Why Technical Reliability Doesn't Fix Customer Trust

Your network is perfect. So why don’t customers love you? 💔 Telecom delivers five-nines reliability, guaranteeing near-perfect service with less than six minutes of failure annually. It is a remarkable engineering feat. Yet customers barely notice it, and many do not care. In 2024, the global telecom average Net Promoter Score was just 31, placing it at the very bottom of all consumer-facing industries. That number is conclusive. Logistics scores around 38, financial services around 44, and tech platforms often hit 50 or higher. Even more telling are the people who ride our networks and love us more than carriers. In Australia, Telstra’s MVNO brands scored 43.4 NPS, while Telstra itself scored 6.9. In the United States, Consumer Cellular, Inc., a value-focused MVNO, achieved an unprecedented 85 Customer Satisfaction score, far outpacing the national carriers in the high seventies. Why are Telcos struggling with customer satisfaction while other industries and even MVNOs flourish? Because performance is invisible. Customers do not remember uptime or latency. They remember their last bill shock, the hours wasted navigating an IVR, or the frustration of unclear plan details. Research supports this: user satisfaction correlates more with the last interaction than network quality metrics. Whispers of empathy and simplicity echo louder than the hum of a tower. Compare this to platforms like WhatsApp or Netflix. They run on best-effort infrastructure; no guarantees, no SLAs, and yet they are adored. They understood that trust, clarity, and emotion drive loyalty. If telecom operators want to escape the NPS bottom, they must stop leading with specs and start leading with emotional intelligence. They need to transform pricing into a conversation that respects budgeting and trust, make onboarding feel like welcoming friends rather than ticking boxes, and ensure support sounds like a neighbor who cares, not a script that reads. Your network is perfect. Now build a brand that works.

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗰𝗮𝗻'𝘁 𝗳𝗶𝘅 𝗮 𝘁𝗿𝘂𝘀𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗔𝗻𝗱 𝗺𝗼𝘀𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗹𝗲𝗮𝗿𝗻 𝘁𝗵𝗶𝘀 𝘁𝗵𝗲 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝘄𝗮𝘆. Last month, a packaging plant director told me his team had spent six figures on a digital improvement platform. Digital dashboards. Real-time analytics. Mobile problem-solving. Six months in: → 19% adoption rate → 8 improvement ideas submitted → 2 actually implemented The technology was brilliant. The culture wasn't ready for it. Here's what I see happening in several organisations: Leaders buy digital platforms to accelerate improvement. • But the platform lands in a culture where people already don't speak up. • Where suggestions get ignored. • Where middle managers dismiss ideas as "impractical."    Technology doesn't create trust. It exposes the lack of it. The real problem isn't the software. It's that your people stopped believing their input matters. At this plant, operators had submitted handwritten suggestions for 18 months. No-one ever responded. Why would they trust a digital system when nobody trusted their judgment? • AI can spot process anomalies in seconds. • But it can't spot why your morning shift leader dismisses every idea from the packing line. • Or why your engineers talk over your operators in problem-solving meetings.    𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝗱: The director shifted his approach. Three things that actually worked: 𝟭. 𝗛𝗲 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗹𝗶𝘀𝘁𝗲𝗻𝗶𝗻𝗴 𝗯𝗲𝗳𝗼𝗿𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗶𝗻𝗴. Daily gemba walks. Not to inspect. To hear what people had been trying to say. 𝟮. 𝗛𝗲 𝗴𝗮𝘃𝗲 𝘁𝗲𝗮𝗺𝘀 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆 𝘁𝗼 𝗮𝗰𝘁. Under £75? Just do it. No approval needed. 𝟯. 𝗛𝗲 𝗰𝗲𝗹𝗲𝗯𝗿𝗮𝘁𝗲𝗱 𝘁𝗵𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗳𝗮𝗶𝗹𝗲𝗱. Because trying matters more than perfecting. The platform usage jumped to 68% in three months. Not because they added features. Because people finally believed someone was listening. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗺𝗮𝗻𝘆 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗺𝗶𝘀𝘀: • Cross-functional collaboration doesn't happen in a digital system. • It happens in conversations. In huddles where the maintenance tech and the quality inspector solve it together. In break rooms where trust gets built. • Your technology amplifies what you already have.    If you have a culture where people feel heard, tools accelerate everything. If you don't, tools just digitise the silence. The strategic question isn't "What platform should we buy?" It's "Have we built the foundation where technology can actually work?" ✅ Start with people. ✅ Build the relationships. ✅ Create the safety to speak up. Add tools second. 👉 If you're considering buying another platform, stop. DM me 'TRUST' first - I'll show you what to fix before you spend another dollar.

