Is Your AI Exposing the Broken Process You Were Hoping It Would Fix? \u2013 NervNow<\/title>\n<link rel=\"preconnect\" href=\"https:\/\/fonts.googleapis.com\" \/>\n<link rel=\"preconnect\" href=\"https:\/\/fonts.gstatic.com\" crossorigin \/>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=Playfair+Display:ital,wght@0,700;0,800;1,700&family=Source+Serif+4:ital,wght@0,300;0,400;0,600;1,300;1,400&family=DM+Sans:wght@400;500;600&display=swap\" rel=\"stylesheet\" \/>\n<style>\n *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }\n\n :root {\n --black: #0d0d0d;\n --off-white: #faf9f7;\n --mid-gray: #6b6b6b;\n --light-gray: #e8e6e1;\n --accent-red: #182a4f;\n --accent-red-light: #e8edf5;\n --border: #d9d6cf;\n --pull-bg: #f4f2ee;\n --tag-bg: #111;\n --tag-text: #fff;\n --max-width: 780px;\n --wide-width: 1100px;\n }\n\n html { font-size: 18px; scroll-behavior: smooth; }\n\n body {\n background: var(--off-white);\n color: var(--black);\n font-family: 'Source Serif 4', Georgia, serif;\n font-weight: 400;\n line-height: 1.85;\n -webkit-font-smoothing: antialiased;\n }\n\n \/* \u2500\u2500 BREADCRUMB \u2500\u2500 *\/\n .breadcrumb {\n font-family: 'DM Sans', sans-serif;\n font-size: 0.72rem;\n font-weight: 500;\n letter-spacing: 0.08em;\n text-transform: uppercase;\n color: var(--mid-gray);\n padding: 20px 32px 20px;\n max-width: var(--wide-width);\n margin: 0 auto;\n }\n .breadcrumb a { color: var(--mid-gray); 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}\n\n \/* \u2500\u2500 ARTICLE FOOTER \u2500\u2500 *\/\n .article-footer {\n margin-top: 3rem;\n padding-top: 2rem;\n border-top: 1px solid var(--border);\n font-family: 'DM Sans', sans-serif;\n font-size: 0.82rem;\n color: var(--mid-gray);\n line-height: 1.65;\n }\n\n \/* \u2500\u2500 SIDEBAR \u2500\u2500 *\/\n .sidebar {\n padding-top: 0;\n position: static;\n max-width: 780px;\n margin: 0 auto;\n padding-bottom: 60px;\n }\n\n .sidebar-block {\n margin-bottom: 40px;\n }\n\n .sidebar-label {\n font-family: 'DM Sans', sans-serif;\n font-size: 0.65rem;\n font-weight: 700;\n letter-spacing: 0.16em;\n text-transform: uppercase;\n color: var(--mid-gray);\n margin-bottom: 14px;\n padding-bottom: 8px;\n border-bottom: 2px solid var(--black);\n }\n\n .sidebar-experts { display: grid; grid-template-columns: repeat(auto-fill, minmax(220px, 1fr)); gap: 16px; }\n\n .expert-card {\n display: flex;\n align-items: flex-start;\n gap: 12px;\n }\n .expert-avatar {\n width: 42px;\n height: 42px;\n border-radius: 50%;\n background: linear-gradient(135deg, #1a1a2e, #533483);\n display: flex;\n align-items: center;\n justify-content: center;\n font-family: 'DM Sans', sans-serif;\n font-size: 0.75rem;\n font-weight: 700;\n color: #fff;\n flex-shrink: 0;\n }\n .expert-info { flex: 1; 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gap: 0; }\n .sidebar { padding-top: 0; position: static; }\n\n }\n @media (max-width: 600px) {\n html { font-size: 16px; }\n .article-wrap { padding: 0 18px 60px; }\n .breadcrumb { padding: 14px 18px 0; }\n\n }\n<\/style>\n<\/head>\n<body>\n\n\n\n<div class=\"hero-header\">\n <div class=\"hero-header-inner\">\n <div class=\"article-series\">NervNow LinkedIn Live \u00b7 AI & Customer Experience<\/div>\n <h1 class=\"article-title\">Is Your AI Exposing the Broken Process You Were Hoping It Would Fix?<\/h1>\n <p class=\"article-excerpt\">Every dashboard shows green. Response times are down. Tickets are closing faster than ever. And yet, somewhere between the metric and the moment, the customer is losing faith. NervNow spoke to CXOs and senior leaders from across industries including Anubhav Mehrotra (Tata 1mg), Harmeet Kaur (The LaLiT Suri Hospitality Group), Dipu KV (Bajaj General Insurance), Rohit Manghnani (Ex Awfis Space Solutions), Rupali Krishna (QuantumHiveAI), Varun Kulkarni (NoBroker.com), and Varadharajan Raghunathan (Tata Group). Let us unwrap what they answered.<\/p>\n <hr class=\"hero-divider\"\/>\n <div class=\"article-meta\">\n <div class=\"meta-avatar\">NN<\/div>\n <div class=\"meta-info\">\n <span class=\"meta-name\">NervNow Editorial<\/span>\n <span class=\"meta-date\">April 2026 \u00b7 12 min read<\/span>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n<div class=\"breadcrumb\">\n <a href=\"https:\/\/nervnow.com\/ro\/\">Home<\/a>\n <span>\u203a<\/span>\n <a href=\"https:\/\/nervnow.com\/ro\/category\/analysis\/\">Analysis<\/a>\n <span>\u203a<\/span>\n <a href=\"https:\/\/nervnow.com\/ro\/category\/analysis\/analysis-ai-technology\/\">AI Technology<\/a>\n<\/div>\n\n\n<div class=\"article-wrap\">\n\n \n <main style=\"max-width: 780px; margin: 0 auto;\">\n\n \n <div class=\"article-body\">\n\n <p>There is a particular kind of executive confidence that emerges when the dashboard turns green. Response times are halved. Ticket volumes are down. Cost-per-interaction is at an all-time low. The quarterly business review looks immaculate, and the AI deployment is declared a success.<\/p>\n\n <p>Then the customer calls back.<\/p>\n\n <p>They call back because their problem was not solved. It was closed. They call back because the AI gave them a confident answer that turned out to be slightly different from what the human agent said an hour later. They call back because in healthcare, in insurance, and in financial services, hesitation is not a soft sentiment. It is a confirmation that trust has already left the building.<\/p>\n\n <p>The enterprise world is in the middle of a quiet crisis that its own measurement systems are designed to obscure. NervNow convened seven senior leaders from healthcare, hospitality, insurance, B2B workspace management, real estate, retail media, and MarTech to interrogate a single uncomfortable question: if AI is making everything faster, why does the customer experience keep getting worse?<\/p>\n\n <p>The answers, collectively, point not to the technology. They point to everything underneath it.<\/p>\n\n \n <h2>The Measurement Trap: When the Dashboard Lies to You<\/h2>\n\n <p>The first problem is that most organizations are measuring the wrong thing entirely, and they are measuring it well. Speed is seductive as a metric because it is clean, real-time, and universally understood. Leadership sees latency drop and concludes the AI is working. What the dashboard cannot show is whether the customer’s actual intent was resolved, or whether it was simply processed and filed.<\/p>\n\n <p>Anubhav Mehrotra, Vice President of Customer Experience at Tata 1mg, is precise about the gap. “Repeat contacts are a lag indicator,” he said. “By the time you see them rise, the customer has already experienced failure.” By the time the metric surfaces the problem, the damage is done.<\/p>\n\n <p>What Mehrotra argues for is a shift toward leading indicators, specifically what he calls “Unresolved Intent Rate at First Contact.” In healthcare especially, where patients do not experiment with uncertainty around medicine, dosage, or delivery, a failure of clarity in the first interaction does not produce a complaint. It produces abandonment, or worse, a patient making decisions on incomplete information.<\/p>\n\n <blockquote>“If the issue is ‘closed’ but the customer still hesitates or seeks validation, it is not resolved. For us, resolution means clarity, confidence, and action.” \u2014 Anubhav Mehrotra, Tata 1mg<\/blockquote>\n\n <p>The resolution metric his team now tracks goes well beyond ticket closure. They measure whether the customer actually completes the next step: places the order, accepts the suggestion, proceeds with a refill without rechecking or escalating. This is a fundamentally different definition of success than what most AI deployments are built to optimize for.