Where Is CX Headed? Three Trends to Watch In 2026 

Customer experience (CX) is rapidly transforming, shaped by emerging technology and shifting consumer preferences. In this challenging environment, there is no room for a misstep. So, where should brands and outsourcers focus their attention? Here are three CX trends to watch in 2026, ones that, according to industry experts and our CX thought leaders, will shape budgets, investments and the way we deliver experiences at scale. `

Trend #1: AI Experimentation Shifts to Strategic Implementation

Gone are the days of experimentation. This will be the year to “prove it”, where AI isn’t just an add-on, it will be central to CX operations, embedded into just about every facet of the business, from backend processes and workflows to real-time customer interactions.

According to Forrester, almost a third of organizations will adopt ‘hybrid’ structures with parallel AI functions that mirror human agent roles. This could lower agent workloads by an average of one hour as AI automates narrow tasks. But, analysts caution restraint.

“3 in 10 firms could harm their total experience growth by over automating complex inquiries or trusting too much on GenAI outputs, which could lead to frustrating self-serve experiences.”

From Salesforce to Klarna, there are many examples of companies that may have jumped too quickly to automate customer interactions, with some forced to roll back their AI-first strategies after they failed to meet expectations. One MIT study even revealed that 95% of organizations will likely see no measurable return on their AI investments.

“I think the problem is that some business leaders adopt a one-size-fits-all mindset without fully understanding that they need to adapt AI to their own business needs,” explains Egbert von Frankenberg, CEO of Knightfox App Design Ltd., and itel technology advisor. “That’s why, when we were developing itelligence, we took a more strategic approach. It had to start with a highly accurate call transcription because everything else has a domino effect. It forms the basis for our AI model training, automated QA, and subject matter knowledge systems, tools that build on top of one another and have real-world use cases. But it’s not just a matter of implementing those tools, you also need to find a way to measure and quantify their impact.”

Experts warn that if companies roll out AI agents prematurely or in contexts where they are not likely to deliver, it could erode satisfaction and damage the overall brand experience.

“Organizations should not only know how to use AI, but when to question it,” von Frankenberg says. “For instance, with intelligent virtual agents, there’s this notion that large language models, LLMs, are the single best approach, when a series of smaller models that are more targeted to a specific problem or situation can actually be better at handling complex customer issues.”

Trend #2: Demand For Human Agents Will Increase

It may seem contradictory that the demand for human agents will increase by almost 10%, despite AI advancements, even when leading analysts believe that autonomous AI systems will soon resolve almost 80% of customer service issues.

This is proven by McKinsey’s consumer surveys that show a lingering preference for live calls, especially among baby boomers (94%), and perhaps surprisingly, even among Gen Z (71%), who still believe it is the quickest and easiest way to get their issues addressed.

Perhaps humans will always be needed for sensitive or emotionally complex interactions, and this will require a deeper examination of how AI and humans can work together collaboratively to create a sort of collective intelligence that elevates the role of human agents instead of making it obsolete.

“Even with advanced AI, human skills will remain essential, and while contact centers won’t disappear, the way agents work will transform as we see greater shifts towards AI augmentation.”

This will of course require a different method of training and a set of foundational skills that combine AI literacy with core soft skills.

“We’re witnessing a transformation in the learning landscape. The learner you encounter today is not merely an iteration but a complete evolution from the one five years prior,” explains Shurland Buchanan, itel’s Chief Learning Officer. “They are more tech-driven, and so the learning experience will adapt. We are already teaching agents how to use AI systems to enhance their performance, how to look at data and interpret it. But I foresee a learning path where agents become in a way prompt engineers who learn how to extract the information they need from knowledge databases by asking the right questions to get the results they need. Humans will also need to remain in the loop to verify AI outputs, and they will need to lean more heavily on critical thinking skills that allow them to do their own quality control, which will become increasingly important for compliance and ethics.”

Technology also doesn’t need to be a zero-sum game. Agents are already seeing net benefits in their day-to-day work with tools that can summarize issues, recommend actions and reduce post call work, which leads to reduced call times and enhanced productivity.

As AI assumes most of the mundane tasks and general queries, agents will become even more valuable for the soft skills that only they can provide, things like complex judgement, empathy, and emotionally nuanced interactions that rely on human sentiment and situational awareness.

Trend #3: Trust Becomes the New AI Frontier

As automation takes hold, many experts see trust as the new “AI frontier”. In fact, it could be the limiting factor in AI deployment, as customers become increasingly sensitive to how their data is used, leading to slower adoption of AI-driven service.

That could be why, in one Gartner survey, almost two-thirds of customers say they prefer not to use AI in customer service, because AI often feels like a black box, where transparency and communication is often lacking around how data is used in AI applications and model training.

“This year, ethical AI practices and transparency could take center stage, becoming not just a necessity but fundamental competitive differentiators.”

According to Duane Williams, itel’s Chief Technology Officer, “there’s an obvious gap between what some businesses are offering and what their consumers are experiencing. What often leaves a bad taste in the mouths of many customers is when AI is used as an aggressive deflection tool. I think a better approach would be to offer it as an option, to be transparent when customers are speaking to AI, and over time, people will naturally gravitate towards it, especially if AI matches or even exceeds a human level of service. If we want to improve trust and increase user adoption, I think that is the method we should use.”

As trust issues persist, outsourced partners could also be expected to meet heightened compliance and transparency standards, as evidenced by the proposed U.S. legislation under the Keep Call Centers in America Act, which would create potential oversight of AI deployment in customer service functions. This could include disclosure when AI is in use and requirements that would force CX operators to offer a human U.S.-based agent upon request.

“I think we’re in the early stages of regulation,” explains Williams, “which is one of the reasons why a lot of the model providers are pressuring regulators to try and come up with proper protocols to adequately regulate this kind of system, and how it uses information. But for now, one of the best ways that we can ensure data privacy is by using commercially available AI models that have in-built controls and allows users to run isolated instances within their own domain, so it's not aware of other versions of itself that exist and can’t cross share information. That is the approach that I think is going to see long term traction because it checks all the boxes from a security and compliance perspective.”

2026 Will Be the Year of AI Refinement

2025 was the year when almost every CX provider felt compelled to have a technology tool in the sandbox, which often meant frantic deployment and many rough edges where AI is concerned, leading to a sometimes-disparate patchwork of AI tools and questionable results.

This year, we can expect to see a greater refinement of not only AI outputs, but AI strategies, with a much more calculated approach to integrations, model accuracy, user transparency and the interaction between human and machine, with many firms reaching a level of normalization, where AI is no longer a “shiny new object” but a central system from which their contact center operates.

“Some will not see returns on their AI investments and will drop out of the AI race because they don’t want to take the risk,” explains von Frankenberg, “while those that stick with it will iterate their AI implementation and will just get better. It will be an interesting time, as we will soon see the ones who make it and the ones who break.”

Curious how we use AI to augment CX interactions and client operations? Learn more about itelligence.