These 3 AI trends will improve your customer service in 2026

Customer service is evolving rapidly under the influence of AI. But not every promise lives up to expectations. Buzzwords are everywhere, while real added value sometimes fails to materialise. In 2026, it's no longer about experimenting with standalone tools, but about creating tangible value for customers and employees alike.

So what remains in 2026 once the hype has subsided? In this blog, we explore three key AI trends in customer contact, separating marketing talk from practical reality. You’ll learn what agentic AI really is, which AI applications genuinely move contact centres forward, and why human support remains indispensable for trust and customer satisfaction.

This will give you clear insight into how to deploy AI strategically for better customer experiences (CX) and an improved employee experience (EX). Shall we dive in?

Trend 1: Agentic AI – marketing buzzword or real added value?

Agentic AI received a great deal of attention in 2025 and was positioned as the next big revolution in customer contact. Perhaps rightly so, but the challenge for 2026 is to turn hype into real, measurable value. And what exactly is agentic AI? We increasingly see all kinds of automation processes labelled as “agentic AI”, even when they don’t belong in that category. That is why it is important to clearly distinguish between different levels of automation. Below is a brief overview.

What is Robotic Process Automation (RPA)?

RPA automates repetitive, rule-based tasks such as re-entering data, processing invoices or copying information between systems. It works according to fixed rules and steps, has no understanding of context or language, and does exactly what it is programmed to do – no more, no less.

What does a chatbot or voicebot do?

A bot performs predefined, script-based tasks via chat or voice. It understands language to a certain extent using NLP, but lacks genuine decision-making capabilities. As a result, it is usually limited to simple dialogues. Useful for FAQs, status queries and straightforward actions.

What is agentic AI?

Agentic AI uses AI agents. An AI agent is goal-oriented, understands context and makes independent decisions. It adapts its approach when situations change, creates plans and executes multiple steps autonomously. It collaborates flexibly with people and switches smoothly between tasks to resolve issues.

When does agentic AI move beyond the hype?

In customer contact, AI agents represent a major step forward compared to traditional chatbots. Think of virtual assistants that not only answer simple questions, but handle complete customer requests end-to-end or proactively propose solutions. But how do you achieve that in practice? How do you turn the hype into tangible results for your customers and employees?

Agentic AI only delivers real value when deployed strategically: in situations where decisions need to be made during customer interactions. The focus should be on increasing productivity at organisational level, not merely speeding up individual tasks.

This requires a strong business case that solves a genuine customer problem, reliable data and well-considered governance. In addition, AI agents must operate safely within clearly defined boundaries. Human oversight and the ability to intervene must always be in place.

Without this foundation, agentic AI remains little more than an appealing label – not a real improvement in customer service.

Trend 2: The rise of new AI use cases

We're all familiar with chat- and voicebots by now – enough has been said and written about them. Fortunately, AI can be deployed for so much more, and that's precisely where this year's focus lies. Recent research from Gartner¹⁾ shows that in 2026, service organisations are investing primarily in four key areas for AI in customer contact:

1.      Agent support – such as next-best-actions, automatic transcription and conversation summarisation, real-time translation, training and coaching, or improving call quality by neutralising accents and filtering background noise

2.      Customer self-service – including personalised product recommendations, relevant help articles or tutorials, automated knowledge management, intelligent search functionality that understands context and intent, automation of self-service processes, or proactive support

3.      Operational optimisation – for example, real-time conversation analysis to identify customer intent and sentiment, intelligent call routing, quality monitoring, scheduling optimisation, automatic ticket classification, or bottleneck prediction

4.      Agentic AI – including automated execution of complete workflows, autonomous handling of service requests, proactive issue detection and resolution, or task prioritisation and adaptive agent-assist

How can I apply AI practically in the contact centre?

The broad application of AI in customer contact was once a distant prospect, but is now everyday reality. For instance, a company like Proximus has already implemented 213 (!) AI use cases, ranging from automatic classification of customer enquiries to a writing assistant that simplifies bilingual communication, and automated coaching for employees²⁾.

The possibilities are endless, so let your imagination run wild! At the same time, focus is essential. Prioritise use cases that demonstrably add value and deploy AI where it makes the biggest impact – so both customers and employees benefit.

^{1) Gartner Newsroom}

^{2) Ziptone – report from the Customer Contact & CXForum annual conference}