In the first post of this two-part series, we explored how contact centers were already implementing AI technologies to improve the experience for their customers, agents, and supervisors. But in the rapidly changing world of AI, the question remains, “What’s next?” In this follow-up, we’ll dig into three core areas that are rapidly growing and how organizations will feel their impact in the contact center in the coming year.
The next step in the evolution of AI for the contact center impacts all three pillars of customer experience, agent experience, and supervisor experience. Agentic AI refers to AI systems that operate by autonomously making decisions and taking actions, potentially across multiple systems, to accomplish defined goals. This goes beyond basic automation and decision trees, and proactively resolves issues to support customers and agents in real-time.
An example of an agentic AI goal could be, “Your goal is to investigate, validate, and resolve customer billing issues regarding ‘Overcharge’ or ‘Missing Discount’ claims. You are able to issue credits up to $50 without human intervention if data supports the customer claim.” For a goal like this, you may also provide tools that the AI can use. For example, API access to interact with data for billing, promotions, and issuing service credits.
Additionally, you can provide operational guidelines to help the AI understand what things are important when making a decision, for instance, “compare the customer’s expected bill against the billing history and available promotions.”
In the contact center, these systems can dynamically define the next best action and act on it by interacting with enterprise applications. For instance, ordering, ticketing, and billing systems. And while these systems are designed to be autonomous, they can still escalate to human agents when the AI can not (or should not) make a decision.
According to a 2025 study by Pindrop, there has been a 680% rise in deepfake activity and a 26% increase in fraud attempts in the contact center.
The use of AI is enabling bad actors to amplify the scale, sophistication, and success rate for attacks against the contact center voice channel. AI-driven bots can navigate IVRs and defeat knowledge-based checks more than half the time, and Generative AI and voice cloning technologies allow attackers to impersonate customers, executives, and key stakeholders. The availability and scalability of these attacks allow them to be coordinated and run with minimal human involvement.
AI-driven bots can navigate IVRs and defeat knowledge-based checks more than half the time.
If organizations continue to rely on legacy, outdated controls for the security of their contact center, they will soon find themselves falling behind as they become targets for these AI threats. To counter these attacks, AI can also be used as an active defense layer across the voice channel.
Machine learning models are used to detect synthetic or manipulated speech, while behavioral AI monitors how callers interact with IVRs and agents to identify automation or social engineering in progress. When high-risk calls or digital behavior are identified, the AI system can act accordingly– such as passing the call to a specialized fraud team and creating a ticket with the security team with the contact information… without impacting legitimate customers.
By leveraging an AI defense strategy, organizations not only protect against these new attack patterns but also enable themselves to adapt at machine speed to identify and learn from new attack patterns.
Traditionally, customer lifecycle management relied on pre-defined stages or a one-size-fits-all workflow. At best, it would create large customer segments with one-size-fits-some workflows associated with them. AI-driven customer lifecycle management is a significant shift from legacy segment-based engagement to dynamic, personal, and contextual journeys that change in real-time. AI can evaluate and understand where a customer is in the moment and respond proactively.
For instance:
AI can consider multiple factors to personalize experiences and make decisions faster and more consistently than humans, delivering secure, natural, and personalized conversations at any scale, without building complex decision trees or SOPs. This allows organizations to shift their focus to refining the experience and their internal processes to reducing churn and improving experiences– not just for customers, but also for their employees.
Throughout this post, we’ve covered three key areas where to look for innovative solutions in the AI contact center space for the coming year. But knowing where to look is only half the battle — knowing why you're implementing AI is what separates successful projects from expensive lessons.
The most common failure mode isn't poor technology; it's solving the wrong problem, or lacking a clear definition of what success looks like before the project starts.
Without well-defined use cases and measurable expected outcomes, AI initiatives routinely fail to deliver value. Not because AI can't perform, but because no one agreed on what it was supposed to accomplish. Before chasing new capabilities, nail down the problem you're solving and what "better" actually looks like.
CXponent can help identify the use cases and prerequisites needed to help ensure the success of your AI contact center project. Connect with us to determine the right path for how AI can innovate and protect your contact center now and into the future.