Building Trust with Evolving User Expectations

The conversation on my last post on AI adoption was helpful. Your comments underscore the complexity and opportunity of integrating AI into our daily work. Thank you! Here’s what I’m taking away: Changing Human Behavior is Harder Than Building A Tool Many of you underscored what I was grappling with: the real hurdle isn’t the technology, but the human response to it. To change the INTERTIA of human behavior, the “what’s in it for me” has to be clear. Without a clear “Day 1 win” that saves time, removes grunt work, or boosts productivity, even the best tools will sit on the shelf. Peer-to-Peer "Aha!" Moments Adoption accelerates when people see their peers succeeding. When colleagues demonstrate how AI genuinely transforms their work, it creates a powerful pull-through effect and a healthy "FOMO" that drives wider usage. We need to amplify these stories and create forums for peer sharing. Continuous Re-engagement AI models evolve incredibly fast. This means we need people to re-try tools they might have dismissed months ago. As one of you put it, we need to "check back in 6 weeks, not 6 months." We also recognize that early adopters often benefit from advanced techniques like curated prompt libraries, and we need to democratize this knowledge. I loved hearing about teams sharing a “prompt of the week” in meetings. We Cannot Ignore Fundamental Fears Several of you brought up the very real anxieties around trust and job security. This concern absolutely impacts willingness to engage, so it's something we must address head-on, transparently and empathetically. Our goal is to empower, not displace, and to focus on how AI can elevate our work and create new opportunities. Adoption is a cycle of curiosity, timing, and capability. It’s about building trust, fostering a safe space to experiment, and recognizing that the opportunity with AI evolves rapidly, requiring us to stay curious and re-engage. 

The trust economy is replacing the attention economy.✅ Marketers have long treated data as their superpower- the more you collect, the sharper your targeting. But as privacy laws evolve, that mindset is hitting a wall. New regulations are redrawing the boundaries of what’s fair, ethical, and legal in data use. Hyper-personalisation still matters. It drives relevance, loyalty, and conversion. Yet creating these experiences while respecting privacy has become the new balancing act. The line between helpful and invasive is thinner than ever. The smartest brands are already adapting. They’re moving from surveillance to service - collecting less, but using it better. They’re making consent experiences simple, data use transparent, and value exchange visible. Instead of chasing clicks, they’re building credibility. Here’s what that looks like in practice: 👉🏻 Audit every data point you collect. If it doesn’t add clear value to the customer, drop it. 👉🏻 Be upfront about how and why you use data. Transparency builds confidence. 👉🏻 Trade access for value - early previews, useful insights, or improved recommendations. Privacy is no longer just about compliance. It’s the foundation of modern marketing trust. The brands that will thrive aren’t those who know the most about their customers but those whose customers choose to share more with them. #futureofmarketing

Innovation can’t come at the expense of trust. Organizations need to manage the privacy, security, and AI-related risks that come with it. Companies like Grammarly get it. That’s why they’re leading the way with a customer centric approach - embedding trust, transparency, and user control into the core of their privacy, security, and AI programs. They have one of THE best TRUST centers out there!! Curious how they do it? Discover Grammarly’s approach by tuning into week’s special edition of the She Said Privacy/He Said Security episode. Justin Daniels and I chat with Jennifer T. Miller, General Counsel, AND Suha Can, CISO, at Grammarly about how the company has built a privacy and security program centered on trust and transparency. I've been SO excited to release this episode - it's packed with how to put the customer first, how privacy and security work together, and why it matters. We covered: ✅ How Grammarly prioritizes privacy and security for its 30 million global users ✅ The evolving partnership between Grammarly’s General Counsel and CISO ✅ Why Grammarly created a transparent privacy, security, and AI web page ✅ Grammarly’s review process for AI-integrated products ✅ Tips for infusing trust into privacy and security programs Key takeaways from our chat: 1️⃣ Build trust by creating transparent, user-focused privacy and security practices 2️⃣ Regularly audit products for privacy, security, and AI-related risks 3️⃣ Foster collaboration between legal and technical teams to mitigate risks and comply with regulations Listen to the full episode here: https://lnkd.in/e3jqhMhZ *** ♻ Share so more companies learn how to put the customer first. 🔔 Subscribe to the podcast to never miss an episode!

