Understanding user trust in software features

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

Trust in health AI isn't just about technology; it's a complex web of factors that must align. Without trust, we will never fully benefit from Health AI tools. The many factors interconnect and make up a complex system impacting the trust. Foundational Trustworthiness *  Data Quality & Unbiased Data: The quality of the data AI is trained with is key. *  AI Characteristics: Competence to perform specific actions. *  Safety: AI systems must not cause harm. *  System Performance & Reliability: Clinical value, accuracy, reliability, and information credibility. AI System Attributes *  Transparency: Communication of functionalities and decision-making processes. *  Explainability: Decisions understandable to users. *  Testability: Systems can be thoroughly tested and validated. *  Technical Selection & Validation: Tool is fit for purpose and functions as intended. Human Factors & Interaction *  User Engagement & Feedback: Involve clinicians and patients in the design, evaluation, and feedback of AI tools. *  Clinician Trust: Built on traits of AI trustworthiness, influences willingness to adopt AI. *  Technological Skills: Knowledge, technological skills, past experiences, and biases that influence trust. *  Human-AI Collaboration: Optimizing systems to enhance teaming and interactions with clinician users. *  Subject-Appropriate Testing: Facilitate subject-appropriate testing and success-monitoring. *  Education & Literacy: Impacts how users understand and interact with AI systems. Contextual & Environmental Factors *  Organizational Assurances: Organizational assurances can affect trust. *  Regulatory Compliance: Adherence to standards and regulations. *  Ethical Guidelines: Addressing bias, data privacy, and informed consent. *  Contextual characteristics: Organizational policies, culture, and specific tasks assigned to healthcare providers. *  Privacy and Data Security: Protecting patient data and ensuring privacy. *  Support and Resources: Appropriate levels of technical understanding and allow for AI to be integrated into the established structures. Societal Impact & Long-Term Considerations *  Equity and Fairness: Tools should not exacerbate existing inequalities. *  Continuous Monitoring and Improvement: Ongoing assessment of AI performance. *  Long-Term Effects on Healthcare: Considering the long-term impact on the patient-provider relationship. *  Addressing Resistance to Technology: Cost, technical concerns, security and privacy, productivity loss, and workflow challenges. *  Privacy Protection: Adhering to regulations and ensuring data security. *  Accountability: Involves organizational policies, regulatory compliance, and establishing clear protocols about who is responsible for errors. Trust is not built on a single factor but is a combination of technical, human, and contextual elements. We need to consider all of these if we want Health AI systems to be adopted. What are you doing to increase trust of AI tools in health?

Over the past few weeks, I’ve spent hours observing small business owners as they integrate AI Agents into their daily workflows. The adoption journey is anything but linear - it starts with enthusiasm, dips into frustration, and then, if designed well, makes the leap into a more sustained usage. I am going to call this moment “the trust leap” - a shift from early curiosity to deeper reliance. Here’s what that journey often looks like: Day 1: Enthusiasm with a Side of Skepticism “What can it actually do? Will it really track leads and keep my data up to date? I’m not sure I trust it to draft my emails just yet.” Week 1: Discovery A “wow” moment - perhaps the Agent completes a task in seconds that would have taken hours. The potential starts to become real. Week 2: Yays and Nays Excitement meets reality. “I could write a personalized email to 20 different customers in just a few seconds - amazing!!.” “But….. it couldn’t handle my complex spreadsheet with all the formulas. And why do I have to explain the same thing multiple times?” Weeks 3-5: Make or Break This is the most critical phase. It’s the moment where user trust is either won or lost. If designed well, users start making the big jump - relying on the AI Agent, feeling confident in its capabilities, and seeing it as a reliable assistant. If designed poorly - if the AI Agent remains inconsistent, hard to control, or unreliable users will abandon it entirely. Week 6-7: If all goes well, Trust Spreads “I feel I have a partner. It’s saving me time and money. I want my team to use it too.” Week 8: A Critical Asset “Please don’t take it away. I’d have to hire someone to do what it does.” .......... While most technology products follow a similar user adoption path, one key distinction I am seeing with AI Agents is that people don’t just see them as tools, they see them as collaborators. People want to build trust that the system understands their intent, adapts to their needs, and won’t fail them at a critical moment. They don’t just need to know how to use it - they need to believe they can depend on it. Designing for user trust will be a critical factor in unlocking AI Agent adoption. Curious what you are learning seeing AI Agents being adopted by “real users” not just tech enthusiasts :)