Hertz rolled out an AI scanner to streamline vehicle damage detection. It turned into a customer support nightmare. The scanner, developed by UVeye, uses high-resolution cameras to capture 360-degree images of returned vehicles. AI then compares those images to identify new damage and automatically generates a bill if the damage exceeds a set threshold. Sounds efficient, right? The system was supposed to solve a known pain point: inconsistent inspections, unclear policies. But instead of improving the experience, they made it more confusing and opaque. One customer returned their rental and got a $350 charge—no human interaction, just an automated email and a chatbot that couldn’t answer questions. The problem wasn’t just technical. It was structural. There was no human failsafe. No opt-in. No clear communication. Customers couldn’t even reach support through the mobile app. And that’s where the real risk lies. When new technology is deployed without proper safeguards, it doesn’t just frustrate customers. It damages trust. And once that trust is lost, it’s hard to get it back. Hertz’s story isn’t just a PR crisis. It’s a cautionary tale for every CX leader racing to adopt AI. Efficiency can’t come at the cost of transparency and human connection. If customers feel like the system is working against them, they won’t stay quiet. They’ll walk. 

Good news: your team cares about your customers. Bad news: your systems don't. We've been taught to treat trust like a feeling. Something we earn through good intentions and healthy metrics. If the culture is right and the scores look fine, the relationship must be fine. That isn't how it works though. Trust erodes inside organizations full of people who genuinely care, because caring doesn't rewrite a policy that punishes the customer. Caring doesn't stop an automation from kicking in at exactly the wrong moment. Caring doesn't fix a renewal process that treats the customer like the bad guy. Your intentions don't show up in your customer's experience. Your systems do. Don't believe me? Ask your support team. Do we have any customer policies that make it harder for us to help them? Do we have any systems that work counter to how we say we want to show up for our customers? Do we have any automations or workflows that make customers feel like a number instead of being valued? I created the Customer Trust Equation because organizations need to see all the moving pieces that create or destroy trust. Trust = Consistency + Response + Connection + Value − Friction When you can see trust as a system, you can design it as one. And friendly reminder... your frontline team should have a very loud say in the trust systems you create.

🔧 Great Tech Doesn’t Deliver Great Fulfillment—People Do! In the race to be the most tech-forward 3PL, some companies have leaned hard into the idea that their homegrown software is the secret sauce. Claims abound: their WMS is revolutionary, their OMS is unmatched, and their AI slashes parcel spend with surgical precision. But here’s the reality: if software alone were the answer, customer churn in 3PL would be near zero. It’s not. The market already offers a wealth of exceptional, purpose-built platforms.  Logiwa,  ShipHero, KIBO, Aptos Retail—these technologies power hundreds, even thousands, of brands and operators with depth, scale, and faster time to value than most in-house tools can ever deliver. So what’s the real differentiator? Operational excellence. You can’t code your way out of a missed SLA. You can’t engineer around a disengaged site manager. And you definitely can’t automate the trust it takes to grow a brand’s business. I’ve seen this from both sides of the table. As SVP Global Logistics at Hasbro, we partnered with global 3PLs around the world. Technology mattered—but what truly earned loyalty was execution. Relationships. Reliability. A shared obsession with the customer and a partnership with with 3PL partners who were as committed. More recently, as COO of Ware2Go, we focused on scaling a national fulfillment network that enabled independent 3PLs to deliver Amazon SFP-certified performance across 20+ sites to help brands of all sizes compete in a hyper competitive and rapidly evolving landscape. Our commercial engine that worked because of the people—warehouse operators, partners, client teams—not in spite of them. The goal wasn’t to be the best tech company in logistics. It was to build trust through consistency, accountability, and shared growth which delivered growth for all. Think about it: when was the last time you walked into a Michelin-starred kitchen and judged the meal by the brand of stove? You didn’t. You valued the experience—the care, the consistency, the execution. That’s what left an impression. Fulfillment is no different. Tech is essential—but it’s no longer "the" differentiator. What matters most is the team behind it, the culture that drives it, and the accountability that holds it all together. In a world where logistics is getting more complex, brands don’t need more dashboards—they need more dependability. The best 3PLs don’t just ship products or obsess over technology—they obsess over the customer! That’s what wins trust, and that’s what keeps it. #logistics #3PL #WMS #OMS #TMS #operationalexcellence