<\/p>\n\n <div class=\"pull-quote\">\n <p>“Repeat contacts are a lag indicator. By the time you see them rise, the customer has already experienced failure.”<\/p>\n <cite>Anubhav Mehrotra, Vice President of Customer Experience, Tata 1mg<\/cite>\n <\/div>\n\n <p>Rohit Manghnani, consultant and former leader at Awfis Space Solutions, traces the root of this misalignment to the objectives set for automated agents from the start. When an AI system is instructed, explicitly or implicitly, to reduce cost and minimize handling time, it behaves rationally within those constraints. “If you optimize for cost, the AI agent, text or voice, will try to close the ticket fast,” Manghnani said. “You need to optimize for CSAT and NPS. Very different objectives. And strong guardrails on what the agent can promise have to be defined.”<\/p>\n\n <div class=\"pull-quote\">\n <p>“If you optimize for cost, the AI agent will try to close the ticket fast. You need to optimize for CSAT and NPS. Very different objectives.”<\/p>\n <cite>Rohit Manghnani, Consultant, Ex-Awfis Space Solutions<\/cite>\n <\/div>\n\n \n <h2>The Force Multiplier Problem: AI Scales Whatever It Sits On Top Of<\/h2>\n\n <p>The second failure pattern is subtler and more dangerous, because it masquerades as a technology problem when it is actually a process problem. When an organization deploys AI on top of a fragmented data architecture, broken workflows, or inconsistent internal processes, the AI does not compensate for those gaps. It accelerates delivery of the consequences. Bad decisions arrive faster. Inconsistencies are served at scale with confidence. The system fails more visibly, and at far greater volume, than any human team could manage on its own.<\/p>\n\n <p>Mehrotra describes the moment his team paused and recalibrated. AI improved response speed but not resolution quality. Ticket volumes dropped marginally, but repeat contacts went up. The response was not to fine-tune the model. It was to step back entirely, map the top 20 customer journeys end to end, identify failure nodes, fix the process, and only then rebuild AI on top of it. “AI sits atop clean workflows now. It augments decision-making. It does not mask inefficiencies.” The metric that mattered, he says, was not response time. It was whether the patient got the right resolution, on time, without calling twice.<\/p>\n\n <p>Harmeet Kaur, GM and Head of Corporate Marketing, Loyalty and Customer Experience at The LaLiT Suri Hospitality Group, observed the same phenomenon from the guest experience side. In hospitality, the gap between a fast response and a meaningful experience is immediately felt by the guest. “We realised something needed attention when AI improved response speed, but the overall guest experience did not see a similar uplift,” Kaur said. “While responses became faster, the journey still felt fragmented and not fully solution-oriented at times.” The fix was not more AI. It was stepping back, integrating systems more thoughtfully, aligning data, and strengthening the end-to-end journey before scaling further.<\/p>\n\n <blockquote>“AI may have been amplifying existing gaps rather than adding real value. We had to fix the journey before we scaled the tool.” \u2014 Harmeet Kaur, The LaLiT Suri Hospitality Group<\/blockquote>\n\n <div class=\"pull-quote\">\n <p>“While responses became faster, the journey still felt fragmented. AI was amplifying existing gaps rather than adding real value.”<\/p>\n <cite>Harmeet Kaur, GM, Corporate Marketing, Loyalty and Customer Experience, The LaLiT Suri Hospitality Group<\/cite>\n <\/div>\n\n <p>Rupali Krishna, President of Digital Technology, MarTech and Analytics (APAC and EMEA) at QuantumHiveAI, describes an AI-led lead scoring deployment for a pharmaceutical client that looked exceptional on paper: faster qualification, smarter routing, better prioritization. Within two weeks, the sales team had stopped trusting the system entirely. The reason was not the model. It was the data underneath it, duplicate leads, sparse fields, and inconsistent tagging in a CRM that had never been properly maintained. When AI was layered on top, it did not fix those problems. It industrialized them.<\/p>\n\n <p>The moment of reckoning came when a medical representative told Krishna: “Your AI is ignoring my best deals and pushing junk.” Her conclusion was unambiguous. “AI does not fix broken systems. It exposes them brutally. If your AI is not trusted by frontline teams, it is not intelligence. It is noise.”<\/p>\n\n <div class=\"pull-quote\">\n <p>“AI does not fix broken systems. It exposes them brutally. If your AI is not trusted by frontline teams, it is not intelligence. It is noise.”<\/p>\n <cite>Rupali Krishna, President, Digital Technology, MarTech and Analytics (APAC & EMEA), QuantumHiveAI<\/cite>\n <\/div>\n\n <p>Varadharajan Raghunathan, Head of Retail Media and CX at Tata Group, puts a different frame on the same failure. Organizations discover the problem when customers start raising new complaints instead of the original ones. Using AI without contextualizing the deployment, he notes, is like fishing with a better net but without a plan. More reach, but less purpose.<\/p>\n\n <div class=\"pull-quote\">\n <p>“Using AI without contextualising the deployment is like fishing with a better net but without a plan. More reach, but less purpose.”<\/p>\n <cite>Varadharajan Raghunathan, Head of Retail Media and CX, Tata Group<\/cite>\n <\/div>\n\n <p>Varun Kulkarni, Senior Director of Marketing, Digital and Growth at NoBroker.com, adds a practical warning for any organization currently in the grip of AI enthusiasm: most professionals fall into the trap of overcomplicating existing processes simply because they want to force-fit AI into them. The more useful discipline is separating problems that existing AI-enabled tools already solve from problems that genuinely need deeper AI integration at the core technology level. Identifying that distinction alone, he argues, resolves a significant amount of organizational friction before it starts.<\/p>\n\n \n <div class=\"do-not-miss\">\n <div class=\"dnm-label\">Do Not Miss<\/div>\n <a href=\"https:\/\/nervnow.com\/ro\/how-do-indian-cxos-actually-build-a-culture-around-ai\/\">How Do Indian CXOs Actually Build a Culture Around AI?<\/a>\n <\/div>\n\n \n <h2>The Ownership Void: When Everyone Owns the Journey, Nobody Does<\/h2>\n\n <p>The third failure point is organizational rather than technical, and it may be the hardest to fix because it requires confronting how power and accountability are actually structured inside large companies. AI-driven customer journeys span marketing, sales, service, and operations simultaneously. They cut across the org chart horizontally, in a world where accountability is still structured vertically. The result is that no single person has both the visibility and the authority to govern the full experience, and the AI, restricted to whatever data its departmental owner can access, makes decisions without the full picture.<\/p>\n\n <p>Dipu KV, Senior President at Bajaj General Insurance, is direct about where accountability must land. “The accountability ultimately rests with the person in charge of the process in which the bot is deployed,” he said. “Even with cross-functional teamwork, process owners need to be clearly identified.” When asked what happens when experience breaks across a cross-functional AI system, KV’s answer cuts through the usual corporate hedging: “If everyone owns it, no one owns it. We have anchored it with a CX leader, but success is collective. AI does not respect org charts. Alignment matters more than authority.”