November 06, 2025
How Award-Winning Brands Use AI for Customer Service: Real Examples from Winners
A few years ago, “AI in customer service” meant a chatbot that got stumped by basic questions and a queue that never ended. We’ve all been there, typing “speak to an agent” like our lives depended on it.
The brands pulling ahead today are the ones that use AI for customer service with real intention. They’re not chasing automation just to tick a box. They’re using it to understand people faster, pick up on emotion sooner, and give their teams the kind of insight that makes every interaction count.
Customer experience has become a full-contact sport. People want answers, empathy, and zero effort, all at once. Right now, 40% of leaders say scaling service is their top priority. AI is an obvious way to do that, yet 64% of consumers still say they’d rather talk to a person. The catch? When companies use AI for customer service the right way, customers don’t even notice it’s there.
This is the new shape of AI in customer experience: predictive, conversational, and emotionally intelligent. It doesn’t replace humans; it supports them. It’s the growing force behind faster resolutions and smoother journeys, which is what great CX should be.
How Companies Can Use AI for Customer Service Today
The first wave of companies to use AI for customer service did what any organization testing new technology does: they overpromised and underdelivered. Chatbots answered the easy stuff and collapsed under anything remotely complex.
That’s changed. The new generation of AI in customer experience has matured. It listens, learns, and acts with context. It doesn’t replace the human agent; it clears the noise around them. In 2025, the smartest service teams don’t see AI as a sidekick. They see it as the connective tissue between data, emotion, and real-time action. Two thirds of companies even think AI can help them provide more of the warm and familiar human service that builds loyalty.
The main use cases right now:
Conversational AI and Virtual Assistants
The most visible transformation starts at the front line. Conversational AI is now a digital voice that adapts to mood and meaning.
Brands like E.ON and Ageas Insurance now use AI for customer service that runs 24/7 on chat, voice, social, whatever the channel. The difference is that these bots actually sound like the company. The tone is calm, reassuring, and familiar. For customers, it feels seamless. For teams, it’s breathing room.
AI Beside the Human, Not in Front of Them
In leading companies, AI doesn’t take over the conversation; it sits beside the agent. Agent real-time assist technology listens in, summarizing, flagging sentiment, and suggesting next steps on the fly. Agents stay human, but sharper, faster, more focused. It’s the new balance of empathy plus insight. The AI whispers, humans deliver.
Service That Predicts, Not Reacts
The next step is foresight. Octopus Energy’s AI has a knack for reading the room. It notices when something’s off, like a bill that suddenly jumps, and reaches out before the customer even thinks to complain. A quick message, a clear explanation, and the tension never builds. That’s the real power when you use AI for customer service the right way: it helps people feel seen, not managed.
Finding the Hidden Friction
Behind the scenes, there’s always quiet work happening, the kind nobody talks about. The data cleanup. The process tune-ups. The small fixes that make everything else smoother. British Gas used AI to dig through millions of complaint logs and found the real story hiding underneath: little issues that, left alone, grew into big problems.
The Rise of the “Doer” AI
The next evolution is already here: agentic AI. Platforms like Dialpad are pioneering systems that don’t just respond, they act. They can open tickets, trigger workflows, send confirmations, and even handle multi-step resolutions on their own. It’s still early, but it’s the bridge between automation and autonomy, where service gets dealt with before a human even logs in.
Listening Between the Lines
Not all feedback is explicit. Modern sentiment analytics can detect tone, emotion, and urgency across channels, spotting a frustrated tweet before it spirals or identifying a happy customer ripe for upsell. Tools like Medallia are leading here, proving that true AI in CX isn’t about words, it’s about meaning.
Earning Trust, Every Time
Even with all the opportunities, AI still has an image problem. 41% of UK consumers say they’d only accept AI support if it came with a discount. That’s a trust issue, not a tech one.
The fix? Be transparent. Tell customers when AI is involved. Let them reach a human without friction. Above all, design systems that feel emotionally intelligent, not automated. Because when AI serves, not sells, people stop caring whether it’s human or machine. They just know it works.
Case Studies: How Winning Use AI for Customer Service
You can spot the difference between brands that dabble in AI and those that accept it as the future. The latter don’t bolt on new tools; they rebuild around them. They’ve figured out how to use AI for customer service without losing what makes their experience human: empathy, tone, and trust.
Some of the best examples come straight from the winners of the UK Customer Experience Awards – companies like Vodafone, Octopus, and HSBC.
Vodafone / VOXI: AI That Speaks Gen Z
Vodafone had a Gen Z problem. VOXI, its youth-focused brand, was growing fast, but so were customer expectations. This audience didn’t want to “submit a query”; they wanted a real-time, two-way chat with something that sounded like them.
