CX trends enterprise leaders should know in 2026

1. Deepening role of AI for contact center modernization

Contact centers have been trying to modernize for some time. But, in 2025, AI added a new layer of urgency. The transformative nature of AI will affect every aspect of contact center operations and customer service. Currently, conversations around this trend are strategic. Premises-based contact centers, for example, will feel new pressure for cloud adoption as this is a prerequisite for the higher-level benefits of AI.

For this process to be effective, all stakeholders must have a shared vision of AI's role. This means contact center modernization cannot exist in a vacuum, as it has with legacy, purpose-built technologies. New thinking from all business leaders is needed. Contact center modernization needs to be tied to broader AI initiatives across the organization, especially for other customer-facing functions such as sales and marketing.

Contact center modernization needs to be tied to broader AI initiatives across the organization.

This also means AI will go beyond automation and cost savings, as these efficiencies will need to translate into better experiences, both for customers and contact center agents. AI can generate automation across the entire customer journey, such as call routing, self-service, back-office workflows and knowledge base for personalized interactions, among other benefits. Operationally, this will extend new forms of automation to workforce management, agent tasks such as call summaries, fraud detection and compliance.

No other technology can affect so many facets of the contact center and integrate them with other business functions across the organization, making AI a leading trend for 2026. This degree of modernization goes beyond operational efficiencies to elevating CX and improving workplace satisfaction for agents and supervisors. AI can enable all this at scale, but only if CX stakeholders have a shared vision for AI's role in contact center modernization.

2. Deploying agentic AI for CX automation, but with caution

The term AI is a meta-level reference to a family of technologies and is best viewed at a strategic level for planning purposes. Specific applications of AI are more tactical, as these relate to specific use cases within the contact center. For 2026, no AI application will be more important than agentic AI, which gained CX traction in 2025. This application is about automation, especially for tasks and processes that an AI agent can manage end-to-end on its own.

Every CX vendor has an agentic AI offering now, but there's a mix of hype and reality. Few deployments are truly agentic, and CX leaders need to understand what constitutes agentic as opposed to achieving automation with other, non-agentic forms of AI.

This entails knowing the difference between deterministic and non-deterministic use cases. The former pertains to scenarios where AI bots can complete a task or manage an inquiry by using fixed logic and is usually best for simpler needs. More complex needs are less linear, have more ambiguity and cannot be handled effectively this way. Agentic AI adds elements that are more human-like, such as reasoning and judgment to navigate in ways that non-agentic bots cannot.

CX leaders don't usually have a data science background. They'll need to uplevel their AI literacy, especially as C-level leaders raise their expectations about AI.

While CX leaders don't usually have a data science background, they'll need to uplevel their AI literacy in 2026, especially as C-level leaders raise their expectations about AI. With agentic AI now on the leading edge of CX automation, this will be top-of-mind for the C-suite, and CX leaders will need to manage expectations.

Agentic AI requires omnichannel orchestration and predictive capabilities to manage a wide range of data inputs to be effective. While the expectation will be for these AI agents to fully automate workflows or tasks, the reality is geared toward more simple tasks. CX leaders are familiar with those realities, and they need C-level leaders to understand where agentic AI can best be deployed in 2026.

There is little margin for error here. For that reason, CX leaders should initially use agentic AI for internal workflows, where miscues can be fine-tuned with little downside. Only at that point should AI agents be used for customer-facing engagement, where the risk is much higher if they are not accurate or interact in ways that make customers uncomfortable.

3. Growing influence of AI copilots on the agent experience

Unlike agentic AI, copilots have been around for some time, both in the workplace and the contact center, and these use cases will continue in 2026. Copilots are a big part of AI's automation story, but with less risk and uncertainty as agentic AI.

This should not be surprising for C-level leaders, as copilots are a key feature of all UCaaS platforms and familiar to most workers. The use cases for CX are more recent, but are becoming core to the agent experience. In these scenarios, copilots provide a constant presence that uses AI to provide real-time support for workers and agents.

As the term implies, copilots work alongside workers, not in competition to replace them. This is a good example for business leaders to understand the nuances around AI's value to the organization. Some forms of AI may lead to job cuts and create cost savings through automation and improved efficiencies. Other forms, such as copilots, are primarily about improving productivity and performance, which contributes to top-line growth, rather than cost savings.