<\/p>\n\n <p>Manghnani frames the technical consequence of fragmented ownership in concrete terms. In workspace and real estate management, an AI that can see browsing history but not credit eligibility, or utilization data but not maintenance schedules, is not making intelligent decisions. It is making fast, confident, wrong ones. “If Marketing owns the AI but cannot see the ERP or Credit Engine data, the journey breaks,” he said. “The ultimate owner is typically the Chief Product Officer or a dedicated Digital Transformation lead who views the customer journey as a continuous loop rather than a linear funnel.”<\/p>\n\n <blockquote>“If everyone owns it, no one owns it. We have anchored it with a CX leader, but success is collective. AI does not respect org charts. Alignment matters more than authority.” \u2014 Dipu KV, Bajaj General Insurance<\/blockquote>\n\n <div class=\"pull-quote\">\n <p>“If everyone owns it, no one owns it. AI does not respect org charts. Alignment matters more than authority.”<\/p>\n <cite>Dipu KV, Senior President, Bajaj General Insurance<\/cite>\n <\/div>\n\n <p>Mehrotra describes the governance model built at Tata 1mg around this challenge: a Unified CX Charter, where one team tracks the end-to-end journey across marketing, ordering, fulfillment, and service. “AI becomes the connective tissue, not the owner,” he said. “If ownership is fragmented, AI will optimize parts but degrade the whole experience. Single-threaded accountability is non-negotiable.”<\/p>\n\n <p>Krishna takes this further, arguing that what organizations need is not a reshuffled org chart but an entirely new function, what she calls a Journey Orchestrator. Not marketing, not sales, not service, but a horizontal layer that owns customer lifecycle metrics, governs AI decision engines, and aligns incentives across functions. She has seen this sit within a Digital or Transformation Office, with unified journey dashboards, AI-driven triggers across touchpoints, and shared accountability for outcomes. “The biggest shift was not technology,” Krishna said. “When marketing, sales, and service started getting measured on the same metrics, behavior changed overnight.”<\/p>\n\n <p>Krishna also draws a distinction that surfaces in legacy organizations everywhere: the difference between “but” and “and.” The siloed manager says “my team” and “your team,” operating with a conjunction that separates. The shift to AI requires the other conjunction, one that connects. AI does not operate well in silos. It needs continuous data, unified context, and orchestration. Organizations still using the language of separation will find that their AI reflects exactly that.<\/p>\n\n \n <h2>The Data Infrastructure Reckoning: Plumbing Is Not Optional<\/h2>\n\n <p>Underlying all three failure patterns, measurement misalignment, process fragility, and ownership diffusion, is a more fundamental problem that most organizations are still reluctant to address directly: the data infrastructure was never built for what AI actually requires.<\/p>\n\n <p>Raghunathan frames the starting point clearly. Organizations need to redefine their data to suit AI, not force-fit what they already have into it. A well-thought-out ETL strategy with a clear vision on cadence and return on investment is not a technical afterthought. It is the prerequisite for everything else.<\/p>\n\n <p>Manghnani describes what he calls the “Innovation-to-Maintenance Ratio” as the clearest marker that incremental patching has hit its ceiling. When 80 percent of engineering budget and time is spent building custom connectors for legacy ERPs or cleaning data for a single AI prompt, the architecture is no longer enabling innovation. It is consuming it. The secondary marker is equally concrete. “If your AI is making decisions on data that is even 24 hours old in a fast-moving market, the system is no longer an asset,” he said. “It is a liability.”<\/p>\n\n <p>KV offers the most memorable framing of the data investment argument. “I call it plumbing, not luxury,” he said. “You do not debate ROI on clean water. Without unified data, AI becomes an expensive guessing engine.” Mehrotra positions the same argument as risk mitigation and value creation simultaneously. In healthcare, bad data does not just reduce efficiency. It breaks patient trust. Kaur makes the logic inescapable in its simplicity: “If the data is not right, the AI will not be right. And if AI is wrong at scale, the cost to the business becomes significantly higher.”<\/p>\n\n <p>Kulkarni offers a practical path for organizations that cannot justify a full infrastructure overhaul upfront: pilot with discipline. Pick a department, run a small-scale exercise, prove the value, and then expand. This approach, he argues, addresses stakeholder approval and solution identification at the same time, without requiring a board-level commitment before a single result is in hand.<\/p>\n\n <div class=\"pull-quote\">\n <p>“This is not something that is good to have. It is a must-have. Pick a department, prove the value, and then expand. That approach solves for both stakeholder approval and identifying the right solution.”<\/p>\n <cite>Varun Kulkarni, Senior Director, Marketing, Digital and Growth, NoBroker.com<\/cite>\n <\/div>\n\n <p>Krishna adds an architectural dimension that is often underappreciated. The modern MarTech transformation is not about adding tools. It is about removing redundancy and adding orchestration. In one recent transformation, she reduced 14 tools to 6 core layers: a unified data layer, a decision engine layer powered by AI and agentic orchestration, an activation layer for channels and journeys, and a feedback loop for continuous learning. The result was simpler workflows, goal-focused teams, faster decisions, lower cost, and higher personalization accuracy. The lesson, she says, is that the competitive advantage is not the biggest MarTech stack. It is the cleanest, most connected decisioning system.<\/p>\n\n \n <h2>The Trust Threshold: In Some Industries, You Only Get One Chance<\/h2>\n\n <p>In healthcare and financial services, trust is not a brand metric. It is a clinical and regulatory requirement. The margin for error is effectively zero, and the consequences of inconsistency are not a poor NPS score. They are a patient making a wrong decision, a claim being disputed, and a relationship that cannot be recovered.<\/p>\n\n <p>Mehrotra describes precisely how trust erodes in healthcare AI deployments, and it rarely looks like a catastrophic failure. It looks like hesitation. “Trust in healthcare does not break with complaints,” he said. “It breaks with hesitation and ambiguity, when customers stop acting, start double-checking, or seek human reassurance.” At Tata 1mg, the failure mode that triggered a structural response was when AI and human channels gave slightly different information, each correct in isolation, but together they eroded confidence and multiplied confusion. The fix was enforcing a single, governed source of truth with strict confidence thresholds across every channel.<\/p>\n\n <p>Mehrotra also draws the clearest line when it comes to personalization in healthcare. A refill reminder for a diabetic patient who is three days from running out is genuinely helpful. A targeted push on a sensitive therapy someone searched for once is a boundary violation, regardless of what the algorithm calculates as relevant. “Customers should feel supported, not monitored,” he said.