The Impact of Trust on AI Adoption

A lesson from self-driving cars… Healthcare's AI conversation remains dangerously incomplete. While organizations obsess over provider adoption, we're neglecting the foundational element that will determine success or failure: trust. Joel Gordon, CMIO at UW Health, crystallized this at a Reuters conference, warning that a single high-profile AI error could devastate public confidence sector-wide. His point echoes decades of healthcare innovation: trust isn't given—it's earned through deliberate action. History and other industries can be instructive here. I was hoping by now we’d have fully autonomous self-driving vehicles (so my kids wouldn’t need a real driver’s license!), but early high-profile accidents and driver fatalities damaged consumer confidence. And while it’s picking up steam again, but we lost some good years as public trust needed to be regained. We cannot repeat this mistake with healthcare AI—it’s just too valuable and can do so much good for our patients, workforce, and our deeply inefficient health systems. As I've argued in my prior work, trust and humanity must anchor care delivery. AI that undermines these foundations will fail regardless of technical brilliance. Healthcare already battles trust deficits—vaccine hesitancy, treatment non-adherence—that cost lives and resources. AI without governance risks exponentially amplifying these challenges. We need systematic approaches addressing three areas:   Transparency in AI decision-making, with clear explanations of algorithmic conclusions. WHO principles emphasize AI must serve public benefit, requiring accountability mechanisms that patients and providers understand.   Equity-centered deployment that addresses rather than exacerbates disparities. There is no quality in healthcare without equity—a principle critical to AI deployment at scale.   Proactive error management treating mistakes as learning opportunities, not failures to hide. Improvement science teaches that error transparency builds trust when handled appropriately. As developers and entrepreneurs, we need to treat trust-building as seriously as technical validation. The question isn't whether healthcare AI will face its first major error—it's whether we'll have sufficient trust infrastructure to survive and learn from that inevitable moment. Organizations investing now in transparent governance will capture AI's potential. Those that don't risk the fate of other promising innovations that failed to earn public confidence. #Trust #HealthcareAI #AIAdoption #HealthTech #GenerativeAI #AIMedicine https://lnkd.in/eEnVguju

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

74% of business executives trust AI advice more than their colleagues, friends, or even family. Yes, you read that right. AI has officially become the most trusted voice in the room, according to recent research by SAP. That’s not just a tech trend — that’s a human trust shift. And we should be paying attention. What can we learn from this? 🔹 AI is no longer a sidekick. It’s a decision-maker, an advisor, and in some cases… the new gut instinct. 🔹 But trust in AI is only good if the AI is worth trusting. Blind trust in black-box systems is as dangerous as blind trust in bad leaders. So here’s what we should do next: ✅ Question the AI you trust Would you take strategic advice from someone you’ve never questioned? Then don’t do it with AI. Check its data, test its reasoning, and simulate failure. Trust must be earned — even by algorithms. ✅ Make AI explain itself Trust grows with transparency. Build “trust dashboards” that show confidence scores, data sources, and risk levels. No more “just because it said so.” ✅ Use AI to enhance leadership, not replace it Smart executives will use AI as a mirror — for self-awareness, productivity, communication. Imagine an AI coach that preps your meetings, flags bias in decisions, or tracks leadership tone. That’s where we’re headed. ✅ Rebuild human trust, too This stat isn’t just about AI. It’s a signal that many execs don’t feel heard, supported, or challenged by those around them. Let’s fix that. 💬 And finally — trust in AI should look a lot like trust in people: Consistency, Transparency, Context, Integrity, and Feedback. If your AI doesn’t act like a good teammate, it doesn’t deserve to be trusted like one. What do you think? 👇 Are we trusting AI too much… or not enough? #SAPAmbassador #AI #Leadership #Trust #DigitalTransformation #AgenticAI #FutureOfWork #ArtificialIntelligence #EnterpriseAI #AIethics #DecisionMaking

How do you know what you know? Now, ask the same question about AI. We assume AI "knows" things because it generates convincing responses. But what if the real issue isn’t just what AI knows, but what we think it knows? A recent study on Large Language Models (LLMs) exposes two major gaps in human-AI interaction: 1. The Calibration Gap – Humans often overestimate how accurate AI is, especially when responses are well-written or detailed. Even when AI is uncertain, people misread fluency as correctness. 2. The Discrimination Gap – AI is surprisingly good at distinguishing between correct and incorrect answers—better than humans in many cases. But here’s the problem: we don’t recognize when AI is unsure, and AI doesn’t always tell us. One of the most fascinating findings? More detailed AI explanations make people more confident in its answers, even when those answers are wrong. The illusion of knowledge is just as dangerous as actual misinformation. So what does this mean for AI adoption in business, research, and decision-making? ➡️ LLMs don’t just need to be accurate—they need to communicate uncertainty effectively. ➡️Users, even experts, need better mental models for AI’s capabilities and limitations. ➡️More isn’t always better—longer explanations can mislead users into a false sense of confidence. ➡️We need to build trust calibration mechanisms so AI isn't just convincing, but transparently reliable. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐚 𝐡𝐮𝐦𝐚𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐚𝐬 𝐦𝐮𝐜𝐡 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. We need to design AI systems that don't just provide answers, but also show their level of confidence -- whether that’s through probabilities, disclaimers, or uncertainty indicators. Imagine an AI-powered assistant in finance, law, or medicine. Would you trust its output blindly? Or should AI flag when and why it might be wrong? 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬—𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐮𝐬 𝐚𝐬𝐤 𝐛𝐞𝐭𝐭𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬. What do you think: should AI always communicate uncertainty? And how do we train users to recognize when AI might be confidently wrong? #AI #LLM #ArtificialIntelligence

