2026 CX Reality: India Customer Experience Trends

𝗛𝗼𝘄 𝗔𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗰𝗼𝘀𝘁 𝗰𝘂𝗿𝘃𝗲𝘀 𝗶𝗻 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗰𝗲𝗻𝘁𝗲𝗿𝘀. For decades, contact center costs followed one rule. More calls meant more people. Costs scaled linearly. AI breaks that rule. Here is how the curve changes. • 𝗛𝘂𝗺𝗮𝗻 𝗼𝗻𝗹𝘆 𝗺𝗼𝗱𝗲𝗹 - Volume goes up. Headcount goes up. Cost rises in a straight line. • 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 - Simple intents get automated. Cost growth slows. Hiring still continues, just at a lower rate. • 𝗔𝗴𝗲𝗻𝘁 𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 - AI handles end to end journeys, not fragments. Cost starts flattening even as volume grows. • 𝗠𝗮𝘁𝘂𝗿𝗲 𝗔𝗜 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 - AI absorbs incremental demand. Human effort stays stable. Cost becomes predictable. This shift is not about replacing people. It is about changing where human effort is applied. Humans focus on: • Exceptions • Judgment heavy cases • Risk and compliance • Experience improvement AI handles: • Repetitive intent handling • Status checks • Data gathering • Rule based execution The real insight. AI does not just reduce cost. It changes the shape of cost. If the cost curve is still linear, the work is task automation. If the curve flattens, journeys have been redesigned. That difference separates real transformation from efficiency theater. #AI #ContactCenter #CustomerExperience #DigitalTransformation #AgenticAI #Automation #FutureOfWork #EnterpriseAI

Last week i was talking about AI with my colleague on a ‘chai’ break. It was a deep long discussion on impact of AI on our current job role and i realised that In our rush to automate with AI, we have forgotten a hard truth; Our job title might stay the same, but the craft of CX has fundamentally shifted under our feet. How we managed Cx is not going to be the same anymore. One thing is very clear, If we are still measuring Cx success by how many tickets team deflect, we are not leading, in reality we are just managing a slower decline in brand trust. In my latest newsletter issue How AI Changes CX Careers Without Changing Job Titles I dive deep into: > Why an instant response can sometimes feel more robotic to your customer than a delay ? > Why your value as Cx leader is moving from managing volume to architecting judgment ? > Why the most valuable skill in 2026 is not how well prompt you write but its high stakes empathy ? Do give it a read & share your thoughts. Link to the newsletter in the 1st comment. #customerexperience #aiforcx #genai

While working closely with AI, one thing became very clear to me: AI didn’t solve big, fancy problems. It helped us fix very real, everyday business issues. One of the first things we noticed was response time. Customer queries were piling up, teams were stretched, and delays were costing us trust. AI helped us respond faster and more consistently. Not perfectly but reliably. And that reliability made a visible difference to customer experience. Another big shift was in how decisions were made. AI didn’t “decide” anything for us. What it did was highlight patterns in data that we were earlier missing or taking too long to notice. The final calls were still human but they were better informed and faster. It also reduced our dependency on a few individuals. Earlier, too much knowledge lived in people’s heads. With AI supported workflows, information became more accessible and standardised. That alone improved continuity and reduced operational risk. What surprised me most was how time got redistributed. AI didn’t remove roles. It removed repetitive work. Teams finally had space to think, review, and improve outcomes instead of just reacting. And honestly, the biggest wins didn’t come from large “AI initiatives”. They came from solving one small problem, seeing impact, and then building from there. My biggest takeaway? AI works best when it’s treated like a tool, not a strategy. The strategy still belongs to people 😊 . #AIInBusiness #BusinessExecution #Leadership #OperationalExcellence #DigitalTransformation #InnovationInPractice

The integration of AI in industries like logistics, healthcare, retail, and finance offers significant economic growth opportunities by enhancing efficiency, reducing costs, and improving service quality. However, this advancement poses challenges such as workforce displacement and ethical concerns. Jobs in logistics, retail, and finance are at risk due to automation, necessitating reskilling and strategic workforce shifts. Policymakers, educators, and industry leaders must collaborate on reskilling programs and ethical guidelines to balance AI's economic benefits with equitable opportunities, ensuring both businesses and individuals can thrive in a rapidly changing economic landscape. #AIImpact #EconomicGrowth #WorkforceDisplacement #Automation #Reskilling #AIinHealthcare #RetailInnovation #AlgorithmicTrading #EthicalAI #FutureOfWork

