We appear to be in “the Age of Disruption”: in society, commerce, government policies, environment, and technology, notably AI like Agentic AI.
As a, if not the prime point of interaction and engagement between organizations and their customers, contact centers are accustomed to working through disruptions. These drive in volume from individuals impacted and concerned about these changes, seeking answers and assistance and drive out proactive outbound contacts.
So, what are contact centers seeing and experiencing? And how are they responding and should respond? We asked our expert panel of industry suppliers to find out.
- Kevin McNulty, Senior Director, Product Marketing, Talkdesk
- Sarika Prasad, Director, Product Marketing, Five9
- Neeraj Verma, Head of Product for Customer Service Automation, NICE
Q. Set the scene. What changes, trends, and developments have you seen emerge over the past 12 months that impact customer contact and what are their drivers?
Kevin McNulty:
Triage is no longer enough. Contact centers are being asked to do more than resolve issues: they’re expected to deliver proactive, personalized, end-to-end experiences across every channel.
The pressure to meet these expectations without increasing headcount or cost has pushed organizations to rethink their approach to automation and rethink the role of AI.
The conversation has moved beyond simple deflection. AI is now assisting human agents during complex interactions, automating back-office tasks like after-call work, and enabling more seamless, contextual self-service.
Generative AI has helped unlock more natural, conversational experiences, but we’re also starting to see the emergence of Agentic AI, where multiple AI agents collaborate to handle multi-step tasks and support multiple personas across the customer journey. While still early, this shift represents a fundamental rethinking of what’s possible in customer experience (CX) automation.
Underpinning these changes is a growing emphasis on data: especially unstructured data from voice and chat. That data is being mined not just for reporting, but to discover automation opportunities, improve knowledge, and inform strategy.
Sarika Prasad:
Over the past year, a convergence of transformative trends has reshaped the inbound contact center, led by the rapid evolution of AI and rising consumer expectations.
As companies accelerate AI integration across their operations, contact centers are undergoing a profound shift. AI now enables them to do far more than just answer FAQs. From appointment scheduling to intelligent triage and self-service workflows, AI is streamlining processes, reducing wait times, and enhancing resolution accuracy.
As these solutions become increasingly sophisticated, contact centers are not just becoming more efficient, they’re building the foundation for stronger customer trust and loyalty and driving revenue.
Further, today’s consumers are more demanding than ever. Expectations for exceptional CX are sky-high and unforgiving. According to our recent “2025 Customer Experience Report - Consumer Edition”, 40% of consumers would abandon a brand after just one poor service experience. That’s a wake-up call: delivering CX excellence isn’t optional, it’s necessary.
To meet these rising standards, leading organizations are embracing the “Three E’s” of modern CX: effortless, efficient, and engaging.
This framework centers on balancing automation with human engagement, ensuring experiences are intuitive, fast, and highly personalized. When done right, it doesn’t just resolve issues, it deepens relationships and drives long-term loyalty.
Neeraj Verma:
Over the past 12 to 24 months, we’ve witnessed remarkable advancements in AI and automation: transformations that are reshaping the customer service landscape, particularly in the realm of agentic systems.
A standout inbound use case is the evolution of IVR. One of the most important shifts with IVR has been the ability of agentic large language models (LLMs) to genuinely comprehend natural human language and convert it into meaningful, actionable outcomes: crucially, without the need for human intervention.
Historically, achieving this level of understanding required complex, deterministic natural language understanding (NLU) flows that were difficult to build and scale. This shift represents a fundamental change in what’s now possible with AI and has driven a significant uptick in automation.
Q. Is there room to grow for automating the handling of customer contacts? Are there factors that are holding it back from answering a greater volume of interactions, and their types and value?
Kevin McNulty:
There’s absolutely room to grow, not just in volume, but in the types of interactions automation can handle.
Most organizations have only scratched the surface. Basic inquiries and transactional tasks are being automated, but more complex, high-value interactions still largely rely on live agents. The gap isn’t due to a lack of potential: it’s due to limitations in tooling, data, and trust.
