What AI really means for customer service

One major shift is that AI no longer lives only within innovation teams — it has made its way into day-to-day operations. While many organizations were still running pilots and proof-of-concepts last year, we now increasingly see AI being used structurally. Chatbots are the first point of contact on websites and in apps. Voicebots are handling full conversations without human intervention. Agent-assist tools are helping service reps in real time by sorting information, suggesting answers, or summarizing calls.

AI is no longer a standalone project or a hype. More and more, it’s seen as an extension of the customer service team — a tool that supports, streamlines and accelerates, when implemented properly.

AI excels in tasks that are repetitive and predictable. A chatbot that answers the same question hundreds of times a day (about delivery times or return policies) is a perfect fit. AI also performs well in high-volume environments, multilingual contexts, or outside office hours. Speed, availability, and consistency are key here.

But AI still has limitations. When conversations become complex or emotional, the technology (for now) struggles. Angry customers, people expressing doubt, or those asking multiple questions at once can still throw AI off. And AI still lacks true empathy, the human ability to sense how something should be said. In those moments, human interaction remains essential.

So AI is not a replacement, but a complement. The challenge lies in smartly dividing responsibilities: what do you let AI handle, and what belongs to the human service team?

The advantages of AI in customer service have not only become clearer over the past year, they’ve become more tangible. Thanks to breakthroughs in generative AI and real-time language models, chat and voicebots are now more fluent than ever. They better understand context, can switch between topics, and are increasingly capable of interpreting a customer’s intent. This leads to faster resolution times, improved satisfaction for simpler queries, and significantly reduced workload for employees.

During peak periods, organizations can scale up effortlessly without needing to hire or train new staff. For international service teams, AI offers instant multilingual support and consistent quality across channels and countries. In short: AI has become a powerful enabler of availability, efficiency, and customer experience. As long as it’s used wisely.

At the same time, new drawbacks have emerged. As organizations become more dependent on AI systems, the risk of over-automation grows — shifting too many tasks to systems that are still fallible. Hallucinations (AI generating false or misleading information) remain a real risk, especially with complex queries. Customers are also increasingly asking for human contact, precisely because the line between real and artificial is blurring.

This makes transparency, control, and trust more important than ever. On top of that, AI introduces new responsibilities in areas like data privacy, governance, and compliance. Systems must be properly trained, monitored, and legitimized. Implementing AI isn’t just about tech. It’s about ethics, oversight, and good governance. Organizations that ignore this run the risk of reputational damage or customer loss.

As AI matures, so do the concerns around it. Customers more often want to know whether they’re speaking to a human or a machine and in many cases, they have a right to know. Transparency is no longer a nice-to-have; it’s a must. The upcoming EU AI Act underscores this: companies must clearly inform users when AI is being used, and high-risk or deceptive applications are prohibited.

Internally, AI also raises important questions. Who is responsible when an AI system makes a mistake? How do you ensure that employees understand how and why AI comes to certain suggestions? And how do you prevent bad data from leading to bad decisions?

These questions force organizations to approach AI with more awareness and care. Not just from an ethical standpoint, but also in terms of risk and reputation management.

What many organizations still underestimate is that AI is not just a technological change, it’s a cultural one. Implementing AI doesn’t only require new tools, but also new ways of thinking and working. Employees must learn to collaborate with a digital colleague. Leaders must be willing to let go of control and trust automated processes. And customers must adapt to new forms of interaction.

This transformation takes time and guidance. The companies leading the way today aren’t necessarily the ones with the biggest budgets or flashiest tools. They’re the ones investing in adoption, training, and ongoing feedback.

AI in customer service is growing up. Not by replacing people, but by strategically supporting them. Not as a gimmick, but as a structural part of a better customer experience. The hype is fading. What remains is a technology with real impact — if it’s used thoughtfully, responsibly, and with the right intent.

So the question is no longer whether you should use AI, but where, how, and for what purpose.