{"id":35316,"topic":"ai","source":"cio.com","title":"Why the future of customer service is resolution, not fast replies - cio.com","url":"https://www.cio.com/article/4194919/why-the-future-of-customer-service-is-resolution-not-fast-replies.html","url_hash":"3d1b3c116e83c25029fc5e7c1d7aa9c58a26756f","author":"","summary":"Why the future of customer service is resolution, not fast replies cio.com","content":"What the data reveals about the gap between speed and satisfaction, and how businesses can redesign AI agents to close it.\nMost AI agents today are optimised for responsiveness—faster first responses, shorter wait times, higher service rates. And on those metrics, they’re delivering.\nIt’s no surprise then that 90% of business leaders believe their customers are satisfied with conversational AI experiences. Yet only 59% of consumers agree, according to Twilio’s latest report on conversational AI.\nWhat could explain this 31-point gap? The data is unambiguous. 54% of consumers say AI agents rarely have context about them as a customer. 78% say the ability to escalate to a human is important, yet few get the chance to do so. And only 15% report experiencing a seamless handoff from an AI agent to a human one.\nAll this to say that the real measure of an AI agent isn’t how fast it answers, but whether it solves the problem. In fact, 72% of consumers would choose an AI over a human if it could resolve their issue more quickly.\nSpeed without resolution will only result in frustration.\nWhy most AI agents aren’t great at resolution\nIt’s not hard to see why most AI agents fail to resolve customer issues: they can’t take action on behalf of the customer, they can’t escalate conversations when they reach their limits, and they lack the real-time context needed to personalise the interaction.\nThink about what a typical AI service interaction looks like. A customer calls with a billing question. The AI agent reads their intent accurately enough. But it can’t pull up the customer’s account in real time, process a credit, or route to a human specialist who knows the context of the conversation. So the customer repeats themselves. Or simply gives up.\nThe root cause is structural. In most stacks, the channel is the system of record, not the conversation. Voice, SMS, chat, and WhatsApp each run as separate sessions, so the moment a customer switches channels or escalates to a human, the interaction resets. Engineering teams paper over this by stuffing full transcripts into AI prompts to fake continuity. This inflates token costs, slows responses, and still truncates older context once the window fills up.\nThis is the gap between a chatbot and an agent. A chatbot responds. An agent resolves. It’s no wonder that the 59% of organisations planning to fully replace their current conversational AI solution within the year understand this distinction. Their early investments were simply optimised for the wrong outcome.\nA smarter agent only gets you so far. Businesses need four capabilities to close the resolution gap:\n- Agency: Agents must be able to take real action, such as scheduling, processing, and updating records, within the conversation itself.\n- Always-on monitoring: Agents should continuously evaluate the quality of interactions and catch failures before they become customer complaints\n- Intelligent routing: Agents should escalate issues with full context so that humans can pick up where they left off.\n- Real-time contextual data: Agents should have the same customer context as a well-prepared human agent. This includes purchase history, past interactions, account status, and preferences.\nNone of these are speculative. They’re available today, and the companies deploying them are already seeing the difference.\nCase in point: OhMD, a healthcare communications platform for physician practices and medical groups. The company built Nia, an AI-powered voice assistant that uses Twilio’s Conversation Relay to handle routine patient calls (scheduling, prescription refills, FAQs). Complex calls are routed to staff with full context, which saves patients from repeating themselves.\nThe results were immediate. OhMD saw a 60% improvement in self-service first-call resolution, with appointment scheduling flows completing in as little as one minute. By 2026, Nia is projected to handle more than 55 million patient interactions annually.\nAs Twilio CEO Khozema Shipchandler noted, “What we’re starting to see with OhMD is that they’ve got a 60% lift in self-serve capability to actually resolve calls. They’re able to drive the conclusion of these calls in less than a minute in many instances.”\nPatients aren’t impressed because the phone rang once. They’re impressed because the call ended with their problem solved.\nThink resolution, not speed\nFor every customer service leader evaluating their AI agent roadmap, the implication is straightforward. Stop measuring success by response time alone. Start measuring it by resolution rate—specifically, self-service resolution rate.