After the technology integrated with AI, multiple businesses and giant organisations have begun to increasingly deploy it to handle customer interactions, increasing the efficiency of the firm. A debate is also emerging over what truly defines good customer experience: the ability to mimic human conversation, or the ability to consistently solve problems efficiently. The human-like customer experiences from AI at scale come through conversational natural language processing, real-time sentiment analysis, and predictive personalisation engines. These tools automate routine support while empowering human teams. The integration of technological progress and heightened customer expectations has created an ideal opportunity for revolutionising customer engagement. The next dimension of the customer experience, along with the next best experience approach, is generally led by front-runners who can effectively use AI and gen AI to enhance predictive capabilities and gain a competitive edge, unlocking multiple potential. Reacting to the development, Industry experts argue that while AI has made remarkable strides in communicating naturally, true customer satisfaction hinges less on conversational polish and more on speed, accuracy, and the strength of the underlying data systems powering these interactions.<h3>Combine automation</h3>According to experts, the real transformation lies not in AI sounding more human, but in how effectively organisations can combine automation with human judgment for complex, high-stakes situations, a balance that will define the next phase of customer experience strategy. Prasad Rai, CEO, DAAS LABS, said, "The conversation shouldn't be whether AI can sound like a human, but whether it can consistently make people feel heard, and those are completely different goals. Organisations often confuse the ability to conduct a conversation well with providing good customer experience, when in reality, people care far more about speed, accuracy, and resolution than about how their question was answered. AI can certainly provide a human-like experience, but only when it's built on the right data foundation: proper context, high-quality data, and a clear understanding of what the customer is trying to do." He noted that several organisations fall short because their data is disjointed, their working processes were never designed and developed this way, and no amount of conversational polish can compensate for that. "What AI should really be doing is taking care of the mundane, well-structured decisions and helping teams move faster and smarter, so that people are freed up for the complex, sensitive conversations that genuinely require trust, judgment, and empathy. The companies that succeed in customer experience won't be the ones with the most human-like AI, but the ones with the strongest data foundation underneath it, because that's ultimately what makes everything easier," he added. Highlighting the real customer experience after integration of AI, Sri Mookiah, Founder &amp; CEO, LOWCODEMINDS, stated that for a long time, businesses assumed personalised customer service and scalability were mutually exclusive. AI is now disproving that notion. Moving ahead, competitive advantage won't hinge on which company has the largest AI model, but on which one delivers the superior customer experience. He said, "AI can now understand context, analyse customer history, interpret documents, communicate naturally across voice and chat, and resolve many issues without human intervention. What once required thousands of service agents can increasingly be delivered by intelligent AI systems operating 24/7. But businesses should be careful not to confuse conversation with experience." "Customers do not remember how quickly an answer was generated. They remember whether their problem was understood, resolved fairly, and whether they felt valued. Empathy, accountability, and judgment remain uniquely human strengths, particularly in complex or emotionally charged situations. The organisations that succeed will not replace humans with AI. They will redesign customer journeys where AI handles routine interactions at scale while people focus on trust, relationships, and high-value decisions. The future of customer experience is not artificial or human. It is augmented, where AI provides the speed, and humans provide the judgment that earns lasting customer loyalty," Mookiah added.