<\/p>\n\n <blockquote>“Trust in healthcare does not break with complaints. It breaks with hesitation and ambiguity, when customers stop acting, start double-checking, or seek human reassurance.” \u2014 Anubhav Mehrotra, Tata 1mg<\/blockquote>\n\n <p>KV, operating in insurance where trust and accuracy carry equal weight, describes the operating principle as moving fast without sacrificing the rigor that prevents trust erosion. The two are not in conflict, but they demand a discipline that many organizations abandon the moment deployment pressure builds.<\/p>\n\n <p>The personalization question cuts across industries. Raghunathan offers a practical framework for navigating it in advertising and targeting: keep messaging cohort-based, but keep targeting personalized. The user feels addressed as part of a relevant group, not singled out as a subject of surveillance.<\/p>\n\n <p>Kulkarni reinforces this from the consumer platform side. Customers are aware that data is being shared when they use a platform, and they are broadly comfortable with it, right up until the moment a brand uses something that has nothing to do with its core journey. That is when trust breaks, and it rarely announces itself before the damage is done.<\/p>\n\n <p>Kaur brings the hospitality dimension, where the stakes around over-automation are immediate and visceral. Guests value being understood and cared for, and that experience is irreducibly human. AI’s role is to empower the team members who create it, not to replace the judgment, empathy, and in-the-moment reading of a guest’s emotional state that no model currently replicates. “It is finally the Guest Services team that makes the impact,” Kaur said. “Which must be kept natural and coming from the heart.”<\/p>\n\n \n <h2>What This Adds Up To<\/h2>\n\n <p>Across seven industries and seven executives, the diagnosis is consistent enough to be treated as structural rather than situational. AI does not create trust. It reveals the trust, or the absence of it, that already existed in the underlying process. The organizations genuinely succeeding with AI-driven customer experience share a sequence that looks nothing like the “deploy and optimize” playbook. They fixed the process first. They unified the data second. They resolved ownership third. They deployed AI on top of a foundation that could actually support it.<\/p>\n\n <p>The organizations that skipped steps are now discovering that their carefully constructed dashboards are measuring the wrong things, their frontline teams are bypassing systems to protect customers, and their most sophisticated AI deployments are, as KV puts it, expensive guessing engines running, in Manghnani’s words, on contaminated fuel.<\/p>\n\n <p>The hardest truth for any CXO is this: if your teams are manually working around your AI to fix outcomes, the infrastructure is already broken. You cannot repair a loss of customer confidence with a faster response time. You can only rebuild it with a better resolution, and that work starts well below the model layer. The infrastructure question is not a technology decision. It is a leadership one.<\/p>\n\n \n <div class=\"read-more\">\n <div class=\"rm-label\">More Deep Dives<\/div>\n <ul>\n <li><a href=\"#\">Are AI Chatbots Actually Improving Customer Experience, or Just Cutting Costs?<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/ai-runs-on-power-what-india-uae-can-teach-each-other-about-the-green-intelligence-economy\/\">AI Runs on Power: What India, UAE Can Teach Each Other About the Green-Intelligence Economy<\/a><\/li>\n <li><a href=\"#\">The Price of Inaction: How AI is Turning Climate Risk Into a Capital Decision<\/a><\/li>\n <li><a href=\"#\">Why is AI Governance Lagging Behind AI Adoption?<\/a><\/li>\n <li><a href=\"#\">Is AI Healthcare Really Reaching Tier-2 India?<\/a><\/li>\n <\/ul>\n <\/div>\n\n \n <div class=\"article-footer\">\n <p>This article is based on a LinkedIn Live session conducted by NervNow. All responses are sourced from the live session and subsequent written contributions by participants, and have been lightly edited for clarity and length. The views expressed are personal to each expert and do not represent the positions of their respective employers or NervNow.<\/p>\n <\/div>\n\n <\/div>\n <\/main>\n\n \n <aside class=\"sidebar\" style=\"border-top: 2px solid #182a4f; padding-top: 40px; margin-top: 20px;\">\n\n <div class=\"sidebar-block\">\n <div class=\"sidebar-label\">Experts in This Story<\/div>\n <div class=\"sidebar-experts\">\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">AM<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Anubhav Mehrotra<\/div>\n <div class=\"expert-role\">VP, Customer Experience \u00b7 Tata 1mg<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">HK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Harmeet Kaur<\/div>\n <div class=\"expert-role\">GM, Marketing, Loyalty & CX \u00b7 The LaLiT Suri Hospitality Group<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">DK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Dipu KV<\/div>\n <div class=\"expert-role\">Senior President \u00b7 Bajaj General Insurance<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">RM<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Rohit Manghnani<\/div>\n <div class=\"expert-role\">Consultant \u00b7 Ex-Awfis Space Solutions<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">RK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Rupali Krishna<\/div>\n <div class=\"expert-role\">President, Digital Technology, MarTech & Analytics \u00b7 QuantumHiveAI<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">VK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Varun Kulkarni<\/div>\n <div class=\"expert-role\">Sr. Director, Marketing, Digital & Growth \u00b7 NoBroker.com<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">VR<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Varadharajan Raghunathan<\/div>\n <div class=\"expert-role\">Head, Retail Media & CX \u00b7 Tata Group<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"sidebar-block\">\n <div class=\"sidebar-label\">Topics<\/div>\n <div class=\"sidebar-tags\">\n <span class=\"sidebar-tag\">AI<\/span>\n <span class=\"sidebar-tag\">Customer Experience<\/span>\n <span class=\"sidebar-tag\">CXO<\/span>\n <span class=\"sidebar-tag\">Data Strategy<\/span>\n <span class=\"sidebar-tag\">MarTech<\/span>\n <span class=\"sidebar-tag\">Digital Transformation<\/span>\n <span class=\"sidebar-tag\">Healthcare AI<\/span>\n <span class=\"sidebar-tag\">Hospitality<\/span>\n <span class=\"sidebar-tag\">Insurance<\/span>\n <span class=\"sidebar-tag\">B2B<\/span>\n <\/div>\n <\/div>\n\n <\/aside>\n\n<\/div>\n\n\n<footer class=\"site-footer\">\n <p>\u00a9 2026 <strong>NervNow\u2122<\/strong> \u00b7 All rights reserved \u00b7 <a href=\"https:\/\/nervnow.com\/ro\/privacy-policy\/\">Privacy Policy<\/a><\/p>\n<\/footer>\n\n<\/body>\n<\/html>","protected":false},"excerpt":{"rendered":"<p>Seven senior leaders from healthcare, hospitality, insurance, real estate, B2B workspace, retail media, and MarTech sat with NervNow to answer one uncomfortable question: if AI is making everything faster, why is customer confidence going the other direction?<\/p>","protected":false},"author":11,"featured_media":6487,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[95,107],"tags":[183],"class_list":["post-6486","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis","category-analysis-marketing-advertising","tag-analysis"],"blocksy_meta":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6486","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/comments?post=6486"}],"version-history":[{"count":4,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6486\/revisions"}],"predecessor-version":[{"id":6492,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6486\/revisions\/6492"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/media\/6487"}],"wp:attachment":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/media?parent=6486"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/categories?post=6486"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/tags?post=6486"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

