AI That Acts: Preparing CX Systems for the Agentic Era

AI is everywhere in CX and contact centers right now. From virtual agents to real-time analytics, it’s clear that intelligent systems are beginning to reshape how brands serve, support, and engage customers. But with all the experimentation, one challenge is becoming increasingly visible: most organizations aren’t building for scale.

Across the industry, we’re seeing a disconnect between AI ambition and AI readiness. CX and contact center teams have embraced AI, but much of the adoption remains shallow. While 92% of companies plan to increase AI investment, only 1% consider themselves fully scaled. Many AI deployments are isolated features that solve a specific problem without connecting to broader workflows. As a result, they struggle to deliver meaningful impact or evolve into systems that drive enterprise value.

But it’s entirely possible for CX leaders to close that gap, without locking themselves into rigid or overly complex infrastructure. By investing in the right capabilities now, organizations can build the foundational elements required for agentic AI.

AI is Already Moving Past Pilots

Many organizations are still running disconnected experiments—chatbots here, automation scripts there—without a long-term integration plan. These projects often lack shared data pipelines, system compatibility, or unified architecture.

When AI systems aren’t integrated into workflows across an enterprise, they can’t adapt, learn, or deliver cumulative value. This is especially problematic for agentic AI, which requires connected systems to initiate actions and drive outcomes autonomously.

To clarify: Conversational AI refers to tools like intelligent virtual agents (IVAs) that engage with customers via voice or chat, typically to answer questions or complete tasks. Agentic AI initiates actions on its own, adapting to new information and making decisions without waiting for human input. Each type of AI has different requirements, but both benefit from strong system integration.

Agentic AI Requires Integrated Infrastructure

Agentic AI marks a shift from reactive tools to proactive systems. Instead of waiting for inputs, these platforms assess context, identify opportunities, make decisions, and act. In a CX environment, that could look like an AI system monitoring customer behavior, triggering personalized outreach, applying a resolution, and confirming case closure—all autonomously.

But this level of autonomy requires agentic systems to be deeply embedded in an organization’s operational fabric. AI tools must connect with systems of record (such as order management), systems of engagement (like customer communications), and systems of execution (like fulfillment and inventory). That integration requires real-time data, well-defined business logic, and reliable escalation paths when AI needs human intervention.

Many contact centers encounter structural limitations in this area. Siloed databases, rigid workflows, and closed application programming interfaces (APIs) prevent AI agents from seeing the full picture or taking appropriate action.

To function effectively, agentic AI needs infrastructure that is:

• Modular: Systems should be easy to update or replace in parts, rather than as a whole.
• Interoperable: Systems should exchange data freely and work across teams and tools.
• Observable: Staff should be able to see what AI is doing and why.
• Governable: Rules and limits should guide how AI operates so it stays in sync with policy and ethics.

Contact centers that begin modernizing with these traits in mind, especially in areas like inventory, fulfillment, and customer engagement, will be far better positioned to scale agentic capabilities when the time comes.

Why Conversational AI Is a Strategic Starting Point

Conversational AI systems—like intelligent virtual agents (IVAs)—offer an ideal entry point into scalable, agentic automation. Unlike rule-based bots, IVAs use natural language understanding and can interact with multiple systems in real time. They can answer customer questions, route complex inquiries, and even trigger transactions.

Since they sit at the intersection of customer experience and backend operations, IVAs create connections between tools and teams that don’t always share data easily. That makes IVAs a useful diagnostic tool and a productivity enhancer. Contact centers deploying IVAs gain insight into integration gaps, data inconsistencies, and escalation paths—insights that are essential for planning broader agentic AI deployments.

Avoiding the “Bolt-On” Trap

A common mistake businesses make is bolting AI tools onto legacy systems without addressing structural limitations. These “bolt-on” deployments may show short-term results but rarely scale. Instead, they can introduce redundancy, security risks, and confusion about ownership.

Instead, organizations should approach AI deployment as a system-wide effort. AI systems need to work in harmony with the business functions they support. That means designing systems that can connect easily and clearly defining how to govern data.

What CX Leaders Can Do Now

Organizations can take practical steps today to prepare for more advanced AI adoption, without overhauling everything at once.

Begin with a comprehensive audit of existing systems. Look at whether core platforms are cloud-based, which typically makes them easier to update and integrate. Identify which platforms can connect to other tools using open APIs, and which are nearing end-of-life. Having a simple evaluation checklist can help clarify where updates will offer the most benefit.

Next, map core workflows to determine where intelligent automation could add the most value. Focus on processes that happen frequently, follow a consistent set of rules, and affect a large number of customers, like routing, case tagging, or collecting feedback.

When choosing tools, select ones that work with your current systems without extensive rework or new customization. These solutions reduce the risk of creating new silos and help avoid future rework.

Training matters, too. Teams need to know more than user instructions—they need visibility into what AI is doing, when it’s acting autonomously, and how to step in when needed. Ensure staff understand escalation paths, what issues they’re expected to handle versus the AI system and have channels to provide feedback on system performance.

Bring key departments—like IT, CX, and operations—into the process early. AI success is about building a foundation that can scale with technology and business goals.

Establish governance policies for all automation tools. Define how the system makes decisions, its boundaries, and what happens when something needs human review. This process includes documenting decision logic, defining guardrails around AI autonomy, and aligning outputs with compliance and fairness expectations. These policies help teams understand what the AI is doing and build trust in how it supports the business.

Finally, choose use cases that matter now and pave the way for what comes next. The goal isn’t just automation for its own sake. It’s to build a foundation that can evolve.

Build Like It’s Going Somewhere

AI is far more than a feature set—it’s a workforce multiplier. To unlock its full value, contact centers need more than pilots—they need evolving systems.

Fortunately, that evolution doesn’t require starting over. It starts with the proper foundation: flexible systems, practical tools, and a plan for integration. Organizations that rethink readiness through this lens—looking beyond what AI can do to what it must enable—will avoid the pitfalls of siloed adoption and fragmented growth. By laying the groundwork now, they give their teams the stability, clarity, and tools to collaborate with AI at scale.