How poor architecture impacts customer trust

Your customers can feel your internal chaos. When your systems are fragmented, the first people to notice are your customers. They receive inconsistent information, suffer from service delays, and get asked for the same details multiple times. In my experience, this is one of the fastest ways to lose trust and business. The truth is, poor data quality leads to unreliable insights and hesitant decisions internally, but it creates a terrible experience externally. And the stakes are high. 84% of customers will switch to a competitor after just one poor experience. When it costs 3.5 times more to acquire a new customer than to keep an existing one, you can't afford to get this wrong. This isn't an IT problem; it's a strategic business problem. Getting the architecture right with scalable systems and ensuring clean, consistent data isn't just about efficiency; it's about survival. We explore this connection between internal systems and customer loyalty in our new guide. When was the last time a customer had to tell you something that your systems should have already known? #CustomerExperience #DataQuality #CX

The most expensive AI architectural bug? The one your customers find before you do. I’ve seen it wipe millions from annual revenue. And it’s usually preventable. Some AI failures shout. Others whisper until it’s too late. In architecture, the most dangerous are silent failures. They hide inside your pipelines, pass all your tests, and only surface when customers notice. They’re not caused by bad prompts or bad models. They happen when your architecture ships without the safety nets probabilistic systems need: ✅ No real-time evaluation loops ✅ No anomaly detection ✅ No rollback triggers I’ve seen it happen. A chatbot passed staging with flying colours. In production, a subtle API change broke entity recognition. It kept replying with plausible nonsense for 3 weeks before anyone noticed. By then, churn had spiked 18% and brand trust took months to rebuild. Architectural anatomy 🔹 Where it starts - Gaps in the evaluation & logging layer of your Enterprise AI System Architecture 🔹 Why it passes unnoticed -Monitoring checks uptime, not behaviour 🔹 Where it shows up - Customer behaviour changes, KPI anomalies, revenue trends 🔹 How to fix it: • Continuous evaluation pipelines (i.e. LangSmith, Arize AI) • Automated regression tests • Anomaly scoring with alert thresholds (i.e. Evidently AI, custom monitors) • Rollback workflows tied to detection events (i.e. canary deploys with auto-revert) Quick Self-Diagnosis (2 minutes) Pick your highest-impact AI use case in production. Ask: How do we know if it’s giving wrong but plausible outputs today? If the answer involves waiting for customer feedback, you’re already exposed. Are you at risk? • No automated output evaluation after deployment • No anomaly alerts feeding into escalation • No rollback trigger connected to detection events If you tick even one, silent failures are only a matter of time. Why it matters 📊 Avg detection time without eval loops: 2–6 weeks 📊 Delayed fixes cost 5–10x more 📊 Brand recovery after trust loss: 6–18 months 💰 At 10k transactions/day, a 2% silent failure rate could leak $X/month Role callouts 🛠 AI Architects - Verify the eval & logging layer tracks behaviour, not just infra metrics 📋 AI Delivery Leads - Tie rollback triggers to behaviour changes ⚖ Compliance - Route anomaly alerts into risk gates, not just dashboards Silent failures don’t just erode performance. They erode trust. And trust is the hardest thing to rebuild. Where in your AI stack would you install your first detection loop? ➕ Follow me (https://lnkd.in/g3F_QTQb) I post daily about the hidden shifts in enterprise AI and careers. 

Enterprise Architecture in Retail: The £4M Black Friday Lesson Retail doesn’t forgive drift - it punishes it at the till.   Retail leaders never ask: “Which integration pattern did we use?”
 They ask: “Why did checkout fail during peak weekend - costing us millions?”   That’s the uncomfortable truth: when EA drifts, it doesn’t just create “IT headaches”. It quietly erodes competitiveness, drains budgets, and puts revenue at direct risk.   The problem is real. And here’s how it looks inside one major retailer:   Key Challenges 1 No unified target architecture - Each digital initiative makes its own choices. We now run three order-management systems and two loyalty platforms, with no clear roadmap to consolidation. 2 Integration bottlenecks - Point-to-point integrations dominate. A simple change like “ship-to-store” requires custom work across POS, OMS, and inventory systems, typically taking 4–6 months. This directly delays omnichannel rollouts. 3 Architecture drift & technical debt - Quick fixes from the last 5 years (POS patches, e-commerce add-ons, ad-hoc middleware) have created fragility. Last quarter, a promotion engine update crashed checkout in 30% of stores because of undocumented dependencies. 4 Vendor and platform sprawl - Business units independently contract SaaS tools (marketing cloud, workforce scheduling, CRM). This duplicate spend and traps us in vendor lock-in. In FY24, over 12% of IT budget went into overlapping licences and integrations. 5 Weak business alignment - We still fund projects by function, not capability. The result: marketing gets a new system while supply chain bottlenecks remain unresolved. The architecture function does not yet anchor investment decisions around strategic retail capabilities.   Consequences 1.    Margin impact: Cost overruns and duplicated spend have added ~8–10% unplanned IT cost annually. 2.    Delayed competitiveness: Omnichannel features like buy-online-return-in-store or real-time inventory visibility lag behind competitors by 12–18 months. 3.    Eroded trust: Business leaders increasingly bypass EA review boards, treating them as slow or irrelevant - accelerating drift and duplication. 4.    Increased operational risk: Outages caused by integration fragility directly affect sales; last year’s POS outage during a holiday weekend cost an estimated £4M in lost revenue.   Closing Hook:
This is not an “IT hygiene” issue.
It’s a boardroom problem: revenue, competitiveness, and customer trust are all on the line.   Question to retail leaders:  Is your enterprise architecture enabling growth - or quietly costing you millions? This is a silent tax of technical debt. What’s your experience? Where do you see EA causing (or solving) the biggest headaches in retail? Share your story (or battle scars) in the comments. Let’s break the silence around enterprise architecture’s real impact. Transform Partner – Your Strategic Champion for Digital Transformation Image Source: AOTEA