\nThat means investing not in faster replies, but in smarter infrastructure: agents that act, routing that adapts, data that flows in real time, and monitoring that holds the system accountable.\nThe future of customer service isn’t about answering faster. It’s about answering fully.\nTo learn more about Twilio, visit here.","image_url":"https://www.cio.com/wp-content/uploads/2026/07/4194919-0-03469500-1783929975-Article5-WhyTheFuture_shutterstock_2748143891.jpg?quality=50&strip=all&w=1024","lang":"en","published_at":"2026-07-13T08:06:58+00:00","fetched_at":"2026-07-13T10:15:04+00:00","status":"read","starred":0,"extract_state":"ok","summary_auto":"What the data reveals about the gap between speed and satisfaction, and how businesses can redesign AI agents to close it. 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And on those metrics, they’re delivering.\nIt’s no surprise then that 90% of business leaders believe their customers are satisfied with conversational AI experiences. Yet only 59% of consumers agree, according to Twilio’s latest report on conversational AI.\nWhat could explain this 31-point gap? The data is unambiguous. 54% of consumers say AI agents rarely have context about them as a customer. 78% say the ability to escalate to a human is important, yet few get the chance to do so. And only 15% report experiencing a seamless handoff from an AI agent to a human one.\nAll this to say that the real measure of an AI agent isn’t how fast it answers, but whether it solves the problem. In fact, 72% of consumers would choose an AI over a human if it could resolve their issue more quickly.\nSpeed without resolution will only result in frustration.\nWhy most AI agents aren’t great at resolution\nIt’s not hard to see why most AI agents fail to resolve customer issues: they can’t take action on behalf of the customer, they can’t escalate conversations when they reach their limits, and they lack the real-time context needed to personalise the interaction.\nThink about what a typical AI service interaction looks like. A customer calls with a billing question. The AI agent reads their intent accurately enough. But it can’t pull up the customer’s account in real time, process a credit, or route to a human specialist who knows the context of the conversation. So the customer repeats themselves. Or simply gives up.\nThe root cause is structural. In most stacks, the channel is the system of record, not the conversation. Voice, SMS, chat, and WhatsApp each run as separate sessions, so the moment a customer switches channels or escalates to a human, the interaction resets. Engineering teams paper over this by stuffing full transcripts into AI prompts to fake continuity. This inflates token costs, slows responses, and still truncates older context once the window fills up.\nThis is the gap between a chatbot and an agent. A chatbot responds. An agent resolves. It’s no wonder that the 59% of organisations planning to fully replace their current conversational AI solution within the year understand this distinction. Their early investments were simply optimised for the wrong outcome.\nA smarter agent only gets you so far. Businesses need four capabilities to close the resolution gap:\n- Agency: Agents must be able to take real action, such as scheduling, processing, and updating records, within the conversation itself.\n- Always-on monitoring: Agents should continuously evaluate the quality of interactions and catch failures before they become customer complaints\n- Intelligent routing: Agents should escalate issues with full context so that humans can pick up where they left off.\n- Real-time contextual data: Agents should have the same customer context as a well-prepared human agent. This includes purchase history, past interactions, account status, and preferences.\nNone of these are speculative. They’re available today, and the companies deploying them are already seeing the difference.\nCase in point: OhMD, a healthcare communications platform for physician practices and medical groups. The company built Nia, an AI-powered voice assistant that uses Twilio’s Conversation Relay to handle routine patient calls (scheduling, prescription refills, FAQs). Complex calls are routed to staff with full context, which saves patients from repeating themselves.\nThe results were immediate. OhMD saw a 60% improvement in self-service first-call resolution, with appointment scheduling flows completing in as little as one minute. By 2026, Nia is projected to handle more than 55 million patient interactions annually.\nAs Twilio CEO Khozema Shipchandler noted, “What we’re starting to see with OhMD is that they’ve got a 60% lift in self-serve capability to actually resolve calls. They’re able to drive the conclusion of these calls in less than a minute in many instances.”