Table of Contents
There’s growing pressure for enterprises to “use more AI.” A recent report found that 80% of CX and contact center leaders said C-level executives are pushing for increased adoption.
In the same research, a third of CX and contact center leaders said their top career goal over the next 5–10 years is to implement an AI solution that delivers clear ROI.
Enterprises are experimenting and succeeding with agentic AI, so there is inevitable pressure for leaders to act quickly and get ahead of their competition. But jumping on trends without a clear business case only leads to short-term pilots with no real ROI.
For contact center leaders looking to transition their call center and their title into AI-first roles in the next 5 years, here are key considerations to help you align agentic AI with your business goals, identify the right entry points, and implement with purpose.
Align AI with your business strategy
If you’re exploring agentic AI, start by anchoring your strategy in business problems. Successful AI projects start with a clear direction, which means looking beyond the tech and thinking about how AI supports your broader business goals. If you can’t tie an AI solution to a clear pain point or opportunity, it’s unlikely to justify the investment.
It’s not about chasing trends; you need to look at how solutions can solve your specific problems and create valuable customer interactions.
Here are a few key questions to ask early in the process:
What’s your risk tolerance?
The more autonomy AI gains, the harder it becomes to predict, control, and correct its decisions. For instance, if an AI system is focused on efficiency, what happens if it prioritizes speed over accuracy or fairness?
Most AI-powered support tools rely on LLMs, which means they generate probabilistic responses. They’re fast, efficient, and creative, but also unpredictable. These risks can be managed effectively, but are you ready to develop a strategy that accounts for these risks?
Will your customers and internal stakeholders accept it?
Ultimately, AI adoption is about people. People need proper training, buy-in, and trust in the technology. If employees or customers resist AI because of fear or misunderstanding, adoption will be slow.
The goal isn’t to bolt on new tools, it’s to embed agentic AI into how your business works. Your organization must set realistic expectations and build trust through transparency, education, and involving stakeholders in AI development.
AI changes more than workflows. It influences how decisions are made and what “business as usual” looks like. That can feel disruptive, so you need to create space and time for:
- Learning: Help employees understand what agentic AI workflows will look like and how they’ll empower them.
- Adaptability: Be clear about how AI will change roles, free up time, and allow employees to focus on more complex, high-value work.
- Ongoing communication: Communicate openly throughout implementation to highlight what’s working, where there’s friction, and how to adjust.
You also need to consider how your customers will respond. If this is your first time using AI-driven channels, you will need to provide guidance, and customers are likely to have questions. You need to prepare for this. Understanding your customers’ behavior and expectations will help you introduce agentic AI in a way that works for them.
Identify where AI can deliver real impact
Once you’re aligned on the business problems agentic AI can solve, the next step is to find where it can deliver the most meaningful impact.
Agentic AI can transform how your organization works, but only if it’s applied intentionally. Use it where it actually makes a difference. Once your organization is aligned internally, you’ll need to consider your customer touchpoints across digital and voice channels. Don’t worry about feasibility yet. Now is the time to capture the experience you want to deliver, not just the one you currently can.
Then, you’ll want to identify where agentic AI can make a real impact. To do this, you’ll need someone who understands both the technology and the operational landscape. This person can spot high-potential entry points for automation that align with your CX and operational goals.
The goal here is to build enough traction and progress to prove the value of agentic AI and build momentum. These entry points should:
- Solve real organizational and customer pain points.
- Integrate smoothly with existing systems and teams.
- Build internal credibility through clear, measurable wins.
For most enterprises, the real question isn’t whether to start with agentic AI. It’s where. Here are some proven use cases for Agentic AI for you to consider.
Retail: Streamlining refunds for damaged products
Refunds can be frustrating for both customers and staff. The process is often slow, requires forms, and takes days to resolve.
Agentic AI can handle this from start to finish by asking the customer to describe the issue, sending them a secure link to upload photos or videos, and passing those to an AI that reviews and summarizes the damage. From there, the refund can be approved automatically without the customer waiting.
It’s a faster, smarter way to handle common requests while reducing manual review.
Here it is in action:
Healthcare: Automating patient scheduling and follow-ups
86% of patients say a positive experience is their top factor when choosing a healthcare provider.
Scheduling appointments manually can lead to long wait times and missed opportunities for care. Patients want easy access, and staff are stretched thin.
Agentic AI can help to book appointments, reschedule based on availability, send reminders, and follow up after visits. It can also help track recovery progress or confirm treatment plans.
That means less admin work for staff and a smoother experience for patients.
Financial services: Automating account inquiries and transaction history requests
Customers often face long wait times and manual verification when inquiring about account details or transaction history. Agentic AI can simplify this by offering quick, accurate responses. When a customer calls, an AI agent can verify their identity and provide account details, transaction updates, or assistance with issues like lost cards without needing a human agent.
By eliminating the need to wait on hold or complete forms, the AI creates a faster, more seamless experience. This allows bank staff to focus on more complex matters, ultimately improving customer satisfaction.
Understand guardrails to balance autonomy and control
Agentic AI is powerful, but only when it operates within the right boundaries. That means designing AI agents with clear rules, oversight mechanisms, and access to accurate, relevant knowledge.
Enterprises must consider the likelihood of unwanted behavior and be prepared to set clear guardrails that let AI operate independently while staying within strict business rules.
This starts with defining correct and safe behavior.
- What should the AI agent do?
- What shouldn’t it do?
- How should it respond when input is unclear or when a customer asks something sensitive?
Designing for safety and relevance means anticipating these edge cases and building a system that can handle them effectively. One of the most effective ways to do this is through retrieval-augmented generation (RAG).
What is RAG, and why does it matter?
Retrieval-augmented generation, or RAG, is a technique that enables agentic solutions like AI agents to cross-reference knowledge from a generative model with a knowledge base. RAG helps organizations balance the potential of agentic AI and the need for controlled responses.
Here’s how it works:
Knowledge base
Agentic AI is only as good as the information it has access to, so developing and maintaining a detailed knowledge base from which your agent can draw is crucial.
Your knowledge base should include everything you want the agent to be able to discuss, but it also needs to outline which topics or behaviors the agent should avoid and how to handle edge cases or sensitive inquiries.
Retriever
The retriever is the “search engine” that enables the agent to cross-reference facts against the knowledge base. The retriever must be accurate enough to cross-reference the knowledge base with little to no margin of error.
With RAG in place, you’re not relying on the AI to “guess” the right answer. You’re giving it access to real, up-to-date information specific to your organization.
Focus on purpose and planning to make agentic AI work
The key isn’t adopting agentic AI for its own sake; it’s using it to solve meaningful problems in a repeatable, scalable way. It’s about having a strategy that balances automation with human involvement, ensures compliance, and gets buy-in from both business and IT teams.
The most successful organizations take an iterative approach: start with real customer problems, build internal momentum, and scale intentionally. With the right mix of vision, planning, and flexibility, your team can turn AI into a long-term driver of business value.
Check out this video to learn more about how agentic AI is driving the future of customer experience in call centers.