You should never make this mistake in your retail spaces. 👇 I’ll tell you something most retailers don’t realize: Your fixtures decide not just how your store looks, but how long people stay. And here’s the mistake I see too often: Brands confuse “visual appeal” with “functional flow.” Let me explain. I’ve seen stores spend crores on marble flooring and imported lights…but cut corners on fixture planning. The result? → Dead corners. Shelves placed too high or too low make products invisible. That’s revenue sitting in the dark. → Bottleneck aisles. One badly placed gondola, and suddenly foot traffic drops by half. Customers don’t like squeezing through. They walk out faster. → Wrong materials. Fixtures that can’t take product weight start sagging. It doesn’t just look sloppy—it signals “cheap” to your customer. And perception is everything. Lack of adaptability. Markets shift fast. If your fixtures aren’t modular, you can’t update displays. The store ages before the lease does. Here’s the truth: customers don’t notice good fixtures. But they always notice bad ones. They cut their visit short. They avoid that section. They don’t come back. So if you’re building retail spaces, don’t make this mistake. Don’t treat fixtures as an expense. Treat them as silent salespeople. Because every bent shelf, awkward corner, or crowded aisle isn’t just a design flaw, it’s lost footfall, lost trust, and lost revenue.

𝐖𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐭𝐡𝐞 𝐚𝐩𝐩 𝐬𝐥𝐨𝐰𝐬 𝐝𝐨𝐰𝐧 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐧𝐞𝐞𝐝 𝐢𝐭 𝐦𝐨𝐬𝐭? That’s exactly what one retailer was facing—frustrated customers lost sales, and a ticking time bomb of poor app performance. 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐜𝐚𝐮𝐬𝐞𝐝 𝐭𝐡𝐞 𝐬𝐥𝐨𝐰𝐝𝐨𝐰𝐧, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐢𝐝 𝐰𝐞 𝐟𝐢𝐱 𝐢𝐭? A leading retailer came to us with a problem they couldn’t ignore: their OutSystems app, once a customer favorite, had become sluggish during peak shopping hours. Long load times, crashing pages, and an overwhelming number of complaints were starting to damage their reputation and bottom line. They knew the app had potential, but something was holding it back. 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐮𝐥𝐩𝐫𝐢𝐭: 𝐏𝐨𝐨𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 Our deep dive revealed the root cause—an outdated app architecture that couldn’t handle the growing customer demands and traffic spikes. It wasn’t designed for scalability, and the cracks were beginning to show. 𝐓𝐡𝐞 𝐅𝐢𝐱: 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐒𝐭𝐫𝐨𝐧𝐠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 Here’s how we turned things around 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩: ▪️ 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: We cleaned up the messy data flow, reorganized databases, and optimized queries for lightning-fast access. ▪️ 𝐒𝐦𝐚𝐫𝐭 𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐋𝐚𝐳𝐲 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: By loading only the data users needed at the moment, we slashed load times and made interactions seamless. ▪️ 𝐑𝐞𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐊𝐞𝐲 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬: Critical modules were redesigned to handle heavy traffic without breaking a sweat. 𝐓𝐡𝐞 𝐏𝐨𝐬𝐢𝐭𝐢𝐯𝐞 𝐒𝐡𝐢𝐟𝐭: The results spoke for themselves: 🔸 46% 𝐟𝐚𝐬𝐭𝐞𝐫 𝐥𝐨𝐚𝐝 𝐭𝐢𝐦𝐞𝐬 even during peak hours. 🔸 𝐇𝐚𝐩𝐩𝐢𝐞𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 with smoother, faster experiences and fewer complaints. 🔸 𝐁𝐨𝐨𝐬𝐭𝐞𝐝 𝐬𝐚𝐥𝐞𝐬, thanks to a user-friendly app that made shopping easy and frustration-free. Your app’s architecture is more than just code—it’s the backbone of your customer experience. This retailer’s success story is proof that fixing the foundation can transform performance and customer satisfaction. Has your OutSystems app hit a performance roadblock? Let’s talk about how a stronger architecture can drive better results for your business. Anuj Upadhyay ⭕ #OutSystems #AppPerformance #RetailSuccess #TechTransformation #ScalableArchitecture #CustomerExperience