\nPatients aren’t impressed because the phone rang once. They’re impressed because the call ended with their problem solved.\nThink resolution, not speed\nFor every customer service leader evaluating their AI agent roadmap, the implication is straightforward. Stop measuring success by response time alone. Start measuring it by resolution rate—specifically, self-service resolution rate.\nThat means investing not in faster replies, but in smarter infrastructure: agents that act, routing that adapts, data that flows in real time, and monitoring that holds the system accountable.\nThe future of customer service isn’t about answering faster. It’s about answering fully.\nTo learn more about Twilio, visit here.","excerpt":"What the data reveals about the gap between speed and satisfaction, and how businesses can redesign AI agents to close it. 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And only 15% report experiencing a seamless handoff from an AI agent to a human one.\nAll this to say that the real measure of an AI agent isn’t how fast it answers, but whether it solves the problem. In fact, 72% of consumers would choose an AI over a human if it could resolve their issue more quickly.\nSpeed without resolution will only result in frustration.\nWhy most AI agents aren’t great at resolution\nIt’s not hard to see why most AI agents fail to resolve customer issues: they can’t take action on behalf of the customer, they can’t escalate conversations when they reach their limits, and they lack the real-time context needed to personalise the interaction.\nThink about what a typical AI service interaction looks like. A customer calls with a billing question. The AI agent reads their intent accurately enough. But it can’t pull up the customer’s account in real time, process a credit, or route to a human specialist who knows the context of the conversation. So the customer repeats themselves. Or simply gives up.\nThe root cause is structural. In most stacks, the channel is the system of record, not the conversation. Voice, SMS, chat, and WhatsApp each run as separate sessions, so the moment a customer switches channels or escalates to a human, the interaction resets. Engineering teams paper over this by stuffing full transcripts into AI prompts to fake continuity. This inflates token costs, slows responses, and still truncates older context once the window fills up.\nThis is the gap between a chatbot and an agent. A chatbot responds. An agent resolves. It’s no wonder that the 59% of organisations planning to fully replace their current conversational AI solution within the year understand this distinction. Their early investments were simply optimised for the wrong outcome.\nA smarter agent only gets you so far. Businesses need four capabilities to close the resolution gap:\n- Agency: Agents must be able to take real action, such as scheduling, processing, and updating records, within the conversation itself.\n- Always-on monitoring: Agents should continuously evaluate the quality of interactions and catch failures before they become customer complaints\n- Intelligent routing: Agents should escalate issues with full context so that humans can pick up where they left off.\n- Real-time contextual data: Agents should have the same customer context as a well-prepared human agent. This includes purchase history, past interactions, account status, and preferences.\nNone of these are speculative. They’re available today, and the companies deploying them are already seeing the difference.\nCase in point: OhMD, a healthcare communications platform for physician practices and medical groups. The company built Nia, an AI-powered voice assistant that uses Twilio’s Conversation Relay to handle routine patient calls (scheduling, prescription refills, FAQs). Complex calls are routed to staff with full context, which saves patients from repeating themselves.\nThe results were immediate. OhMD saw a 60% improvement in self-service first-call resolution, with appointment scheduling flows completing in as little as one minute. By 2026, Nia is projected to handle more than 55 million patient interactions annually.\nAs Twilio CEO Khozema Shipchandler noted, “What we’re starting to see with OhMD is that they’ve got a 60% lift in self-serve capability to actually resolve calls. They’re able to drive the conclusion of these calls in less than a minute in many instances.”\nPatients aren’t impressed because the phone rang once. 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Most AI agents today are optimised for responsiveness—faster first responses, shorter wait times, higher service rates.","full_text":"What the data reveals about the gap between speed and satisfaction, and how businesses can redesign AI agents to close it.\nMost AI agents today are optimised for responsiveness—faster first responses, shorter wait times, higher service rates. And on those metrics, they’re delivering.