Customer experience teams have embraced automation, and tools like chatbots and virtual agents have delivered measurable improvements in service and efficiency. But many teams still struggle to move beyond isolated fixes. Basic scripts and reactive workflows often lack the adaptability and depth to support long-term transformation.
Agentic artificial intelligence—AI that can make decisions, initiate actions, and carry out tasks independently— offers a path to scale. It builds on the foundation established by conversational AI, extending automation into more complex, outcome-driven processes. CX leaders need to revisit the structure beneath their tools to realize that potential.
Roughly 80 percent of organizations are already using some form of AI-powered automation to enhance customer experience (CX), whether it's routing tickets, sending follow-ups, or triggering surveys. These tools solve repeatable problems and reduce manual effort. However, agentic AI operates at a different level.
Rather than waiting for instructions, agentic systems assess context, weigh options, and act without constant human input. In a CX environment, that might mean adjusting a customer communication strategy mid-interaction, initiating a resolution process, or escalating based on behavioral signals and history.
Imagine a customer reaches out to his telecom provider through chat, frustrated about slow internet. Instead of waiting for input or instructions, the agentic system pulls up the customer's history, notices a pattern of recent issues, and runs a diagnostic in the background. It identifies that the modem is outdated, offers a replacement with expedited shipping, and preemptively schedules a technician. As the conversation unfolds, it adjusts its tone based on the customer's language and past interactions. If frustration escalates, it automatically routes the customer to a retention specialist with a complete summary of the issue and a recommended loyalty offer, all with no human prompt needed.
To make all this happen, agentic AI needs more than triggers and scripts. It depends on clean inputs, consistent workflows, and reliable escalation paths. CX leaders who approach it as just another automation layer risk misalignment and miss out on its long-term value.
Build the Foundation Before You Scale
Intelligent virtual agents (IVAs) have become a practical entry point for AI in CX. These systems can resolve common issues, route inquiries, and integrate with back-end platforms to handle tasks like scheduling or payments. But their impact isn't limited to efficiency. Well-designed IVAs also introduce the structural discipline that more advanced systems need. Organizations must clarify workflows, surface integration gaps, and define escalation protocols. This work lays the groundwork for agentic AI, which relies on the same underlying clarity to operate effectively.
As teams experiment with AI, it's easy to assume the goal is to add more tools. But success often comes down to structure. Agentic systems need reliable access to data, consistency in workflows, and guardrails for decision-making. Even the best models can create confusion or unintended consequences without these elements. The organizations that benefit most will design their systems to support intelligent, coordinated action.
That shift from interpreting insights to enabling action is at the heart of agentic AI. Many AI tools surface information, but agentic AI acts on it. To support that, systems need clear rules, reliable transitions, and defined limits so AI knows what to do (and when to stop or ask for help). The better the structure, the more reliably the system can operate in live environments. AI should enhance day-to-day execution, not create new points of confusion.
Team Readiness and Collaboration
Even the best tools can fail without clear roles and cross-functional coordination. CX leaders should establish shared responsibility early, involving IT, operations, marketing, and front-line service teams in implementation planning. Everyone needs to understand what the AI system does, how it behaves, and where it fits into the process.
Agentic AI doesn't just add automation; it shifts how work gets done. Teams that once handled routine inquiries might now handle complex or emotionally nuanced cases. This transition can boost impact, but it also adds cognitive load. Role clarity and change support are essential.
Build time into the rollout plan for training and upskilling. Be explicit about how tasks change, when to prioritize human input, and how to escalate edge cases. Teams also need tools to monitor and troubleshoot the system effectively.
Conversational AI offers a low-friction way to build these muscles. It's a familiar, accessible form of automation that teams can learn to manage, adjust, and collaborate with, creating a foundation of operational fluency for more advanced AI capabilities.
Build for What's Next
Agentic AI is already entering production in enterprise environments, especially in service, support, and operations. Leaders who want to move from reactive tools to proactive systems must now prepare their foundations.
That doesn't mean overhauling everything at once. It means taking practical steps: updating core platforms, aligning teams around shared goals, and choosing tools that can act, not just observe. Conversational AI remains one of the most effective places to start because it delivers value today and builds the system awareness required for what's ahead.
The goal isn't more AI. It's smarter AI that is intelligently deployed and has the proper support.
Rebecca Jones is chief operating officer of WestCX.