Vodafone teamed up with Accenture to build a generative AI assistant that could actually talk like Gen Z. It studied real chats, learned the brand’s personality, and made digital conversations feel quick and human. The impact was instant: response times cut by half, support costs down 30 percent, and satisfaction scores soaring across the board.
Lesson learned: when you use AI for customer service, voice matters. The tech should sound like your brand, not like a bot.
NatWest: Smarter, Safer, More Human Banking
When NatWest’s contact centers were hit by a surge in post-pandemic demand, the challenge wasn’t just speed; it was consistency. Every call mattered, especially in an industry built on trust.
NatWest brought in IBM’s Watson to power a two-tier AI system—one to help customers and one to guide agents. The virtual assistants handled routine questions, while an “agent assist” feature quietly offered prompts, insights, and next steps in real time.
Almost 60 percent of queries were resolved automatically, handling times dropped sharply, and the team’s stress levels finally eased. Everyone felt the difference.
HSBC: Human Empathy at Global Scale
HSBC’s challenge was scale, millions of customers, dozens of countries, and a single brand promise to deliver consistent, human service everywhere. You can’t fake empathy at that level.
To bridge the gap, HSBC worked with Genesys and LivePerson to design a new AI layer that learned from agents. They called it “agent-built bots”, AI trained directly by frontline staff who understood what real conversations sounded like. The result was a system steeped in genuine tone and context, not generic chatbot replies.
It worked, transfers regarding complaints dropped by 32%, supervisors saved two hours a day, and within 3 years, HSBC thinks the pay-off will be around £60 million.
British Gas: Fixing the Root Cause, Not the Symptom
British Gas doesn’t just want to handle complaints faster; it wants fewer complaints in the first place. But that meant understanding the “why” behind them, buried deep inside years of feedback data.
Working with Publicis Sapient, the company created an AI-powered system to dig through millions of records and spot patterns that people had been missing. The platform didn’t rely on guesswork; it used data to find the real causes behind customer frustration. Hidden inside were minor billing errors, broken processes, and tiny oversights that, together, created a lot of unnecessary friction.
Once those root issues were fixed, the impact was immediate: fewer calls (15% less), fewer complaints, and resolution times cut by almost half. Customers noticed the difference. So did employees, who spent less time apologizing and more time helping.
3.5 Octopus Energy: Proactive Energy for People
Octopus Energy has a bit of a rebel streak, which makes it a great example of how companies use AI for customer service well. While most energy providers were scrambling to react to customer complaints during the energy crisis, Octopus was already one step ahead, literally.
On the Kraken platform, something interesting started happening. The system began noticing patterns, and it reached out before anyone picked up the phone: just a short message, sometimes an explanation, sometimes reassurance.
Problems stopped snowballing because customers felt seen early. It’s a simple example of how you can use AI for customer service without losing the human touch. Costs went down by about forty percent, but what really changed was tone. The brand felt warmer. Agents weren’t stuck reading scripts anymore; they were having honest conversations again.
E.ON: Conversational Consistency Across Markets
E.ON had a familiar problem for global brands: too many countries, too many systems, and far too many ways of saying “hello.” The result? A customer experience that felt more like a patchwork than a brand.
So E.ON went back to the drawing board. Working with Cognigy, it built a multilingual conversational AI that didn’t just translate, it understood. The system could switch effortlessly between languages, accents, and cultural nuances, bringing a single, recognizable E.ON voice to every market.
Behind the scenes, that meant something powerful: all those separate teams were now learning together. Data from one country improved the service in another. AI in CX turned a scattered operation into a connected one.
Today, E.ON has found a way to use AI for customer service for more than 2 million conversations annually, while preserving a human touch.
Talkmobile: Scaling with Smart Conversations
Talkmobile isn’t a big name like Vodafone or EE, and that’s exactly what makes its story interesting. With a small team and a loyal but demanding customer base, Talkmobile had to find a way to deliver “big brand” service on a lean setup.
They didn’t chase headcount; they leaned on brains. Partnering with LivePerson, Talkmobile trained conversational AI to handle routine chats with real conversation flow. The AI picked up on tone, learned phrasing, and could sense when a message needed a softer touch or a faster answer.
Pretty soon, the impact showed. CSAT increased to 92%, NPS to 66, and first contact resolution rates to 89%. Agents stopped firefighting and started solving real problems.
Ageas Insurance: From Wait Times to Real Time
Insurance calls usually start with stress. Someone’s had an accident, lost something, or needs help fast. Ageas knew those moments mattered, but as the company scaled, queues were growing with too.
Enter Boost.ai. Working closely with Ageas, they built a conversational AI that could listen, understand, and act in real time. Instead of throwing customers into endless menu loops, it spoke naturally, handling the simple stuff instantly and passing the emotional moments straight to a human.