Earlier this year, we interviewed over 50 shippers. The results shocked us: 60% had lost trust in a logistics partner, not because of price or capacity, but because of poor communication. And no amount of tech could fix it. That hit home for me. I've seen it firsthand: Freight doesn't fail because of bad platforms. It fails because of broken relationships. I've watched "automated freight" promises crumble when a simple phone call could've saved the load. Your relationships matter more than your software. (Way more than you think.) A great team can: ✓ Prevent small issues from becoming crises ✓ Make judgment calls when data falls short ✓ Keep shippers informed before they even ask ✓ Build trust that no algorithm can automate Here's what transformed our business: We stopped hiding behind platforms. We started betting on people. Now, the questions I ask sound different: → Who owns the outcome when a load goes sideways? → How fast can your team actually respond? → Do your people communicate, or just update? → How do you train for empathy, not just efficiency? Red flags I've learned to watch for: ❌ Relies on dashboards instead of direct conversations ❌ Blames "the system" for human mistakes ❌ Talks metrics but can't name relationships ❌ Goes silent the moment things go wrong Because here's what matters: Software evolves. Integrations break. Markets swing. But a team that shows up for you? That changes your lanes forever. ↓ Swipe through the carousel below to see why trust still scales faster than software.

Building Trust in Agentic Experiences Years ago, one of my first automation projects was in a bank. We built a system to automate a back-office workflow. It worked flawlessly, and the MVP was a success on paper. But adoption was low. The back office team didn’t trust it. They kept asking for a notification to confirm when the job was done. The system already sent alerts when it failed as silence meant success. But no matter how clearly we explained that logic, users still wanted reassurance. Eventually, we built the confirmation notification anyway. That experience taught me something I keep coming back to: trust in automation isn’t about accuracy in getting the job done. Fast forward to today, as we build agentic systems that can reason, decide, and act with less predictability. The same challenge remains, just on a new scale. When users can’t see how an agent reached its conclusion or don’t know how to validate its work, the gap isn’t technical; it’s emotional. So, while Evaluation frameworks are key in ensuring the quality of agent work but they are not sufficient in earning users trust. From experimenting with various agentic products and my personal experience in building agents, I’ve noticed a few design patterns that help close that gap: Show your work: Let users see what’s happening behind the scenes. Transparency creates confidence. Search agents have been pioneer in this pattern. Ask for confirmation wisely: autonomous agents feel more reliable when they pause at key points for user confirmation. Claude Code does it well. Allow undo: people need a way to reverse mistakes. I have not seen any app that does it well. For example all coding agents offer Undo, but sometimes they mess up the code, specially for novice users like me. Set guardrails: Let users define what the agent can and can’t do. Customer Service agents do it great by enabling users to define operational playbooks for the agent. I can see “agent playbook writing” becoming a critical operational skill. In the end, it’s the same story I lived years ago in that bank: even when the system works perfectly, people still want to see it, feel it, and trust it. That small "job completed" notification we built back then was not just another feature. It was a lesson learned in how to build trust in automation.