I will never forget the mom in the ER whose child was just diagnosed with Type 1 Diabetes. Tears rolled down her face as she processed this- ‘Will he be okay?’ she asked. ‘Yes. Trust us- we will make sure of it.’ She nodded. There are many skills that a health care professional must have to deliver the best care for their patient. The one that has helped me most as a physician, is establishing trust, often with kind communication. From talking to the parents of the very sick 5-month-old who needed a spinal tap to rule out meningitis, to the teen who was in denial of her pregnancy and didn’t want to tell her mother, to diagnosing a 10-year-old with Type 1 diabetes and giving parents this news, the key ingredient is establishing trust. As AI and innovation explode in healthcare, what role does TRUST play for patient and clinician adoption? The best and most proven AI tools to improve health will not succeed, if they do not have TRUST and relationship building from the clinicians or patients who are using them. Do doctors and patients see AI in health similarly? There have been a number of surveys gauging attitudes towards AI. Recently, Future of Health Index (FHI) Philips questioned over 16,000 patients and 1,926 healthcare professionals in an online survey. The findings included that although 63% of HCPs felt that AI could improve healthcare, only 48% of patients do. Age of patients mattered- only 1/3 of those over 45 felt AI could optimize health. But the issue of TRUST for patients was key: - Over 70% of patients would feel more comfortable about AI use in healthcare, if their doctor or nurse gave them information about it. - 44% of patients would feel more comfortable with AI if reassured an HCP had oversight  - Validated testing for safety and effectiveness of the tool helped 35% of patients more comfortable Clinicians seem to be engaged in AI use in health; the AMA and Healio have shown physicians to be engaged and interested in AI use. In their respective surveys 50% to 68% of doctors are using AI enhanced tools, includeing transcription, search, and patient education. But one theme constantly resonates across all 3 surveys – the desire for SAFETY. 85% of HCPs were concerned about safety and legal risk of AI usage in the FHI survey with over half desiring clear guidelines for usage and limitations. In a time when patients are still waiting almost 2 months to see specialists and clinicians are still feeling overwhelmed with admin tasks, AI can certainly make a difference. But it seems that, at the end of the day, the simple task of TRUST is what will make a difference in the ADOPTION of these tools. And that means having clinicians and patients understand, and be comfortable with the technologies, and ensuring safe and tested innovations as well. Do you think TRUST is important in AI tool integration? #innovation #trust https://lnkd.in/es3tjwib

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.

Just read a fascinating piece by Tetiana S. about how our brains naturally "outsource" thinking to tools and technology - a concept known as cognitive offloading. With AI, we're taking this natural human tendency to a whole new level. Here's why organizations are struggling with AI adoption: They're focusing too much on the technology itself and not enough on how humans actually work and think. Many companies rush to implement AI solutions without considering how these tools align with their teams' natural workflow and cognitive processes. The result? Low adoption rates, frustrated employees, and unrealized potential. The key insight? Successful AI implementation requires a deep understanding of human cognition and behavior. It's about creating intuitive systems that feel like natural extensions of how people already work, rather than forcing them to adapt to rigid, complex tools. Here are 3 crucial action items for business leaders implementing AI: 1) Design for Cognitive "Partnership": Ensure your AI tools genuinely reduce mental burden rather than adding complexity. The goal is to free up your team's cognitive resources for higher-value tasks. Ask yourself: "Does this tool make thinking and decision-making easier for my team?" 2) Focus on Trust Through Transparency: Implement systems that handle errors gracefully and provide clear feedback. When AI makes mistakes (and it will), users should understand what went wrong and how to correct course. This builds long-term trust and adoption. 3) Leverage Familiar Patterns: Don't reinvent the wheel with your AI interfaces. Use established UI patterns and mental models your team already understands. This reduces the learning curve and accelerates adoption. Meet them where "they are"" The future isn't about AI thinking for us - it's about creating powerful human-AI partnerships that amplify our natural cognitive abilities. This will be so key to the future of the #employeeexperience and how we deliver services to the workforce. #AI #FutureOfWork #Leadership #Innovation #CognitiveScience #BusinessStrategy Inspired by Tetiana Sydorenko's insightful article on UX Collective - https://lnkd.in/gMxkg2KD

