Agentic AI in the Contact Center: A Proven Adoption Plan to Real-World Results

AI is rapidly redefining how contact centers operate. What began as the era of generative AI (tools that answer questions, summarize information, create imagery, etc.) is now evolving into agentic AI, where autonomous virtual agents can act, decide, and execute independently.

This evolution is reshaping expectations for both customer experience (CX) and workforce productivity.

In a recent webinar, industry expert Maribel Lopez, Founder & Principal Analyst, Lopez Research, sat down with David Singer, Verint’s Global Vice President, GTM Strategy, to discuss the practical steps organizations can take today to achieve real, measurable outcomes from agentic AI.

From GenAI to Agentic AI: An Evolution Toward Action

First, let’s clarify the difference between generative AI (GenAI) and agentic AI.

• GenAI excels at answering questions, synthesizing information, generating images, and natural-language text but doesn’t take action.
• Agentic AI moves beyond suggestion and into execution. It can act in real time to complete tasks on behalf of human agents or customers – autonomously.

This evolution fundamentally changes what contact centers can automate.

Agentic AI isn’t just a more advanced chatbot. It operates as a new type of digital worker that requires training, access controls, and performance management—just like any human agent.

Laying the Foundation for Responsible Agentic AI Adoption

With agentic AI in the contact center, organizations must understand what it’s doing, why it’s doing it, and whether it’s performing accurately and compliantly.

The key here is observability, not letting AI operate as a “black box” where its decision-making logic is unclear.

Organizations should outline a maturity path that begins with human‑in‑the‑loop evaluation, where teams closely monitor AI decisions. Over time, as trust builds, oversight can shift to spot checks.

This eventually leads to more autonomous operations, helping ensure that AI agents remain accurate, unbiased, compliant, and aligned with business goals.

That’s where critical concepts such as explainable AI and responsible, ethical AI come in:

• Explainable AI: AI systems designed so that the reasoning behind their decisions and processes is visible and can be understood by humans.
• Responsible AI: a broader approach that applies ethical principles and governance frameworks to ensure AI technologies behave safely, fairly, transparently, and in alignment with human values.

By treating AI agents like new employees, including onboarding, coaching, and performance management, organizations can also foster a culture of collaboration between humans and these new digital workers.

Ultimately, successful agentic AI adoption is less about the technology itself and more about operational readiness, observability, and clear outcome alignment. Without measurable goals, organizations risk implementing AI for its own sake rather than as a solution to real business challenges.

Successful Use Cases: 3 Proven Starting Points for Agentic AI Adoption

Starting small is crucial for contact centers piloting agentic AI. Beginning with simple, well‑defined use cases allows organizations to validate performance, manage change effectively, and build confidence among employees and leadership.

A smart way to start is by selecting existing workflows and processes and applying AI to automate them. This approach requires far less change management because teams don’t have to learn an entirely new process; they’re simply improving one they’re already familiar with.

There are three proven and highly actionable starting points that organizations can implement today. These use cases address known pain points, integrate seamlessly into existing operations, and deliver measurable outcomes quickly.

1. Automate After-Call Summarization

One of the most immediate opportunities is automating post‑call work. Agentic AI can instantly:

• Generate accurate call summaries
• Categorize interactions
• Complete routine after‑call documentation

This reduces several minutes of manual effort per call, giving agents more time to focus on customer needs while improving operational efficiency. It also reduces inconsistencies in manual categorization, which is an important benefit for training, auditing, and forecasting.

Learn more about Verint Wrap Up Bot

 2. Shift from legacy IVR to Intelligent Virtual Assistants

Contact centers still often struggle with rigid, menu‑based interactive voice response (IVR) systems that frustrate customers and fail to resolve issues efficiently.

By transforming the most frequent call drivers into intelligent, conversational agentic workflows, organizations can:

• Improve self‑service containment
• Reduce call volumes
• Deliver higher customer satisfaction.

AI-powered intelligent virtual assistants (IVAs) handle natural conversation, determine intent, and perform actions, dramatically elevating the legacy IVR experience without disrupting operations.

Learn more about Verint Intelligent Virtual Assistant

3. Automate Quality Management

Traditional quality management (QM) processes typically review only 1-3 percent of calls. This leaves significant gaps in visibility and performance oversight.

In contrast, AI-powered automated QM can scale monitoring to nearly 100 percent of all interactions, allowing organizations to:

• Identify performance patterns
• Detect compliance risks
• Ensure AI and human agents behave as intended
• Continuously improve CX outcomes.

With agentic AI generating objective, consistent scoring data, leaders gain deeper insights while reducing the manual workload on supervisors.

Learn more about Verint Quality Bot

Drive Your Agentic AI Adoption with Verint

Agentic AI has already moved from concept to reality, reshaping how contact centers operate. Organizations that begin with the right use cases and scale intentionally will unlock the strongest, fastest returns.

Instead of completely replacing current processes, why not adopt solutions that complement the way teams already work.

Aim for achieving consistent, repeatable, and measurable business outcomes that improve both customer and employee experience—not just implementing agentic AI to say you have agentic AI.

Learn how Verint can help you unlock AI business outcomes here.

Watch the full webinar featuring David Singer, Verint’s Global Vice President, GTM Strategy, here.