The CX workforce isn’t human or AI anymore—it’s both

Customer experience teams are hiring a new kind of worker. These workers retrieve data, update systems, trigger workflows, and resolve customer requests across multiple channels. They can handle thousands of interactions while coordinating work across enterprise systems. They are AI agents.

As AI agents become part of daily operations, the structure of customer experience work is changing. Across industries, customer experience teams are already operating a hybrid workforce—humans and AI agents working together to solve customer requests and execute workflows. Automation and enterprise systems must now support humans and AI agents working side by side.

This shift is unfolding faster than most technology transitions. The current agentic AI wave stands apart from previous technology for:

• Speed. Advances in AI are happening at a pace that even industry insiders struggle to keep up with. New models, tools, and capabilities appear daily. Ideas that seemed experimental a couple of months ago are already becoming operational.

• Scope of change. Earlier technology shifts changed how software is delivered or accessed. AI is beginning to change how work gets done.

AI agents are a legitimate workforce.

For many years, AI in customer experience played a supporting role. It improved efficiency, but did not fundamentally change the structure of the workforce as human agents remained responsible for most operational tasks.

That is changing. Hybrid workforce AI agents can perform many operational steps previously handled by humans. They can retrieve information from enterprise systems, update records, trigger workflows, and complete routine service requests across multiple channels.

The result is a workforce that now includes two distinct types of workers: human agents and AI agents. Each excels at different kinds of work. AI agents operate best in environments that demand scale, speed, and consistency. They can process thousands of interactions simultaneously while following defined workflows with precision. Human agents bring a different set of capabilities. They handle judgment, empathy, negotiation, and situations that require contextual understanding. They also resolve exceptions when processes do not follow predictable patterns.

Customer experience organizations will now rely on both. Some customer journeys will be handled primarily by AI agents, others will require human involvement, and many will move between the two as the situation evolves.

One of the most important challenges for CX leaders is designing systems that allow humans and AI agents to coordinate effectively. Workflows must move seamlessly between automated execution and human decision-making. That orchestration layer is a defining requirement for customer experience platforms.

Automation must deliver outcomes, not just interactions.

Automation is also evolving from focusing on improving individual interactions. Interactive voice response systems routed calls to the appropriate department, chatbots answered common questions, and workflow tools triggered predefined actions based on customer inputs. These technologies helped reduce operational costs, but addressed only a small part of the customer journey. A chatbot can provide an order status update, while resolving a delivery issue requires a series of manual steps across different systems. The interaction was automated, but the outcome was not.

Today, AI workforce automation approaches the problem differently. Instead of automating a single interaction, it can automate the full outcome behind a customer request, for example, in a typical service scenario, such as rescheduling a flight or changing a medical appointment. Completing that request may involve identity verification, checking availability, updating a scheduling system, recording the change in a system of record, and sending confirmation notifications. Each step touches a different system or workflow.

In a hybrid workforce environment, multiple AI agents, each designed for a specific task, work together to complete a goal or workflow. One agent retrieves customer data, another evaluates scheduling availability, and a third updates the relevant system and confirms the change. The human agent becomes involved only if the situation requires judgment or negotiation.

Multi-agent orchestration also changes how automation is measured. Instead of focusing on how efficiently a system handles conversations, the real test is whether it successfully resolves customer requests from beginning to end. The distinction may seem subtle, but it is a major shift in the design of CX technology.

Context is the fuel that powers AI agents.

Even the most capable AI agents can’t operate effectively without access to the right information. LLMs provide broad knowledge of language and general topics, but enterprise operations require much more specific context. To execute workflows reliably, AI agents must understand the environment in which they operate. That context typically comes from three sources:

• Systems of record contain the operational data that defines a business. Customer accounts, orders, transactions, and service histories all live within these systems.

• Systems of interaction capture how customers engage with the business over time, including prior conversations, decisions made during earlier interactions, and engagement patterns across channels.

• Enterprise knowledge stores policies, documentation, product information, and internal procedures that often reside across multiple repositories and knowledge bases.

When AI agents have access to these layers of context, they can execute tasks with much greater accuracy and reliability. Without it, they operate with only a partial understanding of the business environment.

This is why the data cloud is an essential component of modern AI systems. A well-designed data cloud brings together systems of record, interaction histories, and enterprise knowledge within a unified environment. It allows AI agents to access the information they need while maintaining governance, security, and data lineage.

Managing AI agents in the hybrid workforce.

The rise of AI agents also requires a new approach to managing customer service operations. Traditional workforce management focused on forecasting call volumes, scheduling human agents, and optimizing staffing levels. With humans and AI agents working side by side, planning becomes more complex. Leaders must understand how automation affects staffing requirements, how human roles evolve, and how work should be distributed between human agents and AI agents.

Some workflows may eventually become fully automated, others will continue to rely on human expertise, and many processes will involve collaboration between both. Handling AI agents as part of the workforce, instead of as software tools, will provide a clearer view of how to structure operations as automation expands.

A new operating model for customer experience.

AI is already part of the workforce. The question now is: what kinds of systems support humans and AI agents working together?

Most enterprise software was designed for a different model. Applications assumed that humans would execute the work, and software would support them along the way. Hybrid workforces reverse that assumption. Work can now be distributed between AI agents and humans depending on the task, context, and the level of judgment required. Designing for that model requires a different approach to enterprise systems. Instead of centering software around individual interactions, platforms must coordinate outcomes across agents, workflows, and enterprise data.

Customer experience automation platforms must support the hybrid workforce by coordinating work between AI and human agents within a single operational system to resolve customer requests and complete workflows across the enterprise.

Many of these capabilities are already being implemented through targeted AI customer experience use cases that automate specific service requests and operational tasks.