Within weeks, wait times dropped. 77% of questions were handled automatically, on the first try, and agents finally had room to breathe. Ageas proved you can use AI for customer service without losing empathy, or locking customers into robotic conversations.
Loveholidays: Always-On Travel Service
Travel brands live and die by timing. A single flight change or lost booking can turn excitement into frustration fast, and Loveholidays was feeling that pressure. Customer queries were flooding in, especially during seasonal peaks, and human agents couldn’t keep up.
Loveholidays turned to Google Cloud’s CCAI and Dialogflow to build virtual assistants that could talk to thousands of travelers at once, around the clock, without losing that personal feel. The AI understood intent, offered quick answers, and passed people to a live agent when human help was needed.
Now, the system handles millions of questions each year, everything from hotel changes to refund requests, while freeing up human teams to deal with complex cases.
Barchester Healthcare: Empathy at Scale
Families reaching out to Barchester Healthcare weren’t chasing updates, they were looking for reassurance. Yet as the organization grew, feedback was getting buried. Teams were collecting data but missing emotion.
So Barchester turned to CustomerSure’s AI-driven feedback platform. It analyzed written comments, survey responses, and messages in real time, flagging issues that carried emotional urgency. Managers could see what mattered most, and act on it fast.
The changes were visible: NPS climbed, response times fell, and staff said they felt more connected to residents’ families than before.
L’Oréal: Personalised AI Beauty Consultations
L’Oréal faced a challenge that every digital brand understands: how do you make online shopping feel personal? After the pandemic, customers still craved that one-on-one beauty advice.
So L’Oréal launched Noli, an AI platform that combines image recognition with natural conversation. Shoppers upload a photo, share their goals, and get product advice that actually feels like it came from someone who sees them.
The results spoke for themselves. Engagement jumped, returns dropped, and customers said the virtual experience felt almost as personal as an in-store one. It’s a brilliant example of how to use AI for customer service that connects on an emotional level. L’Oréal didn’t replace human beauty advisors, it scaled their intuition.
Gap: Threading AI Through Every Corner of Retail
Retail moves fast, sometimes too fast. One missed email, a delayed delivery, or a product that’s suddenly out of stock can lose a loyal customer overnight. Gap saw it happening early. Instead of patching over problems, the team rebuilt from the inside.
With Google Cloud, they wove AI into everything, from inventory to customer service. The system doesn’t wait for trouble; it predicts it. When an order’s delayed or mis-scanned, the fix is already underway before the shopper even asks where it is.
The change was noticeable. Service contacts dropped, response times got faster, and shoppers said the experience finally felt easy. The kind of easy that keeps people coming back without thinking about it.
Common Threads: What the Best Brands Get Right
After digging through all these stories, one thing’s obvious: the best brands don’t talk about AI the way everyone else does. They start with frustration, customers waiting too long, agents burned out, teams stuck fixing the same issue for the hundredth time. While all of these companies use AI for customer service in different ways, they also all:
- Start with the pain, not the platform: Every winning example began with a clear problem. Vodafone wanted faster answers for Gen Z. British Gas wanted fewer complaints. Barchester wanted more data it could use to improve the experience for patients.
- Mix logic with empathy.: The smartest companies don’t replace people; they make them more powerful. Octopus Energy’s AI predicts when someone might struggle with a bill, so a human can step in early. That’s AI in customer experience done right.
- Keep AI alive: None of these systems are “set and forget.” They evolve. Teams train them, tweak them, and let them grow. That’s how you use AI for customer service that actually gets better instead of just faster.
- Go deeper than surface metrics: Anyone can count call times or ticket closures. The real wins come from listening to what customers feel. Barchester Healthcare used sentiment analysis not to score performance but to understand emotion. That’s what changed their culture.
- Don’t let the data lie: Clean, well-governed data keeps AI honest. Bad data breeds bad judgment. The best brands protect trust like currency, and that’s what makes their automation credible.
Maybe the simplest truth of all? People still matter. A lot. Gartner predicts every Fortune 500 company will still rely on human service by 2028. Technology can scale empathy, but it can’t replace it.
How to Use AI for Customer Service: The Right Way
Right now, every company wants to use AI for customer service, but only a handful of them actually get it right. The examples above show what can happen when organizations mix the efficiency and scale of AI with the true power of human empathy and intuition.
Get that balance right, and brands report stronger loyalty, lower costs, and happier teams. Everybody wins. That’s the evolution. AI isn’t replacing service; it’s redefining it. The future of AI in CX won’t be about technology that talks, it’ll be about technology that listens.
The brands that thrive next year, and the year after, will be the ones turning every customer interaction into a chance to prove what good service actually means, with humans and AI working in unison.
