Hyper personalization in terms of resolution of query, hyper personalisation in terms of giving the right offer at the right time, and hyper personalisation in terms of both internal, external customers getting the best kind of experience. Because if you don’t have that culture of giving the right experience internally, that will not kind of reciprocate outside.
Customer experience is suffering not from a lack of tech, but from internal misalignment. Teams chase hyper-personalization, yet still work in silos—hoarding data, duplicating efforts, and reacting instead of collaborating. Real transformation begins when your teams treat each other with the same empathy and urgency they offer customers. That’s when personalization becomes a default—not a feature.
The personalization market is exploding—expected to hit $2.7B—because companies are betting big on one truth: generic experiences no longer cut it.
Miss the AI wave, and you’re not just behind—you’re invisible. Your competitors will deliver smarter, faster, more relevant experiences. You’ll be playing catch-up.
Hyper-personalization isn’t a trend—it’s a takeover. AI is changing how businesses engage, respond, and build lasting loyalty with every click.
The shift from customer-centricity: Why customer obsession is the future
That we probably couldn’t become customer obsessed enough in the next three years. So I think customer obsession, I think we don’t ,nobody has a choice.
AI provides the tools to enhance personalization; however, it is crucial to understand the underlying mindset driving these changes.
Today’s market doesn’t wait. Customers expect brands to anticipate their needs—even before they’re expressed. And that shift isn’t just external—it demands cultural transformation inside the business. . If one doesn’t adapt, they’ll be watching competitors set the pace while still playing catch-up.
1. Customer-centricity: The mediocrity trap
You can just keep waiting for customers to come to you and service him, right? But how many people really complain, right? If you have a bad experience, most customers just walk away. So it’s very important to understand that point of reality, point of truth and serve him there.
The flaw in traditional customer-centricity is glaring: responding to customers’ complaints isn’t enough. This reactive mindset traps businesses in mediocrity. Customer obsession, by contrast, is a bold evolution. It’s about proactively anticipating needs before customers voice them.At the heart of this shift lies “long-term greed”—a mindset that prioritizes sustainable relationships over short-term profits.
As Warren Buffett famously put it: “my favorite holding period is forever.”, this philosophy isn’t just for investors—it’s the blueprint for business growth. Long-term value, built through customer obsession, compounds over time, delivering rewards that far outweigh quick wins.
Consider the example of Amazon. As Sandeep Dutta, President of AWS India and South Asia, shares:
At Amazon, every Amazonian practices customer obsession on a day-to-day basis.
For years, Amazon sacrificed immediate profits to refine its customer experience. The results? A standard others are now striving to emulate, and a position of global leadership.
The takeaway is simple: the future belongs to those who are relentlessly customer-obsessed. If you’re not embracing it, your competitors will surge ahead and leave you behind.
2. Silos: The silent killer of customer experience
The more siloed we created, the more we thought we became more productive, but we really created one of the biggest enemies of team intelligence, which is departments.
For years, organizations believed silos were the key to productivity. In truth, they’ve hindered collaboration and stifled innovation. Fragmented systems and siloed information lead to missed opportunities, slow decisions, and a fractured customer experience.
Yet, many businesses still rely on AI-bolted systems—AI stacked atop outdated infrastructures. These systems improve efficiency in isolated areas but break down when tackling complex issues. While AI may efficiently handle simple queries, it faces challenges when addressing more complex resolutions due to its reliance on fragmented systems.
Bolting on AI won’t fix broken support. Real impact happens when AI connects data, teams, and customer signals in real time. That’s how you deliver answers before the customer even finishes asking. In the age of personalization, relevance isn’t optional—it’s survival.
Collaboration reimagined: With people, AI and purpose at the core
1. Team intelligence and unreasonable hospitality
Low latency, precision, and personalization—why settle for two when you can have all three?
The old model of teamwork was functional. The new model is intelligent—driven by AI, aligned by purpose, and obsessively customer-first. When support, product, engineering, and design operate as one, the results don’t just improve—they multiply.
At the heart of team intelligence are three key elements:
- Discovering knowledge: A robust search process that allows you to quickly uncover what others already know, boosting productivity and preventing wasted time.