Mentoring at NTU LevelUp Round 13 – And Why AI Still Struggles With Trust Today, I had the privilege of mentoring at NTU’s LevelUp Round 13. Every session with young innovators reminds me of something important: We are not building AI for the future. We are building trust for the future. One question stayed with me. “Why don’t people trust AI, even when it performs well?” To answer this, I introduced them to the Trust Equation: Trust = (Credibility + Reliability + Intimacy) / Self-Orientation Surprisingly, this equation explains not only human relationships but also our relationship with AI. 1. Credibility AI must demonstrate consistent competence. Occasional hallucinations destroy months of trust. Transparency and validation matter more than raw power. 2. Reliability People trust what is predictable. AI still varies across tasks, versions, and contexts. Stability is the real differentiator. 3. Intimacy Humans trust systems that communicate clearly, acknowledge uncertainty, and treat users with respect. Empathy is a technical requirement now. 4. Self-Orientation The biggest barrier to trust is the belief that AI serves someone else’s agenda. Governance, ethics, and alignment are no longer optional. How do we build trust in AI? I shared four principles with the students: • build AI as an assistant, not a replacement • explain decisions in human language • embed ethics from day one • co-create with users, not for them Trust is not an output. Trust is a design choice. And the next generation understands this intuitively. Their questions were not about making AI more powerful, but about making AI more human. If this is our future talent pipeline, I am optimistic. Call to Action If you are working at the intersection of AI, healthcare, education, or public service, I would love to exchange ideas on how we can build AI that people can trust. Drop a comment or connect with me. Let’s shape responsible AI leadership together. Nanyang Technological University Singapore

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

*To build trust in complexity, offer small choices and fast feedback* I strongly believe product simplicity and predictability are a superpower. They give the user a sense of control, which is a gift when the world feels so complicated. But some things are legitimately complex. What gives the user a sense of control when predictability is hard to come by? My take: Giving the user a chance to *participate* in the process by laying out steps, enabling them to make specific choices, and offering a clear feedback loop on each small decision. This may make the flow longer, but it gives users a chance to viscerally understand what’s happening. A while ago I got an alarming privacy notification on an important account. I was immediately worried. But the product’s recovery flow calmed me down. Why? It: 1. Laid out all the steps I’d go through, giving me a clear roadmap for what to do. 2. Channeled my anxiety into actions, even if they were small. There were prompts like “Check whether password is compromised? Yes / No”. Is that a necessary prompt? Who would say “no”? But in the moment, the ability to participate in the process of securing my account gave me a sense of control. 3. Gave me fast feedback on each choice by turning each step green on completion. By the end of the list, I felt a sense of relief. Realistically, that product could have taken all those actions without my input. But getting to participate in each step gave me a sense of control. I saw the same thing with a new AI tool my team was working on. Our temptation was to take user input up front and come back with a solution. But our customers didn’t yet trust the magic black box of AI recommendations. Instead, what helped was inserting feedback steps explaining what we were considering and offering the user a chance to change direction at each step. It added friction, but it built trust faster. Then over time, we could remove those interim feedback steps and automatically make decisions. Compare that to a customer service page where you type a question into a contact form and get a message that says, “Thanks, we’ll take care of it.” You don’t really get an understanding of the overall process, a chance to make smaller decisions, or feedback on whether you made the right choices. I’m always stressed about whether I did it right! This applies to people too. When I’m building a relationship with a new manager or peer, I try to frequently outline what I’m doing and give them a chance to redirect. After a few weeks, we know each others’ style and I can stop. Action is the best antidote to fear. Especially when someone is stressed out and longing for control, it helps to ground them in a clear step-by-step process, give them a chance to participate in solving their problem, and letting them know the impact of each choice. That naturally creates some relief, and helps them channel their concern into action. (For regular updates, check out amivora.substack.com!)