The engineering dashboard was a sea of green. Uptime: 99.99%. Deployment frequency: perfect. But the app store reviews told a different story: Buggy. Unreliable. Gave up and deleted. A senior engineer finally voiced the quiet part in a retro: We’re a fire department that’s proud of our response time. Our users just want to live in a neighborhood that doesn’t keep burning down. Leadership was confused. They had invested in top-tier monitoring tools. Their on-call team resolved incidents faster than industry benchmarks. Every system alert was addressed with precision. But user churn was steadily climbing. Someone decided to read the support tickets for a week. Monday: User A tries to upload a large file. It fails with a spinning wheel and no message. He assumes the feature is broken and never tries again. Wednesday: User B hits a cryptic '500 Internal Server Error' at checkout. She feels the site is insecure, abandons her full cart, and doesn't return. Friday: User C sees 'TypeError: Cannot read properties of undefined.' He has no idea what that means. He just concludes the app is low-quality and cancels his subscription. These user-facing failures are invisible on most technical dashboards. But once you see the experience from their side, you can't unsee it. The team was measuring their efficiency at fixing problems. Not the user's experience of having them. Meanwhile, a key competitor was winning praise for reliability. Their secret? They treated every error message as a critical piece of user experience copy. What they had was a User-Facing Error Strategy. The missing layer between technical failure and human frustration. Without it: - Your product feels brittle and untrustworthy, no matter your uptime. - Support costs and silent churn eat away at growth. - Your product team's roadmap is derailed by constant firefighting. With it: - Users understand what happened and what to do next. - Trust is built, even when things go wrong. - Engineers get to build new features, not just apologize for old bugs. Same systems, different empathy. Most startups think reliability is a backend problem. The winners know it's a core feature of the user interface. Build for the human, not just the machine. Is your error handling building trust or burning it down?

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.

This morning, many people opened their favorite apps and nothing worked. A technical issue in Amazon’s data center rippled across the digital world, disrupting thousands of companies & millions of lives in real time. Here’s how big the impact was: Lyft riders were stranded. Snapchat wouldn’t load. Venmo couldn’t send or receive payments. Ring cameras went dark. Prime Video, Hulu, and Disney+ froze midstream. Fortnite, Roblox, Clash Royale, and Clash of Clans kicked players offline. Signal messages failed to deliver. Even Amazon’s own site, Alexa, and Prime Video stopped responding. For a few hours, entertainment stopped, payments froze, communication failed, and digital life itself hit pause. But I see something more.⁣ This wasn’t just a technology failure; it was an emotional one. Because experiences aren’t based on the outage itself. They’re defined by what happens in between; how people feel while it’s broken, and how they’re treated while they wait.⁣ As a business leader, I bet you want to retain loyal customers when unexpected challenges happen. So, here's what you do: 1️⃣ Acknowledge emotions quickly. Silence multiplies frustration. Even a short, human message, “We know this is frustrating, and we’re on it” restores calm faster than a generic tech update. 2️⃣ Communicate with clarity and care. Customers don’t need technical terms; they want reassurance. Say what it means for them: “We’re working to reconnect you, and your data is safe.” 3️⃣ Close the loop with gratitude and honesty. When systems recover, let customers know. Thank them for their patience, acknowledge the inconvenience, and share what’s been done. Transparency rebuilds confidence; appreciation restores connection. 4️⃣ Empower your people, especially your frontline teams. Technology can fix systems, but only people can fix feelings. Give your employees permission, training, and trust to respond with empathy. Top rated brands know technology may fail, but feelings don’t have to. Because what customers remember isn’t the outage; it’s how you made them feel when it happened.⁣ Got questions? Message me, and follow for more actionable proven strategies. Doing CX Right®‬ #customerexperience #customerservice #awsoutage