&nbsp;Also read: <a href="https://www.wionews.com/world/takaichi-s-1-7tn-spending-plan-sparks-fears-of-liz-truss-style-shock-how-will-it-impact-japan-china-and-india-1785059302899">Takaichi's £1.7tn spending plan sparks fears of 'Liz Truss-style' shock: How will it impact Japan, China and India?</a> Giving reality about the customer experience, Hitesh Agrawal, Founder &amp; Managing Director, Them Consulting, stated that the real question is not whether AI can make a difference in customer experience. It has already started. He noted that the real question comes when it actually makes things better for people, and when it quietly makes them worse. "In big banks, the pattern is clear. AI systems now take care of a huge chunk of incoming requests on their own, often 40 to 60 per cent, and in some well‑run setups that climbs to around 80 per cent for simple, routine queries. That shift has completely changed the maths on service speed. First responses that used to take hours now land in minutes. Average query handling times have now been cut to a fraction of what they used to be. For simple things like checking the account balance or confirming a credit card limit, this new speed is genuinely impacting customers positively. Where it gets tricky is what happens beyond those routine moments. When you compare pure AI journeys with human agents, the satisfaction scores are now surprisingly close. On paper, the gap is small. But when you ask people what they actually want when they call in the customer care line in distress, most still will say something similar. If the issue is complex or involves money they are worried about, they would rather talk to a person," he said. "The setups that seem to work best accept that reality instead of fighting it. They use AI to clear out the repetitive work, give agents better context, and then make sure the difficult conversations get to a human quickly, with all the history on the screen. That is what “human‑like at scale” really looks like in practice: machines doing the heavy lifting so people can focus on the moments that actually need them," he added. Sukesh Shetty, Founder &amp; CEO at Away.ai, said, "Most of the AI that got built into customer service last year was built to sound more human. Warmer chatbots. Friendlier scripts with a name and a little personality bolted on. None of that fixed what was actually broken, which is why people still don't trust it. Good customer experience runs on two layers: empathy and information. People are naturally good at empathy. It's the information layer, whether you get the right answer without having to ask twice, that decides if an experience is actually good. Go back and read the complaints people write; nobody's upset about tone; they're upset because someone told them the wrong thing, or told them too late, and that's actually the part AI needs to get right, not how kind it sounds doing it. At Away, our AI doesn't try to be warm with you. It just goes and does the boring part, negotiating with our own supplier network for a better fare. And better isn't always cheaper. Sometimes it's a refundable ticket instead of a locked one, or a lower cancellation fee, and we say so, because most companies would rather imply they found you the cheapest seat than admit they found you a different kind of good deal, and that habit is a big part of why people stopped trusting this stuff. I don't think AI was ever going to earn anyone's trust by sounding more like us. It earns it by being something we can actually rely on when we're not the ones checking."