- Customer integration: Customers should be seen as an integral part of the team, not outsiders. By doing so, you build real-time, personalized, and accurate relationships that resonate with customers.
- Breaking down silos: Customer support, success, product management, engineering, and design must operate as a cohesive unit, working together to solve problems and achieve goals.
These elements lay the groundwork for low latency, precision, and personalization—once thought to be impossible to achieve simultaneously. But with AI, there’s no compromise.
2. Human vs AI in customer service
AI can recommend. Humans can reassure. And in moments of crisis, that difference is everything.
As Vinod Kannan, Chief Integration Officer at Air India, puts it:
People choose Singapore Airlines not just because they’re good, but because they have your back when the shit hits the fan. Not many airlines take care of you when things go wrong—and that’s a huge miss.
Singapore Airlines shows how human-driven service sets a business apart. During crises like ash cloud disruptions, it’s not just about getting passengers from point A to point B—it’s about going above and beyond when things go wrong. Unlike its competitors, Singapore Airlines ensures passengers feel valued in the most stressful situations.
AI enhances efficiency. It gets you part of the way there. But when the stakes are high and the situation messy, it’s human judgment, flexibility, and empathy that turn a bad moment into a brand-defining one.
Exceptional service doesn’t just solve problems—it makes people feel seen. That’s something no algorithm can fake.
3. AI that works: Problem-first, tech-second
AI isn’t a silver bullet—it’s a force multiplier. And it’s only as effective as the problem it’s designed to solve.
In industries like oil & gas or manufacturing, the impact is tangible. AI is helping prevent costly refining shutdowns, optimize energy use, and improve output in paper production. But these wins don’t come from generic algorithms—they come from problem-first thinking.
AI is just one part of a larger ecosystem, and its real power lies in how it integrates with other technologies—hardware, data, and human input—to solve the core problem.
When it comes to the wealth management sector, investors expect one thing above all: visibility. Across currencies, markets, and platforms.
The next frontier is anticipation: using AI not just to react, but to predict, guide, and adapt in real time. But powerful tools alone aren’t enough. Without the right data, context, and cross-functional alignment, even the best models break down. The future belongs to companies that treat AI as part of a larger system—deeply integrated with emerging technologies and grounded in solving real, evolving customer needs.
The AI dilemma: Unveiling the challenges ahead
Right garbage in, garbage out. So if you do not have the right technology, if you do not give the right inputs, the right variables, the output is never going to be what we expect.
A significant pitfall when implementing AI is ensuring that the algorithms are trained with high-quality data. If the system is fed biased, inaccurate, or incomplete data, it could lead to flawed customer experiences.
And data is just the start. True AI readiness demands modernizing legacy systems, aligning cross-functional teams, and building feedback loops that evolve with your users.
AI’s effectiveness depends on a broader ecosystem—how well it integrates with legacy systems, how teams collaborate around it, and how adaptive the feedback loops are.
It’s not about integrating AI, it’s about ensuring it thrives in a connected, adaptive environment characterised by:
1. Data liquidity
As Santoshi Kittur, CTO of 360One, puts it:
Liquid data- put it in the centre of the enterprise. That’s when you start being able to truly be hyper personal, multimodal.
Businesses need to make data—profiles, transactions, market insights—fluid and centralized. Only then can they create the agility, speed, and relevance required to meet customer expectations in today’s fast-moving world.
This approach enables the eventual achievement of agility, efficiency, speed, swift decision-making, and relevance. The future demands that we break free from it, embracing data as a liquid resource.
2. Linearity in workflow
Linearity is the Achilles’ heel of hyper-personalization.
In a world where customers demand tailored, real-time experiences, sticking to a linear workflow is like trying to fit a square peg in a round hole. Linear processes follow a set sequence, where one step hinges on another, causing delays and bottlenecks.
Hyper-personalization demands agility. It requires systems that can pivot instantly based on live customer signals—adapting offers, messages, and experiences in the moment. That’s where non-linear workflows come in: enabling multiple processes to run concurrently, dynamically, and intelligently.
Powered by AI, these systems automate decisions, orchestrate parallel actions, and remove dependencies that slow everything down. The result? Seamless, personalized experiences that evolve with the customer—not behind them.