{"id":6486,"date":"2026-04-22T13:39:48","date_gmt":"2026-04-22T08:09:48","guid":{"rendered":"https:\/\/nervnow.com\/?p=6486"},"modified":"2026-04-22T13:51:36","modified_gmt":"2026-04-22T08:21:36","slug":"why-are-cxos-spending-more-on-ai-and-losing-customer-confidence-cxos-answer","status":"publish","type":"post","link":"https:\/\/nervnow.com\/ro\/why-are-cxos-spending-more-on-ai-and-losing-customer-confidence-cxos-answer\/","title":{"rendered":"Why Are CXOs Spending More on AI and Losing Customer Confidence? CXOs Answer"},"content":{"rendered":"\n\n\n\n\nIs Your AI Exposing the Broken Process You Were Hoping It Would Fix? \u2013 NervNow<\/title>\n<link rel=\"preconnect\" href=\"https:\/\/fonts.googleapis.com\" \/>\n<link rel=\"preconnect\" href=\"https:\/\/fonts.gstatic.com\" crossorigin \/>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=Playfair+Display:ital,wght@0,700;0,800;1,700&family=Source+Serif+4:ital,wght@0,300;0,400;0,600;1,300;1,400&family=DM+Sans:wght@400;500;600&display=swap\" rel=\"stylesheet\" \/>\n<style>\n *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }\n\n :root {\n --black: #0d0d0d;\n --off-white: #faf9f7;\n --mid-gray: #6b6b6b;\n --light-gray: #e8e6e1;\n --accent-red: #182a4f;\n --accent-red-light: #e8edf5;\n --border: #d9d6cf;\n --pull-bg: #f4f2ee;\n --tag-bg: #111;\n --tag-text: #fff;\n --max-width: 780px;\n --wide-width: 1100px;\n }\n\n html { font-size: 18px; scroll-behavior: smooth; }\n\n body {\n background: var(--off-white);\n color: var(--black);\n font-family: 'Source Serif 4', Georgia, serif;\n font-weight: 400;\n line-height: 1.85;\n -webkit-font-smoothing: antialiased;\n }\n\n \/* \u2500\u2500 BREADCRUMB \u2500\u2500 *\/\n .breadcrumb {\n font-family: 'DM Sans', sans-serif;\n font-size: 0.72rem;\n font-weight: 500;\n letter-spacing: 0.08em;\n text-transform: uppercase;\n color: var(--mid-gray);\n padding: 20px 32px 20px;\n max-width: var(--wide-width);\n margin: 0 auto;\n }\n .breadcrumb a { color: var(--mid-gray); 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gap: 0; }\n .sidebar { padding-top: 0; position: static; }\n\n }\n @media (max-width: 600px) {\n html { font-size: 16px; }\n .article-wrap { padding: 0 18px 60px; }\n .breadcrumb { padding: 14px 18px 0; }\n\n }\n<\/style>\n<\/head>\n<body>\n\n\n\n<div class=\"hero-header\">\n <div class=\"hero-header-inner\">\n <div class=\"article-series\">NervNow LinkedIn Live \u00b7 AI & Customer Experience<\/div>\n <h1 class=\"article-title\">Is Your AI Exposing the Broken Process You Were Hoping It Would Fix?<\/h1>\n <p class=\"article-excerpt\">Every dashboard shows green. Response times are down. Tickets are closing faster than ever. And yet, somewhere between the metric and the moment, the customer is losing faith. NervNow spoke to CXOs and senior leaders from across industries including Anubhav Mehrotra (Tata 1mg), Harmeet Kaur (The LaLiT Suri Hospitality Group), Dipu KV (Bajaj General Insurance), Rohit Manghnani (Ex Awfis Space Solutions), Rupali Krishna (QuantumHiveAI), Varun Kulkarni (NoBroker.com), and Varadharajan Raghunathan (Tata Group). Let us unwrap what they answered.<\/p>\n <hr class=\"hero-divider\"\/>\n <div class=\"article-meta\">\n <div class=\"meta-avatar\">NN<\/div>\n <div class=\"meta-info\">\n <span class=\"meta-name\">NervNow Editorial<\/span>\n <span class=\"meta-date\">April 2026 \u00b7 12 min read<\/span>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n<div class=\"breadcrumb\">\n <a href=\"https:\/\/nervnow.com\/ro\/\">Home<\/a>\n <span>\u203a<\/span>\n <a href=\"https:\/\/nervnow.com\/ro\/category\/analysis\/\">Analysis<\/a>\n <span>\u203a<\/span>\n <a href=\"https:\/\/nervnow.com\/ro\/category\/analysis\/analysis-ai-technology\/\">AI Technology<\/a>\n<\/div>\n\n\n<div class=\"article-wrap\">\n\n \n <main style=\"max-width: 780px; margin: 0 auto;\">\n\n \n <div class=\"article-body\">\n\n <p>There is a particular kind of executive confidence that emerges when the dashboard turns green. Response times are halved. Ticket volumes are down. Cost-per-interaction is at an all-time low. The quarterly business review looks immaculate, and the AI deployment is declared a success.<\/p>\n\n <p>Then the customer calls back.<\/p>\n\n <p>They call back because their problem was not solved. It was closed. They call back because the AI gave them a confident answer that turned out to be slightly different from what the human agent said an hour later. They call back because in healthcare, in insurance, and in financial services, hesitation is not a soft sentiment. It is a confirmation that trust has already left the building.<\/p>\n\n <p>The enterprise world is in the middle of a quiet crisis that its own measurement systems are designed to obscure. NervNow convened seven senior leaders from healthcare, hospitality, insurance, B2B workspace management, real estate, retail media, and MarTech to interrogate a single uncomfortable question: if AI is making everything faster, why does the customer experience keep getting worse?<\/p>\n\n <p>The answers, collectively, point not to the technology. They point to everything underneath it.<\/p>\n\n \n <h2>The Measurement Trap: When the Dashboard Lies to You<\/h2>\n\n <p>The first problem is that most organizations are measuring the wrong thing entirely, and they are measuring it well. Speed is seductive as a metric because it is clean, real-time, and universally understood. Leadership sees latency drop and concludes the AI is working. What the dashboard cannot show is whether the customer’s actual intent was resolved, or whether it was simply processed and filed.<\/p>\n\n <p>Anubhav Mehrotra, Vice President of Customer Experience at Tata 1mg, is precise about the gap. “Repeat contacts are a lag indicator,” he said. “By the time you see them rise, the customer has already experienced failure.” By the time the metric surfaces the problem, the damage is done.<\/p>\n\n <p>What Mehrotra argues for is a shift toward leading indicators, specifically what he calls “Unresolved Intent Rate at First Contact.” In healthcare especially, where patients do not experiment with uncertainty around medicine, dosage, or delivery, a failure of clarity in the first interaction does not produce a complaint. It produces abandonment, or worse, a patient making decisions on incomplete information.<\/p>\n\n <blockquote>“If the issue is ‘closed’ but the customer still hesitates or seeks validation, it is not resolved. For us, resolution means clarity, confidence, and action.” \u2014 Anubhav Mehrotra, Tata 1mg<\/blockquote>\n\n <p>The resolution metric his team now tracks goes well beyond ticket closure. They measure whether the customer actually completes the next step: places the order, accepts the suggestion, proceeds with a refill without rechecking or escalating. This is a fundamentally different definition of success than what most AI deployments are built to optimize for.<\/p>\n\n <div class=\"pull-quote\">\n <p>“Repeat contacts are a lag indicator. By the time you see them rise, the customer has already experienced failure.”