The human-like customer experiences from AI at scale come through conversational natural language processing, real-time sentiment analysis, and predictive personalisation engines. 

After the technology integrated with AI, multiple businesses and giant organisations have begun to increasingly deploy it to handle customer interactions, increasing the efficiency of the firm. A debate is also emerging over what truly defines good customer experience: the ability to mimic human conversation, or the ability to consistently solve problems efficiently. The human-like customer experiences from AI at scale come through conversational natural language processing, real-time sentiment analysis, and predictive personalisation engines. These tools automate routine support while empowering human teams. The integration of technological progress and heightened customer expectations has created an ideal opportunity for revolutionising customer engagement. The next dimension of the customer experience, along with the next best experience approach, is generally led by front-runners who can effectively use AI and gen AI to enhance predictive capabilities and gain a competitive edge, unlocking multiple potential. Reacting to the development, Industry experts argue that while AI has made remarkable strides in communicating naturally, true customer satisfaction hinges less on conversational polish and more on speed, accuracy, and the strength of the underlying data systems powering these interactions. Combine automation According to experts, the real transformation lies not in AI sounding more human, but in how effectively organisations can combine automation with human judgment for complex, high-stakes situations, a balance that will define the next phase of customer experience strategy. Prasad Rai, CEO, DAAS LABS, said, "The conversation shouldn't be whether AI can sound like a human, but whether it can consistently make people feel heard, and those are completely different goals. Organisations often confuse the ability to conduct a conversation well with providing good customer experience, when in reality, people care far more about speed, accuracy, and resolution than about how their question was answered. AI can certainly provide a human-like experience, but only when it's built on the right data foundation: proper context, high-quality data, and a clear understanding of what the customer is trying to do." He noted that several organisations fall short because their data is disjointed, their working processes were never designed and developed this way, and no amount of conversational polish can compensate for that. "What AI should really be doing is taking care of the mundane, well-structured decisions and helping teams move faster and smarter, so that people are freed up for the complex, sensitive conversations that genuinely require trust, judgment, and empathy. The companies that succeed in customer experience won't be the ones with the most human-like AI, but the ones with the strongest data foundation underneath it, because that's ultimately what makes everything easier," he added. Highlighting the real customer experience after integration of AI, Sri Mookiah, Founder & CEO, LOWCODEMINDS, stated that for a long time, businesses assumed personalised customer service and scalability were mutually exclusive. AI is now disproving that notion. Moving ahead, competitive advantage won't hinge on which company has the largest AI model, but on which one delivers the superior customer experience. He said, "AI can now understand context, analyse customer history, interpret documents, communicate naturally across voice and chat, and resolve many issues without human intervention. What once required thousands of service agents can increasingly be delivered by intelligent AI systems operating 24/7. But businesses should be careful not to confuse conversation with experience." "Customers do not remember how quickly an answer was generated. They remember whether their problem was understood, resolved fairly, and whether they felt valued. Empathy, accountability, and judgment remain uniquely human strengths, particularly in complex or emotionally charged situations. The organisations that succeed will not replace humans with AI. They will redesign customer journeys where AI handles routine interactions at scale while people focus on trust, relationships, and high-value decisions. The future of customer experience is not artificial or human. It is augmented, where AI provides the speed, and humans provide the judgment that earns lasting customer loyalty," Mookiah added. Also read: Takaichi's £1.7tn spending plan sparks fears of 'Liz Truss-style' shock: How will it impact Japan, China and India? Giving reality about the customer experience, Hitesh Agrawal, Founder & Managing Director, Them Consulting, stated that the real question is not whether AI can make a difference in customer experience. It has already started. He noted that the real question comes when it actually makes things better for people, and when it quietly makes them worse. "In big banks, the pattern is clear. AI systems now take care of a huge chunk of incoming requests on their own, often 40 to 60 per cent, and in some well‑run setups that climbs to around 80 per cent for simple, routine queries. That shift has completely changed the maths on service speed. First responses that used to take hours now land in minutes. Average query handling times have now been cut to a fraction of what they used to be. For simple things like checking the account balance or confirming a credit card limit, this new speed is genuinely impacting customers positively. Where it gets tricky is what happens beyond those routine moments. When you compare pure AI journeys with human agents, the satisfaction scores are now surprisingly close. On paper, the gap is small. But when you ask people what they actually want when they call in the customer care line in distress, most still will say something similar. If the issue is complex or involves money they are worried about, they would rather talk to a person," he said. "The setups that seem to work best accept that reality instead of fighting it. They use AI to clear out the repetitive work, give agents better context, and then make sure the difficult conversations get to a human quickly, with all the history on the screen. That is what “human‑like at scale” really looks like in practice: machines doing the heavy lifting so people can focus on the moments that actually need them," he added. Sukesh Shetty, Founder & CEO at Away.ai, said, "Most of the AI that got built into customer service last year was built to sound more human. Warmer chatbots. Friendlier scripts with a name and a little personality bolted on. None of that fixed what was actually broken, which is why people still don't trust it. Good customer experience runs on two layers: empathy and information. People are naturally good at empathy. It's the information layer, whether you get the right answer without having to ask twice, that decides if an experience is actually good. Go back and read the complaints people write; nobody's upset about tone; they're upset because someone told them the wrong thing, or told them too late, and that's actually the part AI needs to get right, not how kind it sounds doing it. At Away, our AI doesn't try to be warm with you. It just goes and does the boring part, negotiating with our own supplier network for a better fare. And better isn't always cheaper. Sometimes it's a refundable ticket instead of a locked one, or a lower cancellation fee, and we say so, because most companies would rather imply they found you the cheapest seat than admit they found you a different kind of good deal, and that habit is a big part of why people stopped trusting this stuff. I don't think AI was ever going to earn anyone's trust by sounding more like us. It earns it by being something we can actually rely on when we're not the ones checking."