I just got off the phone with a founder. It was an early Sunday morning call, and they were distraught. The company had launched with a breakout AI feature. That one worked. It delivered. But every new release since then? Nothing’s sticking. The team is moving fast. They’re adding features. The roadmap looks full. But adoption is flat. Internal momentum is fading. Users are trying things once, then never again. No one’s saying it out loud, but the trust is gone. This is how AI features fail. Because they teach the user a quiet lesson: don’t rely on this. The damage isn’t logged. It’s not visible in dashboards. But it shows up everywhere. In how slowly people engage. In how quickly they stop. In how support teams start hedging every answer with “It should work.” Once belief slips, no amount of capability wins it back. What makes this worse is how often teams move on. A new demo. A new integration. A new pitch. But the scar tissue remains. Users carry it forward. They stop expecting the product to help them. And eventually, they stop expecting anything at all. This is the hidden cost of broken AI. Beyond failing to deliver, it inevitably also subtracts confidence. And that subtraction compounds. You’re shaping expectation, whether you know it or not. Every moment it works, belief grows. Every moment it doesn’t, belief drains out. That’s the real game. The teams that win build trust. They ship carefully. They instrument for confidence. They treat the user’s first interaction like a reputation test, because it is. And they fix the smallest failures fast. Because even one broken output can define the entire relationship. Here’s the upside: very few teams are doing this. Most are still chasing the next “AI-powered” moment. They’re selling potential instead of building reliability. If you get this right, you become the product people defend in meetings. You become the platform they route their workflow through. You become hard to replace. Trust compounds. And when it does, it turns belief into lock-in.

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.

There’s a quiet but profound shift in how we think about software design. For years, UX has been about clean interfaces, fewer clicks, and predictable flows. Every interaction started at zero, and designers hard-coded every path. Now, we’re seeing the rise of AX (Agentic Experience) where the relationship between user and system becomes the design center. Instead of tapping buttons and filling forms, you’re working with an agent that remembers context, anticipates needs, and grows smarter over time. The shift changes the definition of success. In UX, success meant efficiency: fewer clicks, faster flows, and a clean interface that inspired trust. In AX, success means compounding value: the agent earns trust by showing its reasoning, adapting to your patterns, and handling more autonomy as confidence builds. This dynamic is already visible in tools we use daily. Imagine an email client that learns your tone and priorities, a design platform that remembers brand rules and proposes layouts, or a CRM that tracks relationships and nudges next best actions. These aren’t distant visions, they’re emerging patterns of AX. The critical points I highlight in my latest article: 1. Memory over reset: agents retain goals and context across sessions. 2. Autonomy over scripts: systems plan and act beyond designer-defined paths. 3. Trust through transparency: agents show their work early, then fade into the background. 4. Value through compounding: each interaction builds on the last, strengthening retention and decision quality. As an AI practitioner, I see this as the next frontier: designing not for usability, but for partnership between humans and systems. The move from UX to AX will redefine how we measure adoption, trust, and long-term engagement in every industry https://lnkd.in/eieEynr4

VC Diaries - 93 : I am now convinced that the startups that will win the next decade will not be the best engineering teams, but the most effective trust distribution networks. We have the technology to build almost anything at effective costs now - Thanks to AI and more. But that tech sits on one side of a deep chasm, while the user stands on the other, shrouded in a fog of doubt. "Who do I call if something goes wrong?" "Will I get what I paid for?" "Is this a scam?" These are the real barriers to scale. Think about the early days of e-commerce. The feature that truly unlocked the market wasn't a better search algorithm or a slicker UI. It was 'Cash on Delivery'. COD was never a payment feature - it was a trust feature. It was a guarantee delivered to the customer's doorstep, a bridge across the chasm of uncertainty. It said, "You don't have to believe our app, just believe your own eyes". Founders obsess over new features while ignoring the leaks in their trust pipeline. A user hesitating on the payment page is not thinking about whether you have one more feature than the competition. They are weighing the probability of disappointment. Every rupee you spend on a generous return policy, a clearer onboarding process, or a responsive human support team directly reduces this uncertainty. You need a 'Trust Roadmap' alongside your 'Product Roadmap'. Systematically map out every point of user anxiety and build a feature or a process to eliminate it. This is the real work. Beyond a point, asking "What can we build next?" is going to drive less impact than asking "What uncertainty can we remove next?". In India, the company that is easiest to believe in will always be the company that wins. What do you think? Do share below in the comments. .. PS: 1 - If you are an early-stage founder and align with all I shared above, do share details of what you are building with us at deals@dexter.ventures 2 - I have started to share my learnings as a VC more proactively here, with a note coming out every morning 8.30am. And I would love to get inputs. Thanks, Anuradha Aggrawal | Dexter Ventures