<\/p>\n <cite>Anubhav Mehrotra, Vice President of Customer Experience, Tata 1mg<\/cite>\n <\/div>\n\n <p>Rohit Manghnani, consultant and former leader at Awfis Space Solutions, traces the root of this misalignment to the objectives set for automated agents from the start. When an AI system is instructed, explicitly or implicitly, to reduce cost and minimize handling time, it behaves rationally within those constraints. “If you optimize for cost, the AI agent, text or voice, will try to close the ticket fast,” Manghnani said. “You need to optimize for CSAT and NPS. Very different objectives. And strong guardrails on what the agent can promise have to be defined.”<\/p>\n\n <div class=\"pull-quote\">\n <p>“If you optimize for cost, the AI agent will try to close the ticket fast. You need to optimize for CSAT and NPS. Very different objectives.”<\/p>\n <cite>Rohit Manghnani, Consultant, Ex-Awfis Space Solutions<\/cite>\n <\/div>\n\n \n <h2>The Force Multiplier Problem: AI Scales Whatever It Sits On Top Of<\/h2>\n\n <p>The second failure pattern is subtler and more dangerous, because it masquerades as a technology problem when it is actually a process problem. When an organization deploys AI on top of a fragmented data architecture, broken workflows, or inconsistent internal processes, the AI does not compensate for those gaps. It accelerates delivery of the consequences. Bad decisions arrive faster. Inconsistencies are served at scale with confidence. The system fails more visibly, and at far greater volume, than any human team could manage on its own.<\/p>\n\n <p>Mehrotra describes the moment his team paused and recalibrated. AI improved response speed but not resolution quality. Ticket volumes dropped marginally, but repeat contacts went up. The response was not to fine-tune the model. It was to step back entirely, map the top 20 customer journeys end to end, identify failure nodes, fix the process, and only then rebuild AI on top of it. “AI sits atop clean workflows now. It augments decision-making. It does not mask inefficiencies.” The metric that mattered, he says, was not response time. It was whether the patient got the right resolution, on time, without calling twice.<\/p>\n\n <p>Harmeet Kaur, GM and Head of Corporate Marketing, Loyalty and Customer Experience at The LaLiT Suri Hospitality Group, observed the same phenomenon from the guest experience side. In hospitality, the gap between a fast response and a meaningful experience is immediately felt by the guest. “We realised something needed attention when AI improved response speed, but the overall guest experience did not see a similar uplift,” Kaur said. “While responses became faster, the journey still felt fragmented and not fully solution-oriented at times.” The fix was not more AI. It was stepping back, integrating systems more thoughtfully, aligning data, and strengthening the end-to-end journey before scaling further.<\/p>\n\n <blockquote>“AI may have been amplifying existing gaps rather than adding real value. We had to fix the journey before we scaled the tool.” \u2014 Harmeet Kaur, The LaLiT Suri Hospitality Group<\/blockquote>\n\n <div class=\"pull-quote\">\n <p>“While responses became faster, the journey still felt fragmented. AI was amplifying existing gaps rather than adding real value.”<\/p>\n <cite>Harmeet Kaur, GM, Corporate Marketing, Loyalty and Customer Experience, The LaLiT Suri Hospitality Group<\/cite>\n <\/div>\n\n <p>Rupali Krishna, President of Digital Technology, MarTech and Analytics (APAC and EMEA) at QuantumHiveAI, describes an AI-led lead scoring deployment for a pharmaceutical client that looked exceptional on paper: faster qualification, smarter routing, better prioritization. Within two weeks, the sales team had stopped trusting the system entirely. The reason was not the model. It was the data underneath it, duplicate leads, sparse fields, and inconsistent tagging in a CRM that had never been properly maintained. When AI was layered on top, it did not fix those problems. It industrialized them.<\/p>\n\n <p>The moment of reckoning came when a medical representative told Krishna: “Your AI is ignoring my best deals and pushing junk.” Her conclusion was unambiguous. “AI does not fix broken systems. It exposes them brutally. If your AI is not trusted by frontline teams, it is not intelligence. It is noise.”<\/p>\n\n <div class=\"pull-quote\">\n <p>“AI does not fix broken systems. It exposes them brutally. If your AI is not trusted by frontline teams, it is not intelligence. It is noise.”<\/p>\n <cite>Rupali Krishna, President, Digital Technology, MarTech and Analytics (APAC & EMEA), QuantumHiveAI<\/cite>\n <\/div>\n\n <p>Varadharajan Raghunathan, Head of Retail Media and CX at Tata Group, puts a different frame on the same failure. Organizations discover the problem when customers start raising new complaints instead of the original ones. Using AI without contextualizing the deployment, he notes, is like fishing with a better net but without a plan. More reach, but less purpose.<\/p>\n\n <div class=\"pull-quote\">\n <p>“Using AI without contextualising the deployment is like fishing with a better net but without a plan. More reach, but less purpose.”<\/p>\n <cite>Varadharajan Raghunathan, Head of Retail Media and CX, Tata Group<\/cite>\n <\/div>\n\n <p>Varun Kulkarni, Senior Director of Marketing, Digital and Growth at NoBroker.com, adds a practical warning for any organization currently in the grip of AI enthusiasm: most professionals fall into the trap of overcomplicating existing processes simply because they want to force-fit AI into them. The more useful discipline is separating problems that existing AI-enabled tools already solve from problems that genuinely need deeper AI integration at the core technology level. Identifying that distinction alone, he argues, resolves a significant amount of organizational friction before it starts.<\/p>\n\n \n <div class=\"do-not-miss\">\n <div class=\"dnm-label\">Do Not Miss<\/div>\n <a href=\"https:\/\/nervnow.com\/ro\/how-do-indian-cxos-actually-build-a-culture-around-ai\/\">How Do Indian CXOs Actually Build a Culture Around AI?<\/a>\n <\/div>\n\n \n <h2>The Ownership Void: When Everyone Owns the Journey, Nobody Does<\/h2>\n\n <p>The third failure point is organizational rather than technical, and it may be the hardest to fix because it requires confronting how power and accountability are actually structured inside large companies. AI-driven customer journeys span marketing, sales, service, and operations simultaneously. They cut across the org chart horizontally, in a world where accountability is still structured vertically. The result is that no single person has both the visibility and the authority to govern the full experience, and the AI, restricted to whatever data its departmental owner can access, makes decisions without the full picture.<\/p>\n\n <p>Dipu KV, Senior President at Bajaj General Insurance, is direct about where accountability must land. “The accountability ultimately rests with the person in charge of the process in which the bot is deployed,” he said. “Even with cross-functional teamwork, process owners need to be clearly identified.” When asked what happens when experience breaks across a cross-functional AI system, KV’s answer cuts through the usual corporate hedging: “If everyone owns it, no one owns it. We have anchored it with a CX leader, but success is collective. AI does not respect org charts. Alignment matters more than authority.”<\/p>\n\n <p>Manghnani frames the technical consequence of fragmented ownership in concrete terms. In workspace and real estate management, an AI that can see browsing history but not credit eligibility, or utilization data but not maintenance schedules, is not making intelligent decisions. It is making fast, confident, wrong ones. “If Marketing owns the AI but cannot see the ERP or Credit Engine data, the journey breaks,” he said. “The ultimate owner is typically the Chief Product Officer or a dedicated Digital Transformation lead who views the customer journey as a continuous loop rather than a linear funnel.”