CX leaders are racing to automate, but many realize their existing systems weren’t built for what’s next. Agentic AI, which operates autonomously and initiates actions without needing continual human input, represents a major shift in how brands deliver customer experience. It’s not an upgrade—it’s a new operating model.
Yet many organizations default to bolt-on fixes: adding tools that solve isolated problems without addressing the core limitations of their infrastructure. That approach may create short-term momentum, but it rarely holds. Agentic AI can’t thrive in disconnected environments.
The better path is layered transformation. Technologies like conversational AI, for example, deliver immediate value by streamlining service, capturing real-time customer signals, and freeing employees for high-value tasks. Just as importantly, conversational AI helps standardize workflows and prepare organizations for broader AI integration.
To make agentic AI work, businesses need connected systems that can act on decisions. The path forward starts with building that capability into what already exists.
What Agentic AI Demands from Systems
Before diving into infrastructure challenges, it’s worth defining two terms that often get used interchangeably but mean different things. Conversational AI refers to tools and systems, like intelligent virtual agents (IVAs), that interact with users through voice or text to answer questions, route requests, and assist with basic tasks. Agentic AI builds on conversational AI to go one step further. These systems take initiative, make decisions, and trigger actions across workflows without waiting for human input. Both are gaining traction in CX, but each places different demands on the underlying tech stack.
CX teams have embraced AI, but much of the adoption remains shallow. According to McKinsey, only 1% of companies say AI is fully integrated into operations. Many organizations still launch isolated tools, like a virtual agent to handle routine calls or an analytics model to surface churn risk, without a broader plan for integration or scalability.
These limited pilots often don’t connect to backend systems, workflow engines, or core data infrastructure. As a result, they can’t adapt to new conditions, support cross-channel experiences, or deliver continuous value. AI can’t help agents resolve tickets faster if it doesn’t have access to order data, ticket history, or customer sentiment. Tools that operate in silos can’t evolve into systems that drive meaningful outcomes across the business.
The Role of Conversational AI as a Transitional Layer
Conversational AI is one of the most accessible forms of intelligent automation and one of the most foundational. AI-driven personalization can increase customer engagement and reduce service costs at scale. Conversational AI helps triage inquiries, answer questions, and route issues across channels. Critically, it captures real-time customer signals and creates the connective tissue between the front-end experience and the back-end operations.
In contact centers, conversational AI handles tasks like answering product availability questions, managing returns, confirming delivery status, and freeing up live agents for high-value interactions. These tools also help standardize inputs, enhancing the reliability and accuracy of downstream agentic AI integrations. Conversational AI is a bridge technology that standardizes workflows and prepares data environments for more complex AI integrations.
Conversational AI serves as a proving ground for contact centers that are still building their AI roadmap. It allows teams to operationalize intelligent automation in customer-facing functions while identifying integration challenges, training gaps, and backend limitations they must address before deploying agentic systems.
6 Ways to Evaluate System Readiness for Agentic AI
To make agentic AI a reality, companies need more than isolated pilots—they need a foundation built for autonomy. The checklist below outlines six critical ways to assess whether your current systems are ready to support agentic capabilities.
- Check your systems: Are your platforms cloud-based or still hosted on local servers? Cloud-based systems make sharing data in real time easier, connecting with other tools, and scaling with demand. Also, consider whether your systems send and receive information through APIs (application programming interfaces) when needed.
- Look at how work gets done: Are key processes, like customer onboarding or issue resolution, handled the same way across teams and channels? If workflows vary too much, it’s harder for AI to follow logic, spot patterns, or act without help.
- Identify gaps in orchestration: Can the system execute resolutions identified by AI, or does it stop at recommendations?
- Consider scalability: Can your systems handle increased transaction volumes and automation loads introduced by agentic AI?
- Watch for red flags: If your systems depend heavily on manual steps, like copying data between tools or updating records by hand, they’ll be hard for AI to work with. The same goes for systems that can’t easily connect with others. When tasks can’t be automated or shared between tools, AI has no way to act on what it learns.
- Evaluate integration capabilities: Determine whether current systems can integrate with AI through open standards or require costly custom development.
A system readiness review isn’t a one-time exercise. Organizations that assess and refine their digital infrastructure quarterly are significantly more likely to meet AI integration goals and avoid costly implementation missteps. Frequent reviews also improve cross-functional alignment, ensuring AI deployments support IT and business outcomes.
Aligning Teams for Agentic AI Readiness
A recent study found that 91% of large-company data leaders said cultural challenges and change management impede organizational efforts to become data-driven. Only 9% pointed to technology challenges. Investing in team readiness is as essential as investing in tech. But even with the right tools, progress can stall if teams aren’t working together. It’s important to decide early who is responsible for what. Everyone—IT, operations, marketing, and customer service—must understand how the AI system works and stay in sync as it evolves.
Clarity is key. Teams should agree on when to step in, who makes final decisions, and how to track changes. That way, everyone can trust that the system works as intended and makes responsible decisions.
Change management is also critical. Agentic AI shifts how work gets done—reassigning parts of job roles, redistributing decision-making authority, and raising expectations around speed and accuracy. If leaders don’t clearly communicate these changes, teams may resist adoption, misinterpret system outputs, or fall back on outdated workflows.
Planning for What Comes Next
Companies don’t need to future-proof for every possible AI use case, but they need to build toward extensibility. That means prioritizing tools that integrate cleanly, use open standards, and support incremental automation. It also means choosing solutions that solve today’s problems, like overloaded service teams or inconsistent CX, while setting the stage for tomorrow’s capabilities.
Leaders should think in modular terms. The priority is building systems in components that can evolve, rather than platforms that they must replace. Conversational AI is a smart first step because it delivers short-term impact by improving responsiveness and reducing strain on human teams. More importantly, it creates the operational backbone that agentic AI will rely on.
Agentic AI is not plug-and-play, and it’s not something you can bolt onto legacy infrastructure. But it doesn’t require a ground-up rebuild either. Retailers can move toward automation incrementally by making wise technological choices now. The organizations that get this right may not have the flashiest pilots, but they’ll have systems that are quietly ready to act.
