Customer experience is at a breaking point.
Despite billions of dollars invested in SaaS tools, automation, and AI chatbots, only 3% of companies are customer-obsessed, according to Forrester. This is staggering, considering that customer-obsessed companies see 41% faster revenue growth and 51% better customer retention than others.
If the rewards of customer obsession are so clear, why are 97% of organizations still failing to deliver the experiences customers expect?
The truth is as surprising as it is obvious: AI and automation have been applied the wrong way. Instead of enhancing human efforts, AI is often bolted onto existing processes as an isolated tool that’s disconnected from teams, products, and real customer context.
No points for guessing the outcome—frustrated customers, overwhelmed agents, and AI that simply doesn’t work as promised.
This blog brings together the boldest ideas, sharpest provocations, and forward-looking visions shared by industry leaders at DevRev’s Effortless Mumbai 2025 conference, where customer experience, AI, and enterprise innovation were the heartbeat of every conversation.
Through these rich and candid discussions, we dig deep into why AI has fallen short in customer experience, and how conversational AI unlocks real Team Intelligence to empower companies serve and delight their customers.
How conversational customer experience unleashes AI’s full potential
More than 75% of customer service leaders feel pressure from other leaders in their enterprise to implement GenAI, according to Gartner. When companies talk about improving customer experience with AI, the conversation almost always begins with chatbots and self-service tools. For years, businesses have been sold on the dream that AI chatbots would replace human agents, automate interactions, and slash costs, all while improving customer satisfaction.
Fast-forward to today, and we see how most AI solutions today have failed to deliver on these promises. Why? Because AI is bolted on like an afterthought, disconnected from the broader ecosystem of customer knowledge, team collaboration, and product intelligence.
This is where conversational customer experience fundamentally redefines the role of AI in customer service. Conversational CX doesn’t mean that AI would replace humans, but AI would collaborate with humans, embedded directly in conversations and workflows, fully aware of customer context, product history, and team insights.
The premise that we have is that AI will be hard. And it’s not just hard for you as users and buyers, but also for us folks, because we have to be at the pace of innovation that we think AI will be at. And the only thing that can make it easy is design.
Dheeraj cuts to the heart of the issue: AI cannot be effective without thoughtful design that bridges the gap between users, teams, and systems. Whether it’s a C-level executive or a frontline support rep, everyone needs AI that feels like part of the team, and not a black box. And that begins with designing conversational agents that are smart, contextual, and collaborative—not “big” disconnected systems or “small” trivial bots.
Consider this example: A customer reaches out to an airline about a delayed flight. A traditional chatbot might reply, “Please check our FAQs.” But a conversational AI agent, embedded into the CX team and connected to flight operations data, would know the customer’s itinerary, check real-time flight status, and proactively offer rebooking options, all within seconds. That’s the difference between a frustrating interaction and a loyalty-building moment.
This is what true conversational AI looks like: an active, intelligent, and collaborative system that improves the way teams serve customers.
Because in the end, AI alone is not enough. Conversational AI that’s embedded in team workflows and powered by real context is the only way to deliver the experience that modern customers expect.
Why legacy SaaS and traditional AI bots fail enterprises
Legacy SaaS is expensive—it’s wasteful, it’s big. It was never built for India.
For over two decades, enterprises have invested billions of dollars into SaaS tools—CRM platforms, ticketing tools, knowledge base platforms, and automation suites—all in the pursuit of delivering better, faster, and more personalized customer experiences.
Yet, today, customer dissatisfaction remains at an all-time high, and support teams are more burned out than ever. Why is this happening?
SaaS was supposed to make life easier. Clearly, it didn’t. Instead of unified platforms, enterprises today juggle dozens of SaaS apps that don’t integrate deeply, forcing human agents to stitch together information manually.
These tools were built for a different era when customer engagement was slower and more predictable. In today’s real-time, hyper-personalized economy, legacy SaaS falls short.
Here are four critical reasons SaaS and AI bots fail to deliver great CX:
1. Data silos: the silent killers of CX
Companies know more about their customers than ever before. But that data lives in disconnected systems: CRM, ticketing, marketing automation, support platforms, and more. Fragmented, siloed data is at the heart of this dysfunction brought on by legacy SaaS.