\nIt’s no surprise then that 90% of business leaders believe their customers are satisfied with conversational AI experiences. Yet only 59% of consumers agree, according to Twilio’s latest report on conversational AI.\nWhat could explain this 31-point gap? The data is unambiguous. 54% of consumers say AI agents rarely have context about them as a customer. 78% say the ability to escalate to a human is important, yet few get the chance to do so. And only 15% report experiencing a seamless handoff from an AI agent to a human one.\nAll this to say that the real measure of an AI agent isn’t how fast it answers, but whether it solves the problem. In fact, 72% of consumers would choose an AI over a human if it could resolve their issue more quickly.\nSpeed without resolution will only result in frustration.\nWhy most AI agents aren’t great at resolution\nIt’s not hard to see why most AI agents fail to resolve customer issues: they can’t take action on behalf of the customer, they can’t escalate conversations when they reach their limits, and they lack the real-time context needed to personalise the interaction.\nThink about what a typical AI service interaction looks like. A customer calls with a billing question. The AI agent reads their intent accurately enough. But it can’t pull up the customer’s account in real time, process a credit, or route to a human specialist who knows the context of the conversation. So the customer repeats themselves. Or simply gives up.\nThe root cause is structural. In most stacks, the channel is the system of record, not the conversation. Voice, SMS, chat, and WhatsApp each run as separate sessions, so the moment a customer switches channels or escalates to a human, the interaction resets. Engineering teams paper over this by stuffing full transcripts into AI prompts to fake continuity. This inflates token costs, slows responses, and still truncates older context once the window fills up.\nThis is the gap between a chatbot and an agent. A chatbot responds. An agent resolves. It’s no wonder that the 59% of organisations planning to fully replace their current conversational AI solution within the year understand this distinction. Their early investments were simply optimised for the wrong outcome.\nA smarter agent only gets you so far. Businesses need four capabilities to close the resolution gap:\n- Agency: Agents must be able to take real action, such as scheduling, processing, and updating records, within the conversation itself.\n- Always-on monitoring: Agents should continuously evaluate the quality of interactions and catch failures before they become customer complaints\n- Intelligent routing: Agents should escalate issues with full context so that humans can pick up where they left off.\n- Real-time contextual data: Agents should have the same customer context as a well-prepared human agent. This includes purchase history, past interactions, account status, and preferences.\nNone of these are speculative. They’re available today, and the companies deploying them are already seeing the difference.\nCase in point: OhMD, a healthcare communications platform for physician practices and medical groups. The company built Nia, an AI-powered voice assistant that uses Twilio’s Conversation Relay to handle routine patient calls (scheduling, prescription refills, FAQs). Complex calls are routed to staff with full context, which saves patients from repeating themselves.\nThe results were immediate. OhMD saw a 60% improvement in self-service first-call resolution, with appointment scheduling flows completing in as little as one minute. By 2026, Nia is projected to handle more than 55 million patient interactions annually.\nAs Twilio CEO Khozema Shipchandler noted, “What we’re starting to see with OhMD is that they’ve got a 60% lift in self-serve capability to actually resolve calls. They’re able to drive the conclusion of these calls in less than a minute in many instances.”\nPatients aren’t impressed because the phone rang once. They’re impressed because the call ended with their problem solved.\nThink resolution, not speed\nFor every customer service leader evaluating their AI agent roadmap, the implication is straightforward. Stop measuring success by response time alone. Start measuring it by resolution rate—specifically, self-service resolution rate.\nThat means investing not in faster replies, but in smarter infrastructure: agents that act, routing that adapts, data that flows in real time, and monitoring that holds the system accountable.\nThe future of customer service isn’t about answering faster. 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Most AI agents today are optimised for responsiveness—faster first responses, shorter wait times, higher service rates.","source":"cio.com","date":"2026-07-13T08:06:58+00:00","content":"What the data reveals about the gap between speed and satisfaction, and how businesses can redesign AI agents to close it.