Why Speed Can Reduce Trust in Technology

Unless you’ve been living under the world’s biggest, soundproof rock for the last three years, you’re undoubtedly aware that #artificialintelligence is all but mandatory if you aspire to any degree of entrepreneurial success. But here’s a thought from the old school: Just because you can do something faster doesn’t mean people want it faster. Or even want it at all. Over the last few years, artificial intelligence has gone from novelty to necessity. And somewhere between #ChatGPT and boardroom FOMO, #entrepreneurs started believing that speed equals success. But let’s take a step back. AI can now write 1,000 emails while you sip your morning coffee. It can generate more leads, spit out more campaigns, and automate more sales than your entire team combined. Impressive? Sure. Useful? Depends. See, we often forget one tiny, inconvenient truth: Customers are still human. And humans, unlike algorithms, have this thing called free will. They don't convert just because your AI shouted louder or faster. They convert because they trust you, need you, or like how you make them feel. Which brings me to this little gem: AI can accelerate output. It can’t accelerate trust. It can scale communication. It can’t scale connection. In my years of investing, I’ve learned this: Every new wave of tech promises to make things faster, better, cheaper. But the businesses that endure? They’re the ones that still understand people. They know that speed without empathy is spam. Automation without intuition is noise. So, yes—use AI. Harness it. Build with it. But don’t confuse productivity with persuasion. And don’t expect a robot to replace the patience, persistence, and human touch that business truly requires. Because at the end of the day, your customer isn't an API. They're a person. And people still take their sweet time to decide—especially when it comes to trusting you with their money.

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.

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

Every instant payment hides a silent question: “Can you really trust who’s on the other side?” Today’s fast payment systems move money in seconds. But trust still lags behind. Fraud, impersonation, and misdirected transfers remind us that speed without identity is speed without safety. Where Trust Breaks Down • Authentication Layer – Users are verified through fragmented methods: passwords, SMS codes, app approvals. Convenient, but prone to social engineering. • Validation Layer – Payee details are often unchecked, leading to push payment fraud and reconciliation headaches. • Settlement Layer – Funds move instantly, but if the identity is wrong, recovery is almost impossible. This separation creates friction for honest users — and opportunities for bad actors. Now imagine a Payments Identity Credential (PIC): – National ID attributes + payment metadata bound into a verifiable credential. – Wallet-based consent, where you disclose only what’s needed. – End-to-end authentication of payer, payee, and provider — in real time That’s a structural shift. Fraud risk collapses, onboarding becomes frictionless, and inclusion expands — because identity becomes portable, private, and interoperable across banks and wallets. But new questions emerge: how do we govern credentialing hubs, balance privacy with oversight, and keep competition open? #payments #fraud #instantpayments #openfinance

Superior technology usually loses to trust deficits. The NewCos who win understand this deeply. LegacyCos provide coverage - decades of embedded relationships, compliance infrastructure, institutional safety. NewCos provide velocity - direct paths to outcomes without legacy constraints. Enterprise buyers increasingly ask: "Will this deliver outcomes fast?" and "Can I justify this choice?" LegacyCos excel at the second. NewCos must excel at both. The solution isn't copying LegacyCo's relationship playbook. It's building trust infrastructure optimized for a high clockspeed world. The Trust Ceiling Trust deficits create velocity ceilings regardless of technology quality. This is structural, not situational. Your product solves problems 10x faster, but if buyers don't trust delivery, speed becomes irrelevant. NewCos must engineer trust through domain expertise and proven outcomes - without institutional baggage. The NewCo Playbook: Wedge and Expand Today's mega-deals are fragmenting into smaller, milestone-driven projects. This creates opportunities LegacyCos are too expensive and slow to pursue effectively. Your strategy isn't competing on massive transformations. It's winning wedges and expanding from strength. Domain Expert Credibility Hire thought leaders who understand enterprise needs but aren't tied to legacy delivery models. Import domain knowledge, not institutional constraints. Champion-Led Wedge Entry Embed with operational teams before procurement involvement. Find specific pain points where speed matters more than coverage. Example: You deliver working inventory optimization in 30 days, reducing costs 15%. LegacyCo proposes 12-month workflow optimization before any results. The Speed Advantage LegacyCos take 12 months because they're carrying decades of institutional process. You take 3 months because you're purpose-built for outcomes. This speed differential compounds: - Faster implementation → faster results → stronger references - Wedge wins → adjacent problems → organic expansion - Proven outcomes → higher trust → shortened sales cycles The Readiness Test: Before entering any market → Do you have domain experts who'll publicly endorse your approach? → Can you name three specific wedges where your speed beats their coverage? → Do you have proof points of 3x faster outcomes than traditional approaches? If yes, you have trust infrastructure built for velocity. If no, you're just another vendor. The Choice Enterprise buyers increasingly prefer fast wins over comprehensive coverage. You can build trust infrastructure optimized for velocity, or watch superior technology stall in trust deficit. In enterprise markets: speed without trust stalls; trust without speed stagnates. The network rewards companies that understand both realities. (Full version sent to newsletter subscribers)