Vinod Kannan, Chief Integration Officer of Air India, draws attention to the cost of disconnected data minus context in one of the most high-stakes customer-facing industries: airlines.
In an industry where you have the ability to screw up at so many phases, right from the time you book to the time you travel and get your bags out, the context has changed. So I say that what we could do in the past, just using data is no longer enough.
Airlines operate in complex environments, where customer context such as trip purpose, booking history, loyalty status should drive every interaction. Yet legacy systems trap this data in silos, preventing both human agents and AI from delivering personalized service.
Vinod’s insight illustrates a broader enterprise truth: customer interactions today require full, real-time context to be effective, something traditional SaaS and AI bots cannot provide.
2. Middle management: stuck between tech and action
Compounding the problem is internal organizational inertia, especially among middle management, who often struggle to connect emerging technologies with actionable outcomes.
Gautam Anand, an industry leader with deep experience in deploying CX solutions at scale, explains how this internal bottleneck plays out:
If you go to a large enterprise, the whole hierarchy which is there, a lot of people in middle management are not really sure on how to kind of take AI to the next level or how to make it really work at scale.
This reflects a critical failure of alignment. While executives may set ambitious AI and CX goals, and frontline teams face the daily pain of disconnected workflows, middle managers often don’t know how to bridge the gap. As a result, AI initiatives get stuck in pilots and never translate into scaled impact.
Gautam’s point also speaks to why AI often remains isolated, instead of becoming an embedded, team-wide capability that improves the customer experience.
3. The false promise of AI chatbots
Enterprises have been sold the myth that AI chatbots could “solve” customer experience, replacing human effort with automation. In reality, first-gen AI bots have largely failed, offering canned responses that alienate customers rather than delight them.
As Dheeraj points out, part of the problem is that enterprises don’t approach AI and CX with a system-level mindset. Instead, they approach AI like a feature—adding chatbots on top of broken systems.
AI solutions today often add complexity and cost rather than simplify and improve the experience. Without a unified architecture and access to real-time customer data, AI chatbots are blind—unable to understand customer history, context, or urgency.
4. Frustrated customers, burned-out teams
When AI bots fail to resolve customer issues, the result is familiar to every enterprise today: customers escalate to human agents, but by then they’re already frustrated. Human agents, meanwhile, are overwhelmed by repetitive queries and left cleaning up AI’s mess. All this contributes to poor customer experience.
And the stakes are too high to ignore this: 32% of customers will leave a brand after just one bad experience, PwC points out.
Vinod Kannan points out that airlines and telecommunications are the two industries that have the lowest Net Promoter Scores (NPS). “No one applauds you when things go right, that’s expected. But if the flight is delayed by 15 minutes or your baggage is lost, well, hell hath no fury like a customer scorned,” he observes.
In other words, customer expectations are sky-high, but traditional CX setups—powered by fragmented SaaS and broken AI—simply cannot meet them.
How AI unlocks Team Intelligence to redefine customer experience
SaaS for the past 20 years has siloed us apart. It’s given us clunky integrations, poor user experiences where we’re inundated with notifications and we lose the ability to focus. AI cleans that up. AI gives us the ability to actually deconstruct the silos that SaaS constructed for us and bring teams together under a simple principle that information will redefine how we work.
Today’s customer experience platforms often deploy AI as a last-mile feature: a chatbot here, an automation rule there. But none of this connects deeply with how teams actually work. AI is operating in a silo, disconnected from product data, customer history, and team workflows.
To break free from this trap, AI needs to stop being treated like a feature and start being treated like a core team member—one that understands the company’s data, product, and goals.
This is what Team Intelligence means: humans and AI working as peers, connected to the same knowledge graph, aligned on the same goals, solving for the customer together.
Instead of isolated AI bots running disconnected from the business, Michael envisions AI as part of the team, reshaping the very way companies work internally.
With AI agents embedded into workflows, customer signals from chat, support tickets, or product telemetry are instantly shared across teams, so action can be taken without handoffs or delays.
And this isn’t just about internal benefits. Customers feel the difference when an AI agent not only understands their history and issue, but can immediately take intelligent action or collaborate with human teammates—no more waiting for “escalations.”
