 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Enabling auditability, transparency, and regulatory compliance in automation. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 – Implementing policy-driven AI and decentralized oversight mechanisms.  𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 As industries embrace AI, smart factories, and autonomous supply chains, trust becomes the new currency of industrial success. Ref :https://lnkd.in/dz998J_6