“AI is now assisting human agents during complex interactions, automating back-office tasks...” —Kevin McNulty
Traditional automation often breaks down when interactions require reasoning, involve multiple systems, or demand personalization. Many platforms still rely on rigid scripts, brittle integrations, and siloed data, which makes it hard to scale beyond narrow use cases.
And while Generative AI has opened the door to more natural conversations, businesses are rightly concerned about risk, hallucinations, and control.
This is where multi-agent, governed automation—like what we’re seeing with emerging Agentic AI platforms—offers a path forward.
By using AI Agents that collaborate, draw from a shared data foundation, and operate with human-in-the-loop guardrails, automation can take on more nuanced, outcome-driven work. The shift is from automating tasks to automating outcomes. And that’s where real value gets unlocked.
Sarika Prasad:
While automation has made significant inroads into contact center operations, there are still some key areas for growth. Specifically, when it comes to earning consumer trust and delivering on rising expectations for personalized, engaging service.
Today, AI agents are utilized in contact centers to streamline routine tasks and enable faster response times. However, despite growing adoption, a consumer trust gap persists. According to our research, while 59% of consumers are open to using AI chatbots for quicker resolutions, 30% still question their accuracy.
And the margin for error is thin with more than a third of customers (36%) reporting that a single bad experience would deter them from using chatbots again.
To close this trust gap, brands must go beyond simply deploying automation: they must craft experiences that are reliable, intuitive, and human-centric. This means prioritizing accuracy, clear communication, and seamless handoffs to live support agents when needed. The success of automation hinges not just on functionality, but on engagement, design, and transparency.
Beyond trust, there’s also growth potential in the scope of interactions automation can handle. AI today excels at routine service needs, including FAQs, password resets, and appointment scheduling, but the real opportunity lies in scaling up to more complex, higher-value engagements. Specifically, advanced troubleshooting, proactive issue resolution, intelligent upselling, and deeply personalized guidance.
“...while 59% of consumers are open to using AI chatbots for quicker resolutions, 30% still question their accuracy.”
—Sarika Prasad
However, what holds automation back from this next leap is a familiar challenge: the lack of emotional intelligence and contextual reasoning. These human qualities are still critical when dealing with sensitive or nuanced customer needs. Until Agentic AI advances to the point where it can mimic these capabilities reliably, human and AI collaboration will remain essential.
Moving forward, organizations must design AI not just to answer questions, but to understand people, their context, their preferences, and their emotions.
Neeraj Verma:
There’s tremendous potential for growth in AI and automation, but several key barriers are preventing organizations from fully realizing that promise.
One of the biggest challenges facing enterprises today is their existing infrastructure. Effective AI integration depends on robust APIs and real-time access to clean, connected data.
Yet many organizations are still operating with a patchwork of legacy systems and siloed solutions: a situation we often refer to as a “frankenstack.” These fragmented environments limit AI’s ability to deliver accurate, timely insights and actions.
This kind of disjointed architecture leads to operational inefficiencies, isolated data pools, and inconsistent CXs. Worse, layering AI or automation on top of these systems often magnifies the problems. Take chatbots, for example. Without access to unified customer data, they provide generic or inaccurate responses, which only frustrates customers rather than improving service.
As AI and automation continue to play a more central role in customer engagement, a unified AI platform becomes critical. It ensures smooth handoffs between automated and human-led interactions, streamlines workflows, and delivers the consistent, personalized experiences customers expect.
Are Humans Becoming the Exception in the Contact Center?
Automation in the contact center appears to be expanding rapidly. Accelerating by AI advancements (like Agentic AI) and the drive by the C-suite to adopt these tools to cut costs while enhancing revenue-growing strong, positive CXs.
The question, then, is: Have or will contact centers shortly cross the Rubicon where live agents are the exception, for limited uses, and where automated interactions are the norm? And if so, what roles do you see live agents playing?
Kevin McNulty:
We’re not crossing a line where live agents disappear. We’re redefining what it means to be an agent. The goal isn’t to replace humans. It’s to reimagine their role in a world where AI Agents can now handle a significant portion of the customer journey. That shift doesn’t make live agents obsolete; it makes them more valuable, but in different ways.