While all businesses want to cut costs, AI can also drive revenue growth, especially when used to improve CX.

That last point is another important nuance about AI because the C-suite needs to see AI as more than a vehicle for cost reduction. While all businesses want to cut costs, AI can also drive revenue growth, especially when used to improve CX. Copilots, in particular, can do this in many ways. When human agents struggle to say the right thing or type out a written response, copilots can use sentiment analysis to tailor a more effective response.

Another example is how copilots can use AI to draw from various data sets and knowledge bases to present highly personalized offers to customers that agents could not procure on their own. These offers are designed to drive new purchases that are likely to follow once the agent provides a good resolution to the customer's issue. In these cases, the copilot is doing most of the work, freeing up the agent to focus more deeply on serving the customer.

This may not be the outcome every time. But, for 2026, business leaders need to see copilots playing a growing role for how agents to do their jobs. This means copilots will become more central to automating workflows and integrating across multiple channels and back-office systems.

To support this, business leaders will need the right tools in place to monitor accuracy, data security and compliance, especially once customer interactions lead to new purchases. This also means guardrails are needed to set limits around scenarios where AI can be trusted, along with governance to ensure new capabilities or use cases stay within those bounds.

4. Expanding role of analytics for a holistic view of customers

Analytics is what makes AI relatable to humans, as it reflects the expected effect of an action on an outcome, without a complex discourse about how that expectation was reached. This is particularly important for CX. Agents must interact with customers in real time, and they need analytics at their fingertips without having to look for it.

With strategic CX, businesses must become more customer-centric, not just in the contact center but across the entire organization.

With strategic CX, businesses must become more customer-centric, not just in the contact center but across the entire organization. In the past, the contact center was the repository for all customer knowledge. Now, customer-centric organizations collect data across the entire customer journey with a company. The more holistic view of the customers -- a 360-degree view -- the better the CX will be.

However, to attain this holistic view, organizations need clear access to the data, deploy AI to analyze massive sets of data and make each customer interaction highly personalized. Without analytics, agents have limited visibility into underlying issues and often can only hope for the best outcome.

AI-driven analytics offers a more effective way to use all that data, not just to guide agents to a better response, but to provide rich insights that personalize service with responses that are more prescriptive than reactive. Aside from those insights, AI can do this at scale across an entire customer base, a capability that businesses never had previously.

This is where AI elevates CX and can put agents on a level playing field with customers. In this context, business leaders will see new value with analytics, as it allows agents to keep pace with changing customer expectations. By drawing data from across the organization, analytics can anticipate and predict outcomes, so these changes don't take agents by surprise.

Doing this at scale can help the business forecast demand, and optimize resource deployment for running the contact center. As such, business leaders should view analytics as a core element to having a holistic view of the customer. From there, AI helps drive customer retention.

5. Data quality becomes a priority for AI ROI

Analytics drives a lot of the value for AI, especially around CX. But that value is largely dependent on the quality of the data. Business leaders may have the mistaken notion that having a large volume of data inside the organization ensures that AI initiatives will be successful and thus low risk.

Analytics drives a lot of the value for AI, especially around CX. But that value is largely dependent on the quality of the data.

This is another area where business leaders need to uplevel their understanding about AI. Most existing data pre-dates AI and was never curated to be used with today's technologies. The inherent data still has value, but the associated metadata will be limited. As such, it's easy to overestimate the value of current and historical data for the transformative effect business leaders are expecting with AI.

Data is managed differently now, and going forward it will have greater value for AI. Because of this, organizations need good governance to ensure data quality and to optimize inputs for AI-based applications. Since data increasingly drives business decisions, this issue is highly relevant to senior business leaders. Having quality data inputs is one thing, but having the right tools to extract value is another, and that is a key rationale for investing in AI, as data is the oxygen for AI to thrive.

This is especially important for CX leaders, as the contact center is naturally rich with data, and for this reason it's a leading use case for AI. The better quality the data, the better the analytics. CX will benefit by having more accurate agentic AI outcomes, more efficient workflows to support agents and better orchestration across channels.

The same is true for the business overall. Better CX translates into better results elsewhere, such as sales, marketing, legal and HR. For business leaders, then, the implication is to ensure data quality is a priority when investing in AI.