In this new paradigm, success belongs to companies that ditch rigid sequences and embrace adaptive, event-driven architectures. Because in the age of AI, speed isn’t just a competitive advantage—it’s the backbone of relevance
3. Frictionless journeys & security
The biggest threat to seamless customer journeys? Security that slows things down—or worse, fails silently. Customers want seamless, personalized experiences, but they also demand data protection and privacy.
The key lies in intelligent systems that combine automation and AI with strong security protocols. AI can anticipate needs and personalize experiences at scale while ensuring security at every touchpoint. This approach delivers convenience without exposing sensitive data.
AI systems can inadvertently learn biases from the data they are trained on. Continuous training and eliminating biases are necessary to maximize the benefits of AI. Additionally, regulatory frameworks must be maintained to protect Personally Identifiable Information (PII) and ensure compliance. By employing encryption, real-time monitoring, and automated auditing, businesses can safeguard sensitive data, meet compliance standards like GDPR, and prevent breaches.
4. Achieving omnichannel analytics
AI promises omnichannel intelligence—but most companies are stuck with omnichannel confusion.
Data silos and inconsistent information across systems hinder AI’s ability to create a unified customer view, while poor data quality distorts insights. Integrating diverse sources is complex and costly, and without clear objectives, AI initiatives struggle to deliver tangible results, often falling short of expectations.
Selecting the right AI tools and integrating them with legacy systems is another hurdle. Defining KPIs and accurately attributing customer journeys requires precision, but the multitude of touchpoints complicates this task. Without the right infrastructure, AI can’t effectively track or measure omnichannel success, limiting its potential.
AI-native systems simplify these challenges by breaking down data silos, ensuring accurate, real-time insights, and automating complex integrations. With a unified framework, AI enables seamless omnichannel tracking.
The result? Smarter decisions, better experiences, and growth you can measure—not just hope for.
5.Risk mitigation & trust in AI
When AI gets it wrong, it doesn’t just misinform—it fabricates. That’s model hallucination. And it’s not rare. From false financial predictions to fake product recommendations, hallucinated outputs can derail decisions fast.
This issue is particularly concerning for personalization efforts, where erroneous outputs directly impact customer experiences. To combat this, transparency is essential.
Trust in AI starts with high-quality, unbiased, and diverse datasets, ensuring that AI models generate reliable and fair outcomes.
That starts with data. If the foundation is biased, incomplete, or outdated, the entire system fails—quietly at first, then catastrophically. In customer-facing applications, failure means more than inefficiency—it means broken experiences, false positives, missed threats, and lost loyalty.
On one side, AI must protect: identifying fraud, flagging anomalies, and triggering defenses in real time. On the other, it must personalize—learning behaviors, adapting to preferences, and delivering interactions that feel made-for-you. The tension between protection and personalization is where most systems break.
Getting it right takes more than smart models. It takes continuous experimentation, diverse data, and the discipline to test, measure, and iterate until AI earns the right to act. Trust isn’t granted—it’s built, frame by frame, interaction by interaction. The companies who understand that will lead. The rest will erode customer confidence one false prediction at a time
Beyond AI: DevRev redefines what’s possible
How do you really bring it together? How do you connect to those data sources? How do we build this knowledge graph with a twist that you can also visualize this data in a way that typically the enterprises will need it?
That’s the crux of DevRev’s approach: not just unifying data, but making it visible, contextual, and actionable—at the speed enterprise teams demand.
While others stop at connection, DevRev goes further—mapping relationships, surfacing insights, and turning complexity into clarity. This isn’t just integration. It’s intelligence, visualized.
- AI-native platform with “Airdrop” technology
Most AI platforms fail before they start—because their data lives in silos.
DevRev stands out as a true AI-native platform with its ‘AirDrop’ proprietary technology designed to streamline data integration.
It eliminates data silos and unifies customer support, product, and engineering data into a single, actionable knowledge graph. This enables AI to understand customer needs, engage contextually, and solve problems intelligently.
With all your data in one place, AI finally works the way it should: understanding context, engaging customers intelligently, and resolving issues fast. DevRev doesn’t just make AI possible—it makes it practical.