The integration of AI in industries like logistics, healthcare, retail, and finance offers significant economic growth opportunities by enhancing efficiency, reducing costs, and improving service quality. However, this advancement poses challenges such as workforce displacement and ethical concerns. Jobs in logistics, retail, and finance are at risk due to automation, necessitating reskilling and strategic workforce shifts. Policymakers, educators, and industry leaders must collaborate on reskilling programs and ethical guidelines to balance AI's economic benefits with equitable opportunities, ensuring both businesses and individuals can thrive in a rapidly changing economic landscape. #AIImpact #EconomicGrowth #WorkforceDisplacement #Automation #Reskilling #AIinHealthcare #RetailInnovation #AlgorithmicTrading #EthicalAI #FutureOfWork

Is 2026 the year we finally move past "AI curiosity" into "AI execution"? 🚀 I recently returned from attending two seminars in India . The consensus was powerful: the first true phase of enterprise AI isn’t just about chatbots—it’s about Business Automation through Agentic AI. Across almost every global organization, we see the same friction points: System Fragmentation: Teams spend a significant portion of their day jumping between multiple disconnected systems—ERP, planning tools, and logistics platforms—just to find a single answer for a customer. The "Manual Sprawl": Critical data and regional decisions are often still managed in "temporary" manual spreadsheets. The Time Drain: These repetitive, manual tasks drain domain experts' time, pulling them away from the strategic work they were hired to do. To move forward, AI should be Business-Led, not Technology-Led. The focus must be on solving these exact daily pain points to drive real impact. AI success isn't measured by how many tools are launched, but by usage and actual operational impact. It’s time to move from the "Art of the Possible" to the "Standard of the Business". I’d love to hear from my network: 1️⃣ Are you prioritizing "Chat" or "Agentic Automation" for the rest of 2026? 2️⃣ What is the one "manual spreadsheet" process in your org that is screaming for AI? Let’s discuss below! 👇 #AI #AgenticAI #SupplyChain #DigitalTransformation #BusinessAutomation #Leadership #Efficiency

AI doesn’t fix broken foundations. It amplifies them. After years on the enterprise side and now working alongside CXOs in BFSI, Healthcare, and Manufacturing one thing is clear: Most transformation failures are not technology problems. They are Day-0 problems that were never addressed. What looks like "AI not delivering value" is usually a sequence error. You cannot layer intelligence over "tribal knowledge" or manual workarounds. In those environments, AI only amplifies the noise. Through our work inside enterprises, we’ve found that this sequence is non-negotiable: Day 0: Digitise the Flow. Not just reports or dashboards, but the actual work distribution, finance, and compliance. Build the systems of record first. Day 1: Make Decisions Explicit. Before you automate, define what a decision is, who owns it, and what "good" looks like. Most organizations underestimate the power of this step alone. Day 2: Layer Agentic Intelligence. Only then do AI agents make sense. Not as chatbots, but as operators that detect breakpoints, trigger actions, and close the loop with full auditability. When this order is respected, transformation finally starts to compound without scaling headcount or chaos. AI is a force multiplier, but only on top of a governed, digital core. That is the difference between an experiment and a transformation. #DigitalTransformation #EnterpriseAI #AgenticAI #AIStrategy #OperationalExcellence #Leadership #FutureOfWork #DataGovernance #FoundersView

Your team isn’t slow. Your systems are just overloaded. Myth: Speed scales by adding more people. Fact: Speed scales when the system improves. Hiring extra hands can help in the short term. But as volume grows, so does complexity: - More handovers - More missed context - More “who’s doing what?” moments Following on from our AI education post: once your team knows how to use AI properly, you can reduce the drag that slows service down. AI can help your workflows stay organised and prioritised by: - Summarising tickets, emails, and meetings into clear next actions - Triaging and routing work based on urgency and impact - Creating consistent checklists/runbooks so quality doesn’t wobble under pressure The result? Speed becomes built-in, not something your people are pushed to chase. Want to deliver faster service as you grow without burning out the team? Book a strategy session: https://exodesk.co.nz/ #AI #Automation #Technology #NZTech #KiwiBusiness

Stop Measuring AI Activity. Start Measuring AI Impact. Every AI project starts with excitement. Most end with "we're still evaluating." The reason? Wrong metrics. Vanity metrics: • Call volume handled by AI • 95% accuracy (on what dataset?) • Number of queries answered Metrics that matter: • Time saved per employee (hours/week) • Cost per transaction (before vs after) • SLA adherence improvement • Customer satisfaction change • Employee burnout reduction We worked with a contact centre that deployed AI voice agents. Initial metric: call volume handled. We pushed them to measure business impact. Real results: • 30% reduction in Tier-1 workload • Agent burnout dropped (measured via surveys) • CSAT stayed flat (good—didn't get worse) • Cost per call down 18% That's ROI you can defend to your CFO. AI should improve your business in measurable ways—revenue, cost, time, and quality. If your AI project doesn't have a clear ROI story, let's build one. #AI #ROI #BusinessMetrics #Automation #DigitalTransformation #AIStrategy