<\/p>\n\n <blockquote>“If everyone owns it, no one owns it. We have anchored it with a CX leader, but success is collective. AI does not respect org charts. Alignment matters more than authority.” \u2014 Dipu KV, Bajaj General Insurance<\/blockquote>\n\n <div class=\"pull-quote\">\n <p>“If everyone owns it, no one owns it. AI does not respect org charts. Alignment matters more than authority.”<\/p>\n <cite>Dipu KV, Senior President, Bajaj General Insurance<\/cite>\n <\/div>\n\n <p>Mehrotra describes the governance model built at Tata 1mg around this challenge: a Unified CX Charter, where one team tracks the end-to-end journey across marketing, ordering, fulfillment, and service. “AI becomes the connective tissue, not the owner,” he said. “If ownership is fragmented, AI will optimize parts but degrade the whole experience. Single-threaded accountability is non-negotiable.”<\/p>\n\n <p>Krishna takes this further, arguing that what organizations need is not a reshuffled org chart but an entirely new function, what she calls a Journey Orchestrator. Not marketing, not sales, not service, but a horizontal layer that owns customer lifecycle metrics, governs AI decision engines, and aligns incentives across functions. She has seen this sit within a Digital or Transformation Office, with unified journey dashboards, AI-driven triggers across touchpoints, and shared accountability for outcomes. “The biggest shift was not technology,” Krishna said. “When marketing, sales, and service started getting measured on the same metrics, behavior changed overnight.”<\/p>\n\n <p>Krishna also draws a distinction that surfaces in legacy organizations everywhere: the difference between “but” and “and.” The siloed manager says “my team” and “your team,” operating with a conjunction that separates. The shift to AI requires the other conjunction, one that connects. AI does not operate well in silos. It needs continuous data, unified context, and orchestration. Organizations still using the language of separation will find that their AI reflects exactly that.<\/p>\n\n \n <h2>The Data Infrastructure Reckoning: Plumbing Is Not Optional<\/h2>\n\n <p>Underlying all three failure patterns, measurement misalignment, process fragility, and ownership diffusion, is a more fundamental problem that most organizations are still reluctant to address directly: the data infrastructure was never built for what AI actually requires.<\/p>\n\n <p>Raghunathan frames the starting point clearly. Organizations need to redefine their data to suit AI, not force-fit what they already have into it. A well-thought-out ETL strategy with a clear vision on cadence and return on investment is not a technical afterthought. It is the prerequisite for everything else.<\/p>\n\n <p>Manghnani describes what he calls the “Innovation-to-Maintenance Ratio” as the clearest marker that incremental patching has hit its ceiling. When 80 percent of engineering budget and time is spent building custom connectors for legacy ERPs or cleaning data for a single AI prompt, the architecture is no longer enabling innovation. It is consuming it. The secondary marker is equally concrete. “If your AI is making decisions on data that is even 24 hours old in a fast-moving market, the system is no longer an asset,” he said. “It is a liability.”<\/p>\n\n <p>KV offers the most memorable framing of the data investment argument. “I call it plumbing, not luxury,” he said. “You do not debate ROI on clean water. Without unified data, AI becomes an expensive guessing engine.” Mehrotra positions the same argument as risk mitigation and value creation simultaneously. In healthcare, bad data does not just reduce efficiency. It breaks patient trust. Kaur makes the logic inescapable in its simplicity: “If the data is not right, the AI will not be right. And if AI is wrong at scale, the cost to the business becomes significantly higher.”<\/p>\n\n <p>Kulkarni offers a practical path for organizations that cannot justify a full infrastructure overhaul upfront: pilot with discipline. Pick a department, run a small-scale exercise, prove the value, and then expand. This approach, he argues, addresses stakeholder approval and solution identification at the same time, without requiring a board-level commitment before a single result is in hand.<\/p>\n\n <div class=\"pull-quote\">\n <p>“This is not something that is good to have. It is a must-have. Pick a department, prove the value, and then expand. That approach solves for both stakeholder approval and identifying the right solution.”<\/p>\n <cite>Varun Kulkarni, Senior Director, Marketing, Digital and Growth, NoBroker.com<\/cite>\n <\/div>\n\n <p>Krishna adds an architectural dimension that is often underappreciated. The modern MarTech transformation is not about adding tools. It is about removing redundancy and adding orchestration. In one recent transformation, she reduced 14 tools to 6 core layers: a unified data layer, a decision engine layer powered by AI and agentic orchestration, an activation layer for channels and journeys, and a feedback loop for continuous learning. The result was simpler workflows, goal-focused teams, faster decisions, lower cost, and higher personalization accuracy. The lesson, she says, is that the competitive advantage is not the biggest MarTech stack. It is the cleanest, most connected decisioning system.<\/p>\n\n \n <h2>The Trust Threshold: In Some Industries, You Only Get One Chance<\/h2>\n\n <p>In healthcare and financial services, trust is not a brand metric. It is a clinical and regulatory requirement. The margin for error is effectively zero, and the consequences of inconsistency are not a poor NPS score. They are a patient making a wrong decision, a claim being disputed, and a relationship that cannot be recovered.<\/p>\n\n <p>Mehrotra describes precisely how trust erodes in healthcare AI deployments, and it rarely looks like a catastrophic failure. It looks like hesitation. “Trust in healthcare does not break with complaints,” he said. “It breaks with hesitation and ambiguity, when customers stop acting, start double-checking, or seek human reassurance.” At Tata 1mg, the failure mode that triggered a structural response was when AI and human channels gave slightly different information, each correct in isolation, but together they eroded confidence and multiplied confusion. The fix was enforcing a single, governed source of truth with strict confidence thresholds across every channel.<\/p>\n\n <p>Mehrotra also draws the clearest line when it comes to personalization in healthcare. A refill reminder for a diabetic patient who is three days from running out is genuinely helpful. A targeted push on a sensitive therapy someone searched for once is a boundary violation, regardless of what the algorithm calculates as relevant. “Customers should feel supported, not monitored,” he said.<\/p>\n\n <blockquote>“Trust in healthcare does not break with complaints. It breaks with hesitation and ambiguity, when customers stop acting, start double-checking, or seek human reassurance.” \u2014 Anubhav Mehrotra, Tata 1mg<\/blockquote>\n\n <p>KV, operating in insurance where trust and accuracy carry equal weight, describes the operating principle as moving fast without sacrificing the rigor that prevents trust erosion. The two are not in conflict, but they demand a discipline that many organizations abandon the moment deployment pressure builds.