Instead of being front line responders for every issue, human agents are evolving into orchestrators, advisors, and specialists. They’re managing more complex scenarios and, in some cases, overseeing a team of AI Agents working in parallel.
Picture an agent collaborating with a billing agent, a fraud-checking agent, and a scheduling agent—all digital—and stepping in only when judgment, escalation, or human empathy is needed.
This isn’t science fiction. It’s the next logical step in contact center evolution: AI Agents and humans working side by side as true teammates.
Automation becomes the norm, but humans still provide the oversight, the escalation path, and the creative problem-solving. In this model, the contact center doesn’t lose its people. It frees them to focus on what machines can’t do well alone: context, trust, and high-stakes decision-making.
Sarika Prasad:
While Agentic AI is rapidly transforming contact center operations, it has not yet reached the tipping point where live agents are the exception. That said, it is quickly getting closer.
AI Agents are already handling a growing share of customer interactions, from responding to routine inquiries and managing self-service journeys to executing satisfaction surveys. These capabilities are redefining efficiency and consistency across contact centers.
However, when interactions go beyond the basics, like FAQs, scheduling, or initial routing, the human touch remains essential. Live agents are still the frontline for complexity, emotion, and high-stakes conversations. From resolving nuanced service issues to offering emotional support during distressing moments, their role is irreplaceable.
AI, for all its speed and scalability, still struggles to grasp the subtle cues of human communication, such as tone or emotional undertones: the very elements that often define a great CX.
As Agentic AI continues to evolve, its ability to manage more sophisticated interactions will grow. In time, we’ll see AI take on more advanced tasks, such as basic troubleshooting, contextual personalization, and intelligent escalation.
This evolution does not render live agents obsolete, rather it redefines their role. They’ll become “experience architects”, stepping in not just to solve problems, but to shape meaningful moments. They’ll be the voice of trust in emotionally charged scenarios, especially in industries like healthcare and financial services, where stakes are high, and empathy matters most.
In this future, the contact center isn’t automated or human: it’s harmonized. And that harmony will be the key to both efficiency and enduring customer loyalty.
Neeraj Verma:
Customer service is undergoing a major transformation, with AI-driven automation becoming the new standard for managing routine interactions.
But this doesn’t mean live agents are becoming obsolete: far from it. Their roles are evolving, and they’re now working alongside AI as a powerful partner. AI augments agents by delivering real-time insights, flagging potential vulnerabilities, suggesting appropriate responses, and handling repetitive tasks.
This is freeing agents to focus on what matters most: interactions that are complex, emotionally nuanced and require critical thinking, empathy, and creativity. Areas where AI still has limitations. These high-value interactions demand personalized solutions and a human touch that only a live agent can provide.
This partnership between humans and AI isn’t just efficient: it’s transformative. It enables smarter, faster, and more empathetic service, ultimately elevating the CX.
Q. What are your recommendations when choosing, deploying, and using inbound and outbound customer contact applications?
Kevin McNulty:
The most important recommendation is to shift the mindset from choosing individual applications to selecting a platform: specifically, one designed for Agentic AI.
Traditional customer contact applications were built around static workflows and point solutions. But as AI becomes more capable, the future lies in dynamic, intelligent systems where AI Agents can reason, act, and collaborate to automate complex journeys. That requires a platform with multi-agent orchestration at its core.
That said, transformation doesn’t need to happen all at once. Organizations should start by identifying high-value use cases where automation can deliver quick wins: whether that’s automating outbound appointment reminders, assisting agents during after-call work, or improving identity verification. From there, AI agents can be added, refined, and scaled gradually based on performance and business need.
Equally important is choosing a solution that makes it easy to manage and govern AI, with tools for testing, oversight, and human-in-the-loop controls.
The right platform won’t just support today’s use cases; it will evolve with the business. The companies that succeed will be those that move with purpose, align around value, and build on a foundation designed for what’s next.
Sarika Prasad:
When choosing, deploying, and utilizing inbound and outbound customer contact applications, it’s essential to go beyond simply choosing a platform that offers automation. Instead, I recommend companies take a more strategic approach, evaluating how their customers interact, what they expect, and where human connection still matters most.