🚗 Xiaomi, “China Speed” — and the Hidden Cost of Skipping Process Xiaomi made headlines entering the EV space with breakneck speed. They promised “China speed” in an industry known for caution. And now? Reports of critical failures, software immaturity, and safety risks are surfacing. 💡 The lesson? Shipping fast ≠ building trust. In consumer tech, fast iteration is survivable. Your phone crashes? You reboot. In automotive, there’s no reboot at 120 km/h. Aviation learned this the hard way. That’s why they have DO-178C, traceability, verification, and process rigor baked into the culture. Meanwhile, automotive is caught in the middle: 💨 Chasing agility and startup speed ⚠️ But responsible for life-critical systems Standards like ASPICE and ISO 26262 aren’t roadblocks—they’re guardrails. Xiaomi’s challenge isn’t just technical. It’s cultural. It’s a mindset shift from: “How fast can we ship?” to “How long will we stand behind what we shipped?” Process isn’t the enemy. 🚀 Reckless speed is. Let’s stop treating structured engineering as a burden—and start recognizing it as the backbone of safety and brand longevity. #AutomotiveSoftware #ChinaSpeed #FunctionalSafety #ASPICE #ISO26262 #EngineeringCulture #XiaomiEV #SoftwareDefinedVehicle #ProcessMatters #SafetyCritical #FromResistanceToRespect

The most dangerous thing in SaaS isn’t slow velocity. It’s false confidence. Founders love to brag about how fast they’re moving. “Move fast and break things.” “30 customer interviews this quarter.” “85% adoption of our new dashboard.” A roadmap packed with “strategic initiatives.” On the surface, it all looks impressive. In reality, it’s a mirage. Speed without systems breaks trust. Interviews without synthesis generate noise, not insight. Feature adoption measures activity, not value. Roadmaps optimize for politics, not outcomes. It feels like progress. It looks like traction. Yet this is exactly why so many Series A to C companies stall at $10M ARR, wondering why expansion has dried up and churn won’t budge. The truth is that most product orgs are optimized for building features, not creating outcomes. That’s why their dashboards glow green while customers quietly slip away. The companies that scale sustainably aren’t the ones that ship the fastest or adopt the most features. They’re the ones that: • Build systems that translate customer discovery into conviction • Reward PMs for solved problems, not shipped features • Measure time-to-outcome, not feature adoption • Ruthlessly cut roadmaps to the 3 bets that actually impact retention and expansion Progress isn’t speed. Progress is clarity. If your dashboards are green but your renewals are red, it’s time to stop measuring activity and start architecting outcomes.

The "Trust Deficit" is widening. One reason for it is AI hype & deceptive AI marketers. Another reason for it is poor customer experiences. When people experience uncertainty, their trust becomes more fragile, and more easily broken by misalignment between what’s said and what’s felt. When you layer speed, novelty, and automation on top of unclear systems, you activate psychological insecurity, NOT confidence. The push for AI is exposing 3 cracks in business foundations: 1. Systems are misaligned or siloed Strategic goals aren’t matched by operational capabilities or human behavior patterns. CX doesn’t reflect brand values. Sales, ops, and leadership are often pulling in different directions. 2. Leaders are acting reactively, not reflectively They’re racing to implement before interrogating use cases. Tools are deployed without behavior change - and friction scales. 3. Cognitive capacity is maxed out (internally and externally) Employees are overwhelmed. Customers are overstimulated. And no one has enough bandwidth to pause and question the cost of that next AI integration...and how humans fit into the integration. Over the past two years, we’ve watched businesses scramble to claim “AI-first” positioning...a badge worn to signal innovation, speed, and progress. But customers? They’re reading something very different: → “You’re automating connection.” → “You’re scaling without listening.” → “You’re prioritizing output over outcomes.” And beneath it all, a deeper message: “You care more about tech trends than my customer experience.” This is the trust deficit - and AI is accelerating it. Rebuild trust by doing the following: - Navigate AI with intention, not reactivity - Design human-first systems enhanced by AI, not decimated by it - Reframe "agentic AI" as a collaborator, not a competitor In a time of uncertainty, put people (Customers & Team) first. Hi, I’m Kim Breiland, founder of Kim Breiland Consulting, where we help leaders and founders combine behavioral science and AI to build trusted, innovative companies. Ready to get started? Let's get some time on the calendar: https://lnkd.in/e-RkZUyy