- Knowledge graph for seamless collaboration
DevRev’s AI-powered knowledge graph pulls together critical data—customer info, product usage, incident logs—allowing teams to collaborate seamlessly. This unified data foundation empowers support agents with the context they need to resolve issues faster and enhances team efficiency. By ensuring everyone works from the same real-time data, DevRev accelerates issue resolution and optimizes workflows for greater productivity.
- Conversational AI for real-time, hyper-personalized support
Most bots follow scripts. DevRev’s AI holds conversations—because it learns from every interaction and adapts in real time to your business’s language, logic, and edge cases.
DevRev enhances AI-infused collaboration by connecting real-time data across teams. Whether through email, chat, or project management tools, DevRev’s conversational AI delivers instant insights that help teams stay aligned and make smarter decisions quickly. This seamless collaboration accelerates problem-solving and ensures teams are always in sync.
- Proactive, predictive support with AI-powered incident management
DevRev doesn’t just react to issues; it proactively prevents them. Through “AI-powered” incident management, DevRev continuously monitors systems, identifies early warning signs, and resolves issues before they escalate. This predictive approach eliminates downtime, improves customer satisfaction, and shifts support from reactive to proactive, enhancing overall business resilience.
- AI-powered analytics and streamlined workflows
With “AI-powered analytics”, DevRev turns data into actionable insights. The platform automates routine tasks, spots patterns, and optimizes workflows, allowing teams to focus on complex problems. This efficient use of data improves decision-making, eliminates inefficiencies, and enhances the speed of customer support.
- Enterprise search with conversational AI
DevRev redefines enterprise search with Conversational AI. Instead of passive links and endless results, the AI delivers actionable insights in seconds. It proactively identifies rising customer activity and emerging issues, enabling teams to act immediately and access exactly the information they need—when they need it.
- Dynamic, context-driven conversations
Say goodbye to static forms and hello to dynamic, context-driven conversations. With DevRev, teams interact with AI through real-time, intuitive chats that adapt to specific needs. This fluid interaction eliminates inefficiencies and enhances collaboration, making AI not just a tool, but a proactive team member that offers insights and solutions.
- AI as a co-pilot in product management
DevRev’s AI acts as a co-pilot in product management, handling routine tasks so teams can focus on strategic decision-making. It summarizes feedback, tracks alignment, and optimizes execution. By automating the mundane, product managers can stay focused on engaging customers, crafting roadmaps, and driving product innovation. With our native GPT-powered Turing AI, we are bringing in powerful AI technology to revolutionize the role of a Product Manager.
Innovation’s new epicenter: Why the future is India
India is uniquely positioned to lead the next wave of AI and GenAI innovation. With over 1.4 billion people, a rapidly expanding digital landscape, and a mobile-first consumer base, India offers the ideal environment to develop AI-driven customer experience (CX) solutions at scale.
As Sandeep Dutta, President of AWS India, puts it,
India’s ability to leapfrog outdated models into an AI-first world is its greatest advantage.
This expectation reflects the growing demand for AI solutions that can not only respond quickly but also understand the nuances of a customer’s needs.
India’s business ecosystem is a unique blend of scale, variety, and agility. Small businesses—spread across industries as diverse as agriculture, retail, and healthcare—are thriving in a fragmented market. And it’s this very heterogeneity that fuels India’s AI revolution.
Rather than relying on one-size-fits-all solutions, Indian businesses are creating AI systems that are tailored to their specific needs. This sparks innovation at a level unseen in larger, more homogenous economies.
The use cases developed here will set the standard for AI-powered customer experiences worldwide. By tackling challenges like high employee churn and the need for cost-effective, hyper-personalized service, India is demonstrating that AI-driven solutions can be both scalable and sustainable.
India’s business ingenuity, focused on AI native platforms and doing so at scale, is quietly setting the stage for a new kind of AI leadership. It’s not about competing on the largest platforms; it’s about creating smart, nimble solutions that can scale across diverse industries—and that’s where India’s true strength lies.
In a world driven by AI, businesses that focus on building deep, human-centric connections will be the ones who thrive. DevRev is the next-generation solution for modern customer support, designed to handle the complexities of a fast-paced, data-driven world.
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