The human-like customer experiences from AI at scale come through conversational natural language processing, real-time sentiment analysis, and predictive personalisation engines.&nbsp;

Can AI deliver human-like customer experience at scale? Experts weigh in

As artificial intelligence (AI) rapidly integrates into every facet of our lives, data centres are evolving to meet the growing demand for intelligence and scale. The global AI market is projected to reach $407 billion by 2027, with AI workloads estimated to account for over 50% of data centre infrastructure by 2025. The rise of AI-driven applications, particularly generative AI (GenAI), is reshaping industries, from healthcare and finance to entertainment and education. This shift requires not only cutting-edge computational power but also a fundamental rethinking of how data centres are designed, managed, and scaled.&nbsp;

AI and GenAI: The Driving Force Behind Modern Data Centres&nbsp;

AI’s ability to process vast amounts of data in real-time has unlocked new capabilities, from predictive analytics to conversational agents powered by large language models (LLMs). GenAI, in particular, is revolutionising how businesses generate content, build solutions, and automate processes.&nbsp;

Large-scale AI applications rely on advanced hardware such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) to accelerate training and inference processes. Traditional data centres once focused primarily on storage, are now built to handle the complexities of AI workloads. This requires significant adaptations in network architecture, storage systems, and data transmission, all geared towards maximising efficiency and speed.&nbsp;

Scaling for Intelligence: Infrastructure Adaptations&nbsp;

The challenge is no longer just about storing data; it’s about how fast that data can be processed, analysed, and used to drive intelligent decisions. Data centres are undergoing a transformation to accommodate AI’s need for scalable, high-performance infrastructure. Hyper-converged infrastructure, which integrates computing, storage, and networking into a single system, is a key innovation in helping data centres manage AI workloads. Additionally, edge computing is gaining traction as it allows data to be processed closer to the source, reducing latency and bandwidth use. These adaptations help ensure that AI applications can run seamlessly, providing real time responses in everything from autonomous vehicles to smart factories.

Energy Efficiency and Sustainability in the AI Age&nbsp;

With great computational power comes great energy demand. AI and GenAI models are highly resource-intensive, driving up the energy consumption of data centres. In response, the industry is focusing on energy efficiency and sustainability. Innovations like liquid cooling, which dissipates heat more effectively than traditional air-based methods, are helping data centres manage the heat generated by powerful AI hardware. AI itself is being leveraged to optimise power usage, dynamically adjusting energy consumption based on workload needs. This not only reduces operational costs but also minimises the carbon footprint of data centres—a crucial consideration as businesses aim to meet sustainability goals.&nbsp;

Security and Reliability: Safeguarding AI-Driven Data&nbsp;

The integration of AI into business-critical operations places even more emphasis on security and reliability. AI models often handle sensitive data, making them a target for cyberattacks. To counter this, data centres are utilising AI-powered threat detection systems to identify and mitigate risks in real-time.&nbsp;

The Role of GenAI in Data Centre Management&nbsp;

Interestingly, AI is not only driving the demand for data centres but also transforming how they are being managed. Generative AI models are now being used to optimise operations within data centres, automating routine tasks and enhancing overall efficiency.&nbsp;

From traffic routing to load balancing, GenAI systems can predict peak usage times, allocate resources more effectively, and even prevent potential hardware failures through predictive maintenance. These advancements help reduce operational costs and streamline data centre management, ensuring that the infrastructure keeps pace with AI’s growing needs.

The Future of Data Centres in the AI Age&nbsp;

As AI and GenAI continue to evolve, the role of data centres will become even more critical. The global data centre market is expected to exceed $105 billion by 2028, driven largely by AI advancements. The demand for scalable, intelligent, and efficient infrastructure will only increase, pushing data centres to further innovate. We can expect advancements in quantum computing, new cooling technologies, and even more integration of AI into data centre operations.&nbsp;

In the age of AI, data centres are not just storage facilities but also the backbone of an intelligent digital economy. By meeting the demands of AI and GenAI, data centres are enabling businesses to innovate, scale, and stay competitive in a rapidly changing world.

As artificial intelligence (AI) rapidly integrates into every facet of our lives, data centres are evolving to meet the growing demand for intelligence and scale.