In the future of conversational customer experience, AI doesn’t just answer support queries. It connects sales, product, engineering, and support into one unified effort to serve customers. To unlock Team Intelligence, AI needs these three dimensions: searching across siloed company information to empower agents, supporting customers faster and more intelligently, and even generating content when needed.
As Dheeraj puts it, “Enterprise AI can actually make us more productive because we can search better. Enterprise AI can make us more efficient because we can support better. And it can make us more creative because it can generate better.”
For AI to become a true participant in CX, it must act like a team member: one that listens, thinks, and contributes in real time.
Michael paints this vision vividly: “The AI is another team member for you, for your organization, for your department. And it’s there—it’s part of the team, it’s listening, it’s learning, it’s thinking, it’s taking notes, it’s taking directions and it’s getting feedback from you.”
Notice the nuance here: AI doesn’t just “do tasks,” but engages in dialogue, adjusts based on feedback, and learns from each interaction. This is conversational AI at its most powerful: responsive, intelligent, and human-centered.
Imagine a product manager reviewing customer complaints about a new feature. Instead of manually reading hundreds of tickets, the product manager can ask the AI in natural language to summarize trends, highlight urgent issues, and even propose fixes, all while collaborating with human teammates for final decisions.
In sum, the future of customer experience isn’t about AI replacing humans, but about AI and humans working together, powered by shared knowledge and aligned goals.
Because in the end, customer experience is a team sport, and AI is now part of that team.
The DevRev blueprint: AI-native, conversational customer experience at work
How do we get our information that’s siloed off to be connected to the tools we use on a daily basis and to make our workflows feel effortless as we get work done? That’s where we help you bring AI into your organization. And we do that really in two proprietary ways: we call it Airdrop and Knowledge Graph.
Customers expect seamless, real-time, hyper-personalized interactions, and they expect them at scale. Yet, delivering personalized CX at scale remains a painful trade-off for most enterprises. Enterprises either sacrifice personalization to cut costs, or they balloon headcount to meet rising demand, eroding margins and slowing innovation.
This “cost versus quality” dilemma is precisely what DevRev solves.
DevRev offers a radical alternative to fragmented, bolt-on AI solutions: a modern business operating system for CX where AI and humans work together seamlessly, powered by deep context and real-time learning. This lets enterprises rethink CX through an AI-native, conversational lens and actually unify all the data silos that prevent AI from being truly useful.
Here’s a look at the 4 key components of DevRev that upgrades CX for the AI era:
Airdrop: Unifying data without disruption
The average enterprise uses 625 applications, an estimate reveals. So, enterprises can’t rip and replace existing tools overnight. They need a pathway to unify data and bring AI into workflows without disrupting operations.
That’s why DevRev’s patented Airdrop technology is a game-changer: a system that ingests and unifies fragmented data sources into the Knowledge Graph, while preserving existing workflows.
As Michael puts it, Airdrop is a “connective tissue” that lets DevRev sit comfortably in the existing tech stack of enterprises. “It doesn’t ask you to come adopt DevRev all at once. It actually starts by saying, where does your data lie? Where are you getting your work done today? Let us connect to those systems. Let’s bidirectionally sync with those systems so we can understand what information is flowing every second.”
This approach meets enterprises where they are: respecting existing systems, while making data AI-ready. Without such a foundation, conversational AI will always struggle to be more than a chatbot: disconnected, surface-level, and frustrating for customers.
Knowledge Graph: The AI brain for CX
Enterprises sit on oceans of customer data trapped in CRMs, ERPs, support platforms, product tools, and spreadsheets. Without a unified, AI-ready data foundation, even the smartest AI fails to deliver personalized, contextual support.
As Dheeraj explains, the Knowledge Graph is DevRev’s answer to this problem—an AI-native data layer that connects every customer touchpoint, product detail, and internal workflow into a single system of record: “The idea of a knowledge graph is core to a lot of our thinking. We feel that this is what builds the central asset of an operating system, the knowledge graph.”
This Knowledge Graph becomes the living, breathing brain behind AI agents, enabling them to converse intelligently, resolve issues dynamically, and anticipate needs proactively.
SAW: Search, Analytics, Workflows
At the foundation of DevRev’s AI-native conversational customer experience is a tightly integrated system of Search, Analytics, and Workflows (SAW), which sits on top of the Knowledge Graph, and is built to unify data, generate real-time insights, and automate action.