Why are you ignoring a crucial factor for trust in your AI tool? By overlooking crucial ethical considerations, you risk undermining the very trust that drives adoption and effective use of your AI tools. Ethics in AI innovation ensures that technologies align with human rights, avoid harm, and promote equitable care. Building trust with patients and healthcare practitioners alike. Here are 12 important factors to consider when working towards trust in your tool. Transparency: Clearly communicating how AI systems operate, including data sources and decision-making processes. Accountability: Establish clear lines of responsibility for AI-driven outcomes. Bias Mitigation: Actively identifying and correcting biases in training data and algorithms. Equity & Fairness: Ensure AI tools are accessible and effective across diverse populations. Privacy & Data Security: Safeguard patient data through encryption, access controls, and anonymization. Human Autonomy: Preserve patients’ rights to make informed decisions without AI coercion. Safety & Reliability: Validate AI performance in real-world clinical settings. And test AI tools in diverse environments before deployment. Explainability: Design AI outputs that clinicians can interpret and verify. Informed Consent: Disclose AI’s role in care to patients and obtain explicit permission. Human Oversight: Prevent bias and errors by maintaining clinician authority to override AI recommendations. Regulatory Compliance: Adhere to evolving legal standards for (AI in) healthcare. Continuous Monitoring: Regularly audit AI systems post-deployment for performance drift or new biases. Address evolving risks and sustain long-term safety. What are you doing to increase trust in your AI tools?

“You’re the one who closed it. You’re the one who owns it.” That’s what I was told after closing my first seven-figure enterprise deal—into a market our company had never sold into before. I assumed there’d be a handoff. Instead, I became the face of the partnership. Onboarding. Product delivery. Executive alignment. Renewal strategy. All of it. We had committed to roadmap work and new workflows. Expectations were sky-high, and we were building as we went. There was no process for handling escalations. So I wrote one. There was no precedent for how to communicate when we missed. So I set the tone. I remember one of our first post-sale calls. The customer asked me to send a recap. I had taken notes—but I hadn’t expected to be the one sharing them. That moment reshaped my thinking. In the post-sale phase, the expectations aren’t lower. They’re higher. The details matter more. And how you follow through becomes the measure of trust. Then came the moment I’ll never forget. At the customer’s annual conference, one of their daily users approached me during a happy hour with a list of concerns. I listened, acknowledged, and promised to follow up. She kept going—not because she didn’t believe me, but because she needed to believe someone would act. That’s when her CEO stepped in and said: “If Sarah says she’ll follow up, she will. I’ve seen it firsthand—she delivers.” That moment stayed with me. Because trust may begin in the sales cycle, but it’s earned through execution—especially when things don’t go according to plan. Years later, at another event, a different end user from that same customer came up to me and said: “Thank you. What we’re doing together is making a real difference in people’s lives.” I managed that relationship for over a decade. What started as a new market experiment became a flagship account and a defining chapter in my career. Here’s what I’ve learned: * Set the tone early. The way you show up during the sales process shapes how customers experience everything that follows. * Think long-term. It’s not just about the close. It’s about building value that compounds over years. * Own the outcome. When issues arise, don’t deflect—lead. Be the steady voice that drives solutions. * Build trust that lasts. Trust is earned through consistency. Deliver on what you say, especially when it’s hard. Because the best sellers don’t just hit quota. They lead. They deliver. They become the reason customers stay.

Health tech is about to struggle, more… In U.S. #healthcare, we spend a lot of time focusing on clinical outcomes, safety, and adherence. Appropriately so. But there’s an underlying layer that often determines whether someone stays or goes with a product or service: expectation. Expectation is an unwritten contract between a person and a system, shaped before a product or service is even used. It’s built through messaging, word of mouth, past and analogous experiences, and hope. When it’s met, you build trust. When it’s not, you lose people. Mis-matched expectations show up all the time: - A “personalized” onboarding that feels generic - A “seamless” tool that’s glitchy - A promise of simplicity that hides complexity Now add #AI to the mix. Smarter, faster digital tools are accelerating the shift. People are expecting more hyper-personalization, real-time responses, and near-perfect predictive accuracy. Strong products can lose relevance overnight when expectations outpace the experience. In fact, Reforge recently published a story on Product-Market Fit collapse, and how it’s happening in real time (link in comments).  For health tech companies, this is a call to action. Innovation takes time. But alignment can start now. Here’s my advice to leaders: + Understand what customers expect + Design experiences to meet or reset expectation early and often + Be honest about what your product can and can’t deliver Because once expectation breaks, so does trust.