As artificial intelligence (AI) rapidly integrates into every facet of our lives, data centres are evolving to meet the growing demand for intelligence and scale. The global AI market is projected to reach $407 billion by 2027, with AI workloads estimated to account for over 50% of data centre infrastructure by 2025. The rise of AI-driven applications, particularly generative AI (GenAI), is reshaping industries, from healthcare and finance to entertainment and education. This shift requires not only cutting-edge computational power but also a fundamental rethinking of how data centres are designed, managed, and scaled. AI and GenAI: The Driving Force Behind Modern Data Centres AI’s ability to process vast amounts of data in real-time has unlocked new capabilities, from predictive analytics to conversational agents powered by large language models (LLMs). GenAI, in particular, is revolutionising how businesses generate content, build solutions, and automate processes. Large-scale AI applications rely on advanced hardware such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) to accelerate training and inference processes. Traditional data centres once focused primarily on storage, are now built to handle the complexities of AI workloads. This requires significant adaptations in network architecture, storage systems, and data transmission, all geared towards maximising efficiency and speed. Scaling for Intelligence: Infrastructure Adaptations The challenge is no longer just about storing data; it’s about how fast that data can be processed, analysed, and used to drive intelligent decisions. Data centres are undergoing a transformation to accommodate AI’s need for scalable, high-performance infrastructure. Hyper-converged infrastructure, which integrates computing, storage, and networking into a single system, is a key innovation in helping data centres manage AI workloads. Additionally, edge computing is gaining traction as it allows data to be processed closer to the source, reducing latency and bandwidth use. These adaptations help ensure that AI applications can run seamlessly, providing real time responses in everything from autonomous vehicles to smart factories. Energy Efficiency and Sustainability in the AI Age With great computational power comes great energy demand. AI and GenAI models are highly resource-intensive, driving up the energy consumption of data centres. In response, the industry is focusing on energy efficiency and sustainability. Innovations like liquid cooling, which dissipates heat more effectively than traditional air-based methods, are helping data centres manage the heat generated by powerful AI hardware. AI itself is being leveraged to optimise power usage, dynamically adjusting energy consumption based on workload needs. This not only reduces operational costs but also minimises the carbon footprint of data centres—a crucial consideration as businesses aim to meet sustainability goals. Security and Reliability: Safeguarding AI-Driven Data The integration of AI into business-critical operations places even more emphasis on security and reliability. AI models often handle sensitive data, making them a target for cyberattacks. To counter this, data centres are utilising AI-powered threat detection systems to identify and mitigate risks in real-time. The Role of GenAI in Data Centre Management Interestingly, AI is not only driving the demand for data centres but also transforming how they are being managed. Generative AI models are now being used to optimise operations within data centres, automating routine tasks and enhancing overall efficiency. From traffic routing to load balancing, GenAI systems can predict peak usage times, allocate resources more effectively, and even prevent potential hardware failures through predictive maintenance. These advancements help reduce operational costs and streamline data centre management, ensuring that the infrastructure keeps pace with AI’s growing needs. The Future of Data Centres in the AI Age As AI and GenAI continue to evolve, the role of data centres will become even more critical. The global data centre market is expected to exceed $105 billion by 2028, driven largely by AI advancements. The demand for scalable, intelligent, and efficient infrastructure will only increase, pushing data centres to further innovate. We can expect advancements in quantum computing, new cooling technologies, and even more integration of AI into data centre operations. In the age of AI, data centres are not just storage facilities but also the backbone of an intelligent digital economy. By meeting the demands of AI and GenAI, data centres are enabling businesses to innovate, scale, and stay competitive in a rapidly changing world.

Data Centres in the Age of AI: Meeting the Demand for Intelligence and Scale  

The recent revelations from a recent EY survey offers a closer look at how Gen AI, the next frontier in artificial intelligence, is reshaping the retail experience right here in India.

Challenges & opportunities in the retail renaissance with Gen AI

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Can AI deliver human-like customer experience at scale? Experts weigh in

Data Centres in the Age of AI: Meeting the Demand for Intelligence and Scale

Challenges & opportunities in the retail renaissance with Gen AI