6. Developing an identity-first approach for trusted CX

As customer service becomes further defined by digital technologies, issues related to ascertaining the customer's identity become more complex. Almost all customer interactions occur over digital channels, and often across multiple channels during a single session. The more touchpoints for these interactions, the greater the risks on several fronts.

Fraud is an ongoing issue, especially in the contact center, along with marketing and its outbound messaging campaigns. Without adequate controls, bad actors can impersonate customers, exposing the business to financial and brand risk, identity theft and compliance violations. Marketers are faced with similar and equally problematic risks, such as robocalls.

As AI adoption broadens, business leaders should prioritize the importance of managing these risks in 2026. Becoming identity-first is part of that response, but requires a systemic approach that will unfold over time. Recognizing the risks is the important first step, as the threats are exponentially more powerful when all the touchpoints are digital.

Fraud and cybersecurity concerns will never be eliminated, but an identity-first posture helps mitigate the threats.

Fraud and broader cybersecurity concerns will never be eliminated, but an identity-first posture goes a long way to mitigating the threats. The same applies to AI, as identity should be viewed as another strategic use case, not just for authenticating customers in real time, but also protecting them against identity theft. In support of this, business leaders should note that AI excels at pattern recognition and detecting anomalies, and both are highly relevant here. Aside from protecting the identity of bona fide customers, AI can also flag bad actors in ways that human agents cannot.

C-suite strategic guidance and action checklist

These trends are central to shaping CX outcomes in 2026, but other developments should be considered as well. Taken together, these trends should help business leaders see the bigger picture -- not just to make CX better, but also how that upside benefits the business as a whole.

To recap, here's a CX 2026 action checklist for C-suite executives and CX business technology buyers:

• Modernize the contact center with deeper AI integration. In this process, all company stakeholders need a shared vision of AI's role. Enable AI to integrate contact center insights with other business functions and software. This step can produce better experiences for customers and contact center agents.
• Deploy agentic AI for CX automation. Understand the difference between agentic AI and other forms of automation. Agentic AI agents can automatically manage tasks from start to finish. CX and C-suite leaders need to uplevel their AI literacy in 2026. At the same time, CX leaders must manage the C-suite's expectations of AI.
• Enable AI copilots to support human agents in real time. Copilots work with workers, not in competition to replace them. Primarily, copilots aim to improve productivity, performance and customer personalization, which supports top-line growth. Copilots should integrate across channels and back-office systems.
• Expand analytics for a holistic view of customers. Contact center and customer journey data should be integrated across an entire organization. Analytics can also help personalize customer interactions and support agents.
• Prioritize good, quality data. Be cautious of old data that may not jibe with modern AI approaches. Good baseline data is important, but so are the extraction tools for that data. Contact centers are rich in data, which can yield insightful analytics, agentic AI workflows and improved orchestration across the company.
• Develop an identity-first strategy to boost security. As customers interact with more digital touchpoints, security risks increase. Business leaders need to understand and mitigate these risks. Trusted CX takes an identity-first approach, which protects customers from identity theft while also streamlining log-ins.

For the contact center, modernization is the strategic imperative, but this is more than just CX. Investment decisions and AI ROI criteria for the contact center should be made in a broader context, since CX now requires data inputs and insights from across the organization. This means CX must also be about agent experience and employee experience, as they have a role to play for elevating CX.

CX must also be about the agent experience and employee experience.

For technology decision-makers, this presents a strong rationale for platform consolidation where applications that bring all this together need to be tightly integrated. There needs to be seamless sharing of data across the organization -- not siloed -- and workflow orchestration for driving better CX, especially cross-team collaboration to support the contact center. This has been in play with CCaaS and UCaaS providers for some time, where enterprises are moving to a common platform. And, in 2026, expect to see CPaaS become part of that platform consolidation trend.

While CX is ultimately about human interaction between agents and customers, technology is the enabler for outcomes that are paramount to the business. This is especially true for AI given its transformational capabilities, but they will only be realized with a strategic approach shaped by leaders both inside the contact center and from business-level leaders who understand CX in terms of impacting overall success for the business.

Jon Arnold is principal of J Arnold & Associates, an independent analyst providing thought leadership and go-to-market counsel with a focus on the business-level effect of communications technology on digital transformation.