You can move fast with AI. But are your people still following you? In one org we recently studied, the leadership team rolled out 5 new AI tools in 3 months. → Engineers were told to use AI copilots → HR was told to launch AI onboarding → Sales got AI content tools → Ops got AI automation dashboards → Legal got...nothing On paper, it looked like transformation. In practice, it looked like chaos. Teams didn’t know who owned what Adoption was uneven or quietly resisted Data risks were flagged and ignored Managers were guessing what success looked like This is what happens when speed becomes the KPI. And trust becomes the cost. People stop asking questions. They start avoiding eye contact in reviews. They nod in meetings. Then go back to old ways of working. That’s how AI fatigue sets in. Not because the tech failed. But because the rollout forgot the people. If you're a CXO, ask yourself: → Do your teams know why each AI tool was chosen? → Do they trust the data flowing through it? → Do they feel like part of the process or just a use case? You can’t scale what people don’t trust. And you can’t build trust through memos. At PeopleAtom (now rebranding), we’ve seen organizations reverse this. → CXOs slowing down to bring teams into the “why” → Clear role-based guidelines that reduce fear → Adoption metrics that include trust, not just usage → Peer feedback loops across functions before public rollout That’s what makes AI stick. Not another tool. Not another slide deck. But clear, people-first implementation that earns buy-in. If you're leading fast and feeling friction — you're not alone. DM me ‘CXO’ I’ll show you how other CXOs are handling this without stalling out. Fast is good. Trusted is better.

Friends in sales! A new Harvard Business Review article reveals what I've been saying all along about LLMs: "the root issue isn't technological. It's psychological." Here are Six Principles (and why behavioral change is everything): This study focuses on customer service chatbots, but the insights inform AI adoption across organizations (and what leaders need to do differently). LLMs don't behave like software. They behave like humans. Which means we need a human behavioral approach. This is about change management. (BTW, this is what we do at AI Mindset at scale, but more on that below.) ++++++++++++++++++++ SIX PSYCHOLOGICAL PRINCIPLES FOR EFFECTIVE CUSTOMER SERVICE CHATBOTS: 1. Label Your AI as "Constantly Learning" Users are 17% more likely to follow an AI's suggestions when it's framed as continuously improving rather than static. People forgive small errors when they believe the system is getting smarter with each interaction, similar to working with an enthusiastic new hire. 2. Demonstrate Proof of Accuracy Trust comes from results, not technical explanations. Showing real-world success metrics can increase trust by up to 22%. Concrete evidence like "98% of users found this helpful" is more persuasive than explaining how the tech works. 3. Use Thoughtful Recognition (But Don't Overdo It) Subtle acknowledgment of user qualities makes AI recommendations 12.5% more persuasive. BUT! If the flattery feels too human or manipulative, it backfires. Keep recognition fact-based. 4. Add Human-Like Elements to Encourage Better Behavior Users are 35% more likely to behave unethically when dealing with AI versus humans. Adding friendly language, empathetic phrasing, and natural interjections can reduce unethical behavior by nearly 20% by creating a sense of social connection. 5. Keep It Direct When Users Are Stressed When users are angry or rushed, they want efficiency, not empathy. Angry users were 23% less satisfied with human-like AI compared to more straightforward responses. In high-stress situations, clear and direct communication works best. 6. Deliver Good News in a Human-Like Way Companies rated 8% higher when positive outcomes came from a human-like AI. People tend to attribute positive outcomes to themselves, and a warm, human-like delivery amplifies that emotional boost. Focusing on psychological principles rather than technical features in chatbots and adoption will create AI experiences that users actually want to adopt, driving both satisfaction and results. Huge thanks to the authors: Thomas McKinlay 🎓, Stefano Puntoni, and Serkan Saka, Ph.D. for this tremendous work! +++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your organization is ready to create an AI-powered culture—not just add tools—AI Mindset would love to help. We drive behavioral transformation at scale through a powerful new digital course and enterprise partnership. DM me, or check out our website.

🚨 Why Enterprise AI Doesn’t Fail Because of Bad Models: It Fails Because of Broken Trust Most AI teams build features first and try to earn trust later. We flipped that model. At Calonji Inc., we built MedAlly.ai, a multilingual, HIPAA-compliant GenAI platform, by starting with what matters most in enterprise AI: ✅ Trust. Not as a UI layer. Not as a compliance checklist. ✅ But as the core architecture. Here’s the Trust Stack that changed everything for us: 🔍 Explainability = Adoption 📡 Observability = Confidence 🚧 Guardrails = Safety 📝 Accountability = Defensibility This wasn’t theory. It drove real business outcomes: ✔️ 32% increase in user adoption ✔️ Faster procurement and legal approvals ✔️ No undetected model drift in production 📌 If your platform can't answer "why," show behavior transparently, or survive a trust audit, it's not ready for enterprise scale. Let’s talk: What’s in your Trust Stack? #EnterpriseAI #AITrust #ExplainableAI #AIArchitecture #ResponsibleAI #SaaS #CTOInsights #PlatformStrategy #HealthcareAI #DigitalTransformation