\nMost AI agents today are optimised for responsiveness—faster first responses, shorter wait times, higher service rates. And on those metrics, they’re delivering.\nIt’s no surprise then that 90% of business leaders believe their customers are satisfied with conversational AI experiences. Yet only 59% of consumers agree, according to Twilio’s latest report on conversational AI.\nWhat could explain this 31-point gap? The data is unambiguous. 54% of consumers say AI agents rarely have context about them as a customer. 78% say the ability to escalate to a human is important, yet few get the chance to do so. And only 15% report experiencing a seamless handoff from an AI agent to a human one.\nAll this to say that the real measure of an AI agent isn’t how fast it answers, but whether it solves the problem. In fact, 72% of consumers would choose an AI over a human if it could resolve their issue more quickly.\nSpeed without resolution will only result in frustration.\nWhy most AI agents aren’t great at resolution\nIt’s not hard to see why most AI agents fail to resolve customer issues: they can’t take action on behalf of the customer, they can’t escalate conversations when they reach their limits, and they lack the real-time context needed to personalise the interaction.\nThink about what a typical AI service interaction looks like. A customer calls with a billing question. The AI agent reads their intent accurately enough. But it can’t pull up the customer’s account in real time, process a credit, or route to a human specialist who knows the context of the conversation. So the customer repeats themselves. Or simply gives up.\nThe root cause is structural. In most stacks, the channel is the system of record, not the conversation. Voice, SMS, chat, and WhatsApp each run as separate sessions, so the moment a customer switches channels or escalates to a human, the interaction resets. Engineering teams paper over this by stuffing full transcripts into AI prompts to fake continuity. This inflates token costs, slows responses, and still truncates older context once the window fills up.\nThis is the gap between a chatbot and an agent. A chatbot responds. An agent resolves. It’s no wonder that the 59% of organisations planning to fully replace their current conversational AI solution within the year understand this distinction. Their early investments were simply optimised for the wrong outcome.\nA smarter agent only gets you so far. Businesses need four capabilities to close the resolution gap:\n- Agency: Agents must be able to take real action, such as scheduling, processing, and updating records, within the conversation itself.\n- Always-on monitoring: Agents should continuously evaluate the quality of interactions and catch failures before they become customer complaints\n- Intelligent routing: Agents should escalate issues with full context so that humans can pick up where they left off.\n- Real-time contextual data: Agents should have the same customer context as a well-prepared human agent. This includes purchase history, past interactions, account status, and preferences.\nNone of these are speculative. They’re available today, and the companies deploying them are already seeing the difference.\nCase in point: OhMD, a healthcare communications platform for physician practices and medical groups. The company built Nia, an AI-powered voice assistant that uses Twilio’s Conversation Relay to handle routine patient calls (scheduling, prescription refills, FAQs). Complex calls are routed to staff with full context, which saves patients from repeating themselves.\nThe results were immediate. OhMD saw a 60% improvement in self-service first-call resolution, with appointment scheduling flows completing in as little as one minute. By 2026, Nia is projected to handle more than 55 million patient interactions annually.\nAs Twilio CEO Khozema Shipchandler noted, “What we’re starting to see with OhMD is that they’ve got a 60% lift in self-serve capability to actually resolve calls. They’re able to drive the conclusion of these calls in less than a minute in many instances.”\nPatients aren’t impressed because the phone rang once. They’re impressed because the call ended with their problem solved.\nThink resolution, not speed\nFor every customer service leader evaluating their AI agent roadmap, the implication is straightforward. Stop measuring success by response time alone. Start measuring it by resolution rate—specifically, self-service resolution rate.\nThat means investing not in faster replies, but in smarter infrastructure: agents that act, routing that adapts, data that flows in real time, and monitoring that holds the system accountable.\nThe future of customer service isn’t about answering faster. 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