<\/p>\n\n <p>The personalization question cuts across industries. Raghunathan offers a practical framework for navigating it in advertising and targeting: keep messaging cohort-based, but keep targeting personalized. The user feels addressed as part of a relevant group, not singled out as a subject of surveillance.<\/p>\n\n <p>Kulkarni reinforces this from the consumer platform side. Customers are aware that data is being shared when they use a platform, and they are broadly comfortable with it, right up until the moment a brand uses something that has nothing to do with its core journey. That is when trust breaks, and it rarely announces itself before the damage is done.<\/p>\n\n <p>Kaur brings the hospitality dimension, where the stakes around over-automation are immediate and visceral. Guests value being understood and cared for, and that experience is irreducibly human. AI’s role is to empower the team members who create it, not to replace the judgment, empathy, and in-the-moment reading of a guest’s emotional state that no model currently replicates. “It is finally the Guest Services team that makes the impact,” Kaur said. “Which must be kept natural and coming from the heart.”<\/p>\n\n \n <h2>What This Adds Up To<\/h2>\n\n <p>Across seven industries and seven executives, the diagnosis is consistent enough to be treated as structural rather than situational. AI does not create trust. It reveals the trust, or the absence of it, that already existed in the underlying process. The organizations genuinely succeeding with AI-driven customer experience share a sequence that looks nothing like the “deploy and optimize” playbook. They fixed the process first. They unified the data second. They resolved ownership third. They deployed AI on top of a foundation that could actually support it.<\/p>\n\n <p>The organizations that skipped steps are now discovering that their carefully constructed dashboards are measuring the wrong things, their frontline teams are bypassing systems to protect customers, and their most sophisticated AI deployments are, as KV puts it, expensive guessing engines running, in Manghnani’s words, on contaminated fuel.<\/p>\n\n <p>The hardest truth for any CXO is this: if your teams are manually working around your AI to fix outcomes, the infrastructure is already broken. You cannot repair a loss of customer confidence with a faster response time. You can only rebuild it with a better resolution, and that work starts well below the model layer. The infrastructure question is not a technology decision. It is a leadership one.<\/p>\n\n \n <div class=\"read-more\">\n <div class=\"rm-label\">More Deep Dives<\/div>\n <ul>\n <li><a href=\"#\">Are AI Chatbots Actually Improving Customer Experience, or Just Cutting Costs?<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/ai-runs-on-power-what-india-uae-can-teach-each-other-about-the-green-intelligence-economy\/\">AI Runs on Power: What India, UAE Can Teach Each Other About the Green-Intelligence Economy<\/a><\/li>\n <li><a href=\"#\">The Price of Inaction: How AI is Turning Climate Risk Into a Capital Decision<\/a><\/li>\n <li><a href=\"#\">Why is AI Governance Lagging Behind AI Adoption?<\/a><\/li>\n <li><a href=\"#\">Is AI Healthcare Really Reaching Tier-2 India?<\/a><\/li>\n <\/ul>\n <\/div>\n\n \n <div class=\"article-footer\">\n <p>This article is based on a LinkedIn Live session conducted by NervNow. All responses are sourced from the live session and subsequent written contributions by participants, and have been lightly edited for clarity and length. The views expressed are personal to each expert and do not represent the positions of their respective employers or NervNow.<\/p>\n <\/div>\n\n <\/div>\n <\/main>\n\n \n <aside class=\"sidebar\" style=\"border-top: 2px solid #182a4f; padding-top: 40px; margin-top: 20px;\">\n\n <div class=\"sidebar-block\">\n <div class=\"sidebar-label\">Experts in This Story<\/div>\n <div class=\"sidebar-experts\">\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">AM<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Anubhav Mehrotra<\/div>\n <div class=\"expert-role\">VP, Customer Experience \u00b7 Tata 1mg<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">HK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Harmeet Kaur<\/div>\n <div class=\"expert-role\">GM, Marketing, Loyalty & CX \u00b7 The LaLiT Suri Hospitality Group<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">DK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Dipu KV<\/div>\n <div class=\"expert-role\">Senior President \u00b7 Bajaj General Insurance<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">RM<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Rohit Manghnani<\/div>\n <div class=\"expert-role\">Consultant \u00b7 Ex-Awfis Space Solutions<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">RK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Rupali Krishna<\/div>\n <div class=\"expert-role\">President, Digital Technology, MarTech & Analytics \u00b7 QuantumHiveAI<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">VK<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Varun Kulkarni<\/div>\n <div class=\"expert-role\">Sr. Director, Marketing, Digital & Growth \u00b7 NoBroker.com<\/div>\n <\/div>\n <\/div>\n <div class=\"expert-card\">\n <div class=\"expert-avatar\">VR<\/div>\n <div class=\"expert-info\">\n <div class=\"expert-name\">Varadharajan Raghunathan<\/div>\n <div class=\"expert-role\">Head, Retail Media & CX \u00b7 Tata Group<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"sidebar-block\">\n <div class=\"sidebar-label\">Topics<\/div>\n <div class=\"sidebar-tags\">\n <span class=\"sidebar-tag\">AI<\/span>\n <span class=\"sidebar-tag\">Customer Experience<\/span>\n <span class=\"sidebar-tag\">CXO<\/span>\n <span class=\"sidebar-tag\">Data Strategy<\/span>\n <span class=\"sidebar-tag\">MarTech<\/span>\n <span class=\"sidebar-tag\">Digital Transformation<\/span>\n <span class=\"sidebar-tag\">Healthcare AI<\/span>\n <span class=\"sidebar-tag\">Hospitality<\/span>\n <span class=\"sidebar-tag\">Insurance<\/span>\n <span class=\"sidebar-tag\">B2B<\/span>\n <\/div>\n <\/div>\n\n <\/aside>\n\n<\/div>\n\n\n<footer class=\"site-footer\">\n <p>\u00a9 2026 <strong>NervNow\u2122<\/strong> \u00b7 All rights reserved \u00b7 <a href=\"https:\/\/nervnow.com\/ro\/privacy-policy\/\">Privacy Policy<\/a><\/p>\n<\/footer>\n\n<\/body>\n<\/html>","protected":false},"excerpt":{"rendered":"<p>Seven senior leaders from healthcare, hospitality, insurance, real estate, B2B workspace, retail media, and MarTech sat with NervNow to answer one uncomfortable question: if AI is making everything faster, why is customer confidence going the other direction?<\/p>","protected":false},"author":11,"featured_media":6487,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[95,107],"tags":[183],"class_list":["post-6486","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis","category-analysis-marketing-advertising","tag-analysis"],"blocksy_meta":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6486","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/comments?post=6486"}],"version-history":[{"count":4,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6486\/revisions"}],"predecessor-version":[{"id":6492,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6486\/revisions\/6492"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/media\/6487"}],"wp:attachment":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/media?parent=6486"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/categories?post=6486"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/tags?post=6486"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}