For example, organizations in high-trust, high-stakes industries like healthcare or financial services must prioritize solutions that enhance and empower human agents, rather than fully replace them. These interactions often involve emotional nuance, sensitivity, compliance considerations, and personalized problem solving that automation alone can’t reliably deliver.
In industries like retail or travel, where routine inquiries such as checking order status, processing returns, and managing reservations are more common, high-volume, high-efficiency automation would be most effective.
Regardless of industry, automation should never be “set and forget.” The key to ongoing success lies in the regular evaluation of both the customer and agent experience. Is the system reducing wait times? Are issues resolved on first contact? What does sentiment analysis reveal about satisfaction or frustration?
Regular audits, supported by real-time analytics, customer feedback, and agent insights, ensure that automation tools stay aligned with evolving expectations. The more visibility you have into real consumer behavior, the more effectively you can improve your contact center strategies to balance effortless use, efficiency, and engagement.
Neeraj Verma:
When it comes to choosing, deploying, and using Agentic AI, there are a few key strategies that can make a real difference.
First, start with analytics. Think of analytics as your roadmap; it helps you to understand where the biggest challenges lie and where you can make the most impact. By using Agentic AI to analyze customer interactions, you can pinpoint the areas that need the most attention. This way, you’re not just guessing where improvements are needed: you’re making data-driven decisions.
“And as this technology matures, I believe we’ll see a tipping point where outbound interactions vastly outpace inbound ones.” —Neeraj Verma
Next, focus on human augmentation. AI is a powerful tool that can enhance the capabilities of human agents. Think of AI as a partner that helps them work more efficiently and accurately, while ensuring customer service excellence.
Is Outbound Coming Back?
Outbound customer contact benefits organizations and customers and prospects alike by presenting both critical, helpful information and attractive offers.
Unfortunately, this engagement method has been struggling, what with rampant fraud, overly aggressive practices, and resulting customer distrust and annoyance, leading to tough, restrictive legislation.
But could outbound be coming back? What changes are happening that could make it more effective and acceptable?
Here’s what two of our panelists had to say.
Kevin McNulty:
Outbound customer contact is undergoing a reinvention. Historically, outbound meant either dialing for dollars or triggering follow-ups based on static rules. But with smarter AI, better data integration, and rising expectations for personalization, it’s becoming more predictive, relevant, and omnichannel.
At the same time, there’s no ignoring the elephant in the room: fraud and spam have trained customers not to pick up the phone. Robocalls, spoofed numbers, and irrelevant outreach have created a trust gap that impacts even legitimate brands. Consumers are answering fewer calls and ignoring unknown numbers, which means outreach must now be more thoughtful, transparent, and permission-based.
What’s changing is the how and why of outbound. It’s less about pushing messages and more about orchestrating engagement.
AI is helping determine the right time, channel, and content for outreach: whether it’s a proactive message about a delay, an automated loyalty offer via SMS, or a copilot nudging an agent to follow up on an abandoned cart.
Done right, outbound becomes part of the experience, not an interruption. The key is context: when outreach is timely, relevant, and clearly tied to a known relationship, customers are far more likely to engage.
Neeraj Verma:
Outbound engagement has advanced dramatically: not just in volume, but in sophistication. Today’s outreach is more timely, relevant, and personalized. We’re seeing this transformation firsthand where intelligent outbound interactions are driving measurable gains in customer engagement and operational efficiency.
The use of agentic for proactive customer interaction extends far beyond traditional outbound campaigns. Organizations are now leveraging AI for fraud detection, accident response, and even real-time event-driven outreach.
For example, instead of a customer calling their insurer after an accident, the insurer contacts them to say they are aware and are working on a claim. Or consider vehicle theft: instead of the owner reporting a stolen car, the car itself flags the incident and notifies the owner.
These are powerful shifts. Agentic AI is not just reacting to customer needs: it’s anticipating them. And as this technology matures, I believe we’ll see a tipping point where outbound interactions vastly outpace inbound ones. That’s not just a trend: it’s a reflection of what customers increasingly expect: intelligent, timely, and proactive experiences.