Cinco tendências que estão moldando a experiência do cliente em 2026

1. Experiences become the strategic differentiator.

We’re no longer in a service economy where organizations can largely focus on operational efficiency. In today’s experience economy, customer expectations are higher than ever before. In fact, 30% of consumers say they’ll abandon a brand after just one poor experience. Organizations need to do more than be empathetic. They must learn how to compete and succeed in an economy where experience drives trust, loyalty and growth.

This isn’t about managing AI point solutions and channels; it’s about designing and managing experiences just like you would a product. Successful organizations consider what kind of experience they’d like to provide — effective, efficient and personalized from the customer’s point of view — and then deploy the right technologies to provide that end-to-end experience. And they use an AI strategy that encompasses their data, company knowledge and systems.

“What we see happening is that as the industry matures and as AI matures, delivering an end-to-end experience on top of a hodge-podge of different technologies is just extremely difficult,” said Peter Graf, Senior VP of Strategy at Genesys. “And so we see leaders go and standardize across a couple of strategic platforms in their organization.”

Peter Graf

Senior VP of Strategy, Genesys

2. Humans and agentic AI, working together.

Agentic technologies are redefining what AI-powered systems can accomplish in CX: Understanding context, moving autonomously and acting toward goals. Agentic AI is truly different from previous technologies, with an ability to take action and drive towards business goals. It’s also an opportunity for organizations to reassess their overall AI strategies and ensure they’ve got a clear view on how to measure the value and ROI of AI in CX

“I think we’ve come to the point in this AI maturation process that there is not a one-size-fits-all model,” said Alex Ball, Senior VP of Genesys Cloud. “There’s not going to be one enterprise LLM or model that solves everything, so you need to have systems that work together and that interact with each other.”

Ball added that organizations need to think about the building blocks of agentic AI in terms of interoperability. “The APIs of today will become the MCP servers and A2A protocols of tomorrow.”

Regardless of your industry, you should be asking: “How do I make this an orchestrated experience where things happen in a purposeful sequential manner?” When it comes to human-AI collaboration, it’s critical to consider the handoffs. Virtual agents can now handle routine tasks; humans must handle more difficult tasks and need to be empowered with effective copilots and support tools.

Alex Ball

Senior VP of Genesys Cloud

4. Hybrid human/AI workforces can deliver empathy at scale.

The question isn’t whether to adopt AI, it’s how to create a hybrid workforce that improves agent capacity and satisfaction, while continually making AI-powered capabilities smarter.

“From a customer perspective, we’re very intentional about making it clear what our virtual agents will be able to do during the lifecycle of the interaction — and this is consistent regardless of the channel,” said Adam Mitchell, Head of Enterprise Business Solutions at Voya Financial, adding that Voya keeps a human in the loop throughout the customer journey.

Voya also empowers its employees with an AI hub for learning, exploring and applying AI responsibly across the business. Frontline employees are helping Voya understand what’s working well at the organization, along with potential friction points for customers and employees alike. In turn, Voya is using these friction points as use cases for AI and automation. AI now handles simple tasks like password resets while humans are handling more emotional interactions like beneficiary claims.

Adam Mitchell

Head of Enterprise Business Solutions, Voya Financial

5. Metrics are evolving beyond contact center measures.

Traditional contact center metrics were based on human time constraints. They were about time, not quality; about transactions, not an overall journey. And they looked backward, not forward.

“In an AI world, we really have to look at quality,” said Rebecca Wettemann, CEO and Principal Analyst at Valoir. “We have to look at the quality of the AI outputs, thinking about things like success rate and — if an AI agent isn’t successful — effective escalation.”

Today, organizations need to evaluate the quality of the overall journey, not just the outcome of a siloed interaction. Capabilities like real-time sentiment analytics and journey tracking are enabling businesses to course correct live experiences, not just evaluate them afterward. But this requires a fundamental shift from reactive measurement to proactive intervention.

“This is not just about improving customer service,” said Wettemann. “This is about improving products. This is about having greater insights and understanding to be able to deliver new products to market based on what customers are really telling me.”

Rebecca Wettemann

CEO and Principal Analyst, Valoir