- Search: Traditional search in enterprises is broken. DevRev transforms this reality with an AI-powered enterprise search that continuously integrates, deduplicates, and prepares hundreds of data sources using custom connectors.This is a ChatGPT-like system purpose-built for enterprises, enabling employees to ask natural language questions and receive precise, context-rich answers drawn from every corner of the business.
- Analytics: AI without the right insights is blind. DevRev’s analytics engine converts raw, siloed data into real-time, actionable intelligence, providing companies with a complete 360° view of customers, products, and operations. By organizing feedback, interactions, product issues, and team actions into one analytical layer, agents offer responses that are hyper-personalized and accurate, while leaders can identify top customer needs, monitor product performance, and prioritize high-impact fixes.
- Workflows: Insight without action is meaningless. DevRev closes this gap with AI-native workflows that automate complex business processes, not via rigid rule-based systems, but through dynamic, real-time AI-assisted flows. Unlike traditional workflow tools that are code-heavy and disconnected, DevRev’s workflows combine custom-code and no-code options, enabling automated ticketing, onboarding, resolution management, and resource allocation without manual intervention.
AI agents: Team members, not bots
With unified data in place, DevRev introduces AI agents that go far beyond traditional bots. These are conversational, reasoning agents that act as team members, resolving real customer issues, learning from interactions, and collaborating with humans.
AI agents in DevRev are not just front-line responders. They become embedded in your CX operations, working alongside support, product, and engineering teams.
One of DevRev’s biggest differentiators is how easy it is to deploy AI agents. Unlike traditional AI projects that require months of engineering effort, DevRev allows businesses to launch no-code AI agents that learn continuously, from real-time conversations, tickets, and customer feedback.
This means AI agents get smarter every day automatically as they learn from new interactions and evolving customer needs.
India as ground zero for AI-native customer experience
There is no reason why this next wave of AI or GenAI will not be led from India. In fact, our own view is that the use cases will be created here and they’ll actually be used somewhere else.
When global executives talk about the future of AI and customer experience, few look to India as the first stop. But as Sandeep and other industry leaders at Effortless Mumbai 2025 made clear, India is not only an emerging market for AI but is fast becoming its proving ground, particularly for AI-powered customer experiences.
What makes India different? The answer lies in a unique mix of massive population scale, tech-savvy consumers, high service expectations, and an environment where legacy customer service models no longer work at scale. If you can build AI-native CX that works in India, where millions of users are coming online, interacting daily, and expecting instant responses, you can build it anywhere.
For years, the common perception of AI adoption in India focused on urban elite audiences in major cities like Bengaluru, Mumbai, and Delhi. But this story has changed: AI adoption is now moving beyond Tier 1 into Tier 2, Tier 3, and even Tier 4 towns, driven by mobile penetration, cheap data, and conversational interfaces.
As Gautam Anand points out from his experience in the banking sector, India’s smaller towns and rural users are not only ready but eager to engage with AI, provided it’s in the form of conversational, intuitive experiences: “Their expectation is that, if I put a query on the chatbot, the bank is smart enough to understand the context and able to give me a solution there and then without really talking to anybody.”
So, younger Indians, many of them first-time digital users, are choosing chatbots over human agents because they trust AI to deliver faster, more personalized, and less intimidating interactions. They expect AI to understand context.
India is also a country where mobile-first behavior is deeply ingrained. The ubiquity of WhatsApp and Unified Payments Interface or UPI apps for real-time online payments has made chat-based and conversational interfaces second nature for hundreds of millions of users.
These trends make India an ideal sandbox for AI-native, conversational CX solutions. And in a market as competitive as India, customer obsession is not optional, but survival. In such an environment, AI is the only way to meet soaring customer expectations without exploding operational costs.
So, India needs AI-native systems that are affordable, scalable, and capable of multilingual, context-aware interactions, all out of the box. And DevRev’s architecture, with its AI-native Knowledge Graph and conversational interfaces, is designed to do precisely this.
So here’s the rallying cry: It’s time to reboot customer experience with conversational AI. It’s time to treat AI not as a tool, but as a teammate.
Ready to see how AI-powered, conversational CX can transform your business? Book a demo now to see how DevRev helps your organization provide AI-first customer experience that’s scalable, conversational, and effortless.