The 23andMe story highlights how venture capital due diligence to evolve: Technology without trust is a dead end. In healthcare, trust needs to be central to the evaluation process—not just speed to market or the technology. Trust in the product, in the data security, in the quality of care, and in the company’s ethical practices is the foundation for sustainability. Historically, there are plenty of examples of speed without trust ultimately failing: Honor Time to Market: 3 years (2014–2017) Honor aimed to disrupt home healthcare for elderly patients with tech but struggled with privacy concerns and data security. The lack of transparency and failure to establish robust security measures led to low adoption. Pear Therapeutics Time to Market: 4 years (2013–2017) Pear launched one of the first digital therapeutics for addiction treatment, but providers weren't recommending it as the clinical evidence and validation were poor. Pear was seen as rushing to market with a solution that wasn’t thoroughly tested in real-world settings. Lemonade Health Time to Market: 1.5 years (2018–2020) Lemonade Health offered home prescriptions and online consultations but faced trust issues over the accuracy of prescriptions and the security of patient data. Babylon Health Time to Market: 5 years (2013–2018) Babylon Health, one of the leaders in AI-powered telemedicine, was questioned for accuracy and issues around data privacy that caused hesitation from providers and was again perceived as putting technology before clinical care. HealthSpot Time to Market: 2 years (2011–2013) HealthSpot introduced telemedicine kiosks, offering remote consultations in physical locations, but it didn’t earn clinical validation and buy-in from key stakeholders. Spring Health Time to Market: 3 years (2016–2019) Spring Health tried to disrupt corporate mental health services with a personalized approach but was perceived as too new and unproven for employers and users who were concerned about the security of their mental health data. 23andMe is a reminder that no company, regardless of its size or consumer base, is immune to the damage caused by a lack of trust. For VCs, this means evolving the due diligence process. Don’t just focus on how quickly a product can get to market or how innovative the technology is—ask: is this company earning trust with the right stakeholders? This is where physicians in VC can be key - to ensure that trust is front and center in every investment. They understand what patients will trust, what clinicians will accept, and what needs to be done to ensure clinical validation and data security. #healthtechPhyCap FundDutch RojasJoseph JasserPaul Slosar, MD, MHCDSTracy PooleFrederic Liss, M.D.Giovanni L.

Are we sacrificing safety for speed? I just got off a call with a podcast host talking about about how humanity has become the #AI test bed! Today, powerful AI tools are being released to millions at a breakneck pace. In the past 2 weeks alone, four more #LLMs were released in an already crowded space - each with larger context window, better performance etc. And yet all of them still carry the same flaws - lack of accuracy (hallucinations), bias which makes them difficult to trust for most applications beside a few. In the rush to roll out the latest generative AI and large language models, it seems the companies developing them have forgotten some important lessons from the past. I remember a time, not too long ago, when software was meticulously crafted, rigorously tested, and slowly rolled out to users. When products were built to last. My Sony Music System from 1993 still works. Please don’t judge! In the mad dash to be first to market with the hottest new #LLM, those practices have fallen by the wayside. The companies themselves admit they can't fully anticipate how the models will behave in the wild. We are all becoming unwitting test subjects. Biases from the training data used by the generative models continue to rear their ugly head in their outputs along with hallucinations. A recent study showed that over 80% of the population believe everything that is written. Every lapse in truthfulness undermines trust and spreads misinformation at an unprecedented scale. History shows that a lack of testing and human oversight can cause grievous implications. While the potential of LLMs is immense, the risks of undertested AI infiltrating every aspect of our lives and society are greater. Move fast and break things is a dangerous tactic. What can we do? ➡️ Lobby for proactive regulation, stronger industry standards, and major investment in AI ethics and safety research. ➡️ Researchers & practitioners need to build evaluation frameworks into every step of the process, not just bolted on after the fact. ➡️ Business leaders need to step back, slow down and embrace the responsible practices and wisdom of other safety-critical industries like aerospace, medicine and nuclear energy. As users: ➡️ Demand evidence of rigorous testing, risk assessment, and bias analysis before using or promoting an AI system. ➡️ Prioritize AI solutions that incorporate responsible development practices, oversight, and safety considerations. ➡️ Embrace safety-critical engineering. The plane is being built as we're flying it with #generativeAI. Let's make sure we're heading to a destination we actually want to arrive at. It's still not too late to put in trustworthy practices and safeguards. Our collective future may depend on it. #AI #ResponsibleAI #ethicalAI #genAI Center for Equitable AI and Machine Learning Systems Latimer Oslo for AI Michael Anton Dila Kem-Laurin L.