As AI becomes integral to our daily lives, many still ask: can we trust its output? That trust gap can slow progress, preventing us from seeing AI as a tool. Transparency is the first step. When an AI system suggests an action, showing the key factors behind that suggestion helps users understand the “why” rather than the “what”. By revealing that a recommendation that comes from a spike in usage data or an emerging seasonal trend, you give users an intuitive way to gauge how the model makes its call. That clarity ultimately bolsters confidence and yields better outcomes. Keeping a human in the loop is equally important. Algorithms are great at sifting through massive datasets and highlighting patterns that would take a human weeks to spot, but only humans can apply nuance, ethical judgment, and real-world experience. Allowing users to review and adjust AI recommendations ensures that edge cases don’t fall through the cracks. Over time, confidence also grows through iterative feedback. Every time a user tweaks a suggested output, those human decisions retrain the model. As the AI learns from real-world edits, it aligns more closely with the user’s expectations and goals, gradually bolstering trust through repeated collaboration. Finally, well-defined guardrails help AI models stay focused on the user’s core priorities. A personal finance app might require extra user confirmation if an AI suggests transferring funds above a certain threshold, for example. Guardrails are about ensuring AI-driven insights remain tethered to real objectives and values. By combining transparent insights, human oversight, continuous feedback, and well-defined guardrails, we can transform AI from a black box into a trusted collaborator. As we move through 2025, the teams that master this balance won’t just see higher adoption: they’ll unlock new realms of efficiency and creativity. How are you building trust in your AI systems? I’d love to hear your experiences. #ArtificialIntelligence #RetailAI

Speed looks impressive on a dashboard. Trust looks invisible until it is the only thing left standing. In my time watching systems rise and fall across blockchain, governance, and enterprise, one pattern keeps repeating: yesterday's flashy launch becomes tomorrow's cautionary tale if people cannot rely on the system without second guessing. ⚠️🔁 Here is why trust is the real benchmark: ✅ Reliability wins. People forgive slow features. They do not forgive surprises that break their workflow or money. 🔍 Predictability compounds. Predictable behavior from your product and your team turns first-time users into habitual users. 🛡️ Safety builds adoption. Clear governance, transparent incentives, and recoverable failure modes let partners and enterprises say yes. 🤝 Reputation outlasts velocity. Reputation is earned by consistency, not by hype. A quick builder checklist you can use today: • Track trust metrics, not just usage metrics. Examples: time to first value, repeat actions, governance participation, dispute rates. • Design for observable failure modes so partners can audit and accept risk. • Make promises you can keep and communicate those promises plainly. Plain language builds credibility. • Invest in education and onboarding. Trust is taught more than it is coded. Fast can win rounds. Trust wins decades. If you want something that lasts, build for the latter. 🔥 Share one sentence about a time trust saved or broke a project you care about. I will highlight the most useful examples. 👇

75% of organizations implementing AI fail to exploit the full value. Why? A lack of trust and understanding between the system and its users. Trust determines whether your brilliant AI system gets shelved 🚫 or revolutionizes an organization 🚀 What exactly is trust in this context? 🤔 Trust is the attitude that human or AI, will help me achieve my goals in situations where I may be vulnerable or uncertain. It's that gut feeling that says, "This system will do what I need it to do when I need it done." We begin our "relationship" with AI because we have tasks to accomplish. The exchange comes down to whether the functionality works or not. 👍🏽 👎🏽 This transactional interaction carries the same psychological principles as my human-to-human interpersonal trust. So, what can impact trust in AI? 1️⃣ Individual differences. Some people are naturally more inclined to trust technology than others. 2️⃣ Past experiences. Interaction with similar technologies shapes initial trust levels. A previous disappointing experience with an AI tool might create skepticism toward all AI systems, regardless of their actual capabilities. What can you do? ✅Examine the psychology behind trust in AI and automated systems ✅Utilize frameworks for building appropriate trust Your goal is to create AI systems that not only perform well technically but also gain human confidence and adoption. Trust in AI isn't a nice-to-have. It's foundational. 💯 #humancentereddesign #productmanagement #innovation #AI #systemsthinking #humanfactors