Customer interactions can be likened to railroad cars in a train being pulled by a locomotive. Each interaction, like the train, is on a journey, and each customer has a destination, like the cars that are carrying the products.
On locomotive-powered trains, couplers or drawbars enable the railroad cars to move forward by transmitting the energy from the locomotive(s) propulsion systems. Contact centers perform a similar function with customer interactions. The agents (human and now AI) pull the customers along.
But how the trains and contact centers perform depends on many factors: like the power of the companies that are moving them, operational environments, and the tightness of their connections. They share the challenges of avoiding splitting apart or, worse yet, derailments.
To find out how the customer interaction train is moving, to see how clear the track is, but also to look forward and respond to any troublesome issues, we had virtual conversations with our panel of industry supplier experts. They are:
- Sarita Fernandes, Vice President, Product Management, Avaya
- Lisa Orford, Global Vice President, Product Management, Contact Center, 8x8
- Sarika Prasad, Director, Product Marketing, Five9
Q. What changes, trends, and developments have you seen emerge over the past 12 months that impact customer contact, and what are their drivers?
SARITA FERNANDES
Over the past 12 months, customer contact has become much more dynamic. Organizations are moving away from fixed workflows and toward systems that can respond in real time, based on what’s happening in the interactions.
Customer expectations are a big driver. People expect companies to understand their histories, intent, and context across every channel. At the same time, enterprises are trying to connect fragmented systems and make faster decisions with better data.
As a result, customer interactions are becoming a much more valuable signal for each business. Each conversation reflects customer sentiment, intent, and the state of the relationships at that moment.
That shift is increasing demand for technologies that can work with live data. Approaches like model context protocol (MCP)-enabling platforms allow AI systems to access and act on real-time context, making interactions more relevant and responsive.
We’re also seeing more focus on how AI and human agents work together. The most effective models bring both into the same workflow, with AI supporting speed and scale while humans focus on judgment and customer experience (CX).
This is what we call “tandem care”, a model of service where AI agents and human agents collaborate in real time, each amplifying the strengths of the other to improve outcomes.
This coordination allows for seamless transitions between self-service and human agent assistance, ensuring that full context and progress are preserved while delivering an effortless customer and employee experience.
LISA ORFORD
The biggest shift I’ve seen is that customers have stopped being patient. They expect you to know who they are, remember what happened last time, and resolve their issues fast, regardless of which channel they’re using. And when you can’t deliver that, they leave. It’s that simple.
What’s driving it? AI has reset expectations. People have experienced genuinely helpful automated interactions, so now they hold every contact center to that standard.
The organizations that are struggling most right now are the ones with fragmented systems; they can’t deliver a connected experience because their data is scattered across tools that don’t talk to each other.
The contact center used to be a cost center you managed. Today it can make all the difference in a competitive market. The leaders who recognize that are investing in platforms that unify the CX. The ones who don’t are losing customers to competitors who have.
SARIKA PRASAD
Over the past year, contact centers have shifted from AI hype to operational reality. Teams are no longer just experimenting with AI. They are implementing it at scale and across the full customer journey.
AI solutions now go well beyond managing simple FAQs. They power intelligent triage, real-time quality management, and end-to-end self-service. Voice AI has matured dramatically. Interactions that once felt robotic now feel fast, natural, and contextually aware.
At the same time, younger customers are raising the bar. Customers expect AI-powered service as standard, with a clear path to a human agent when needed.
“As AI continues to evolve, its usage is going to go much further, becoming more mature, reliable, and more widely adopted...”
—Sarika Prasad
Yet technology alone is not enough. CX has become the defining battleground for brand loyalty.
- Businesses must create experiences that feel effortless, personal, and deeply human.
- There is a growing recognition that AI must augment, not replace, human empathy.
- Trust and governance around AI deployment are now boardroom priorities, not afterthoughts.
This shift is being driven by a fundamental change in mindset. Contact centers are a growth engine, not just an operational cost. When organizations get this balance right, combining smart automation with genuine human connection, they will not just resolve issues. They will build lifetime customer loyalty.
Q. What is the state and direction of AI in the contact center? Is it mature and reliable, or does it still have issues? Are contact centers realizing benefits from it and if so, where?
Further, what is AI being used predominantly for? Assisting live agents to improve the CX? Or to deflect and/or shorten live agent contacts and reduce the need for staff?
SARITA FERNANDES:
AI is delivering real value today, especially in areas like agent assist, summarization, and automating routine interactions. Many organizations are already seeing improvements in efficiency and productivity.
Where it still falls short is consistency. In a lot of environments, AI doesn’t have full visibility into the customers, the interaction histories, or what’s happening across systems. When that context is missing, the outputs can feel disconnected.
That’s why more of the focus is shifting to how systems connect and share information in real time. With MCP, AI can access live, contextual data during the interactions, improving both accuracy and relevance.
“...AI and human agents should be working from the same information. Clear handoffs and shared context go a long way in keeping the experience consistent.” —Sarita Fernandes
Contact center agents can now be more proactive with customer responses, as they’re able to leverage the power of AI to easily access a vast array of data points, such as customer profile and demographics, behavioral data, transactional data, and more.
Right now, the most common use cases are clear. AI is helping agents with real-time guidance and next best actions. It’s handling routine inquiries, directing requests to the right places, and turning conversations into structured data that the business can actually use and learn from over time.
As that happens, the connection between AI and human agents becomes more important. When both are working from the same context, transitions are smoother, and the experience stays consistent.
All of this also depends on having the right governance and data controls in place, so organizations can trust how AI operates and how customer data is being used.
LISA ORFORD:
AI is delivering real value, but not universally and not by accident.
Where it works well: agent assistance. Real-time coaching, post-call summaries, and surfacing the right information at the right moments. Agents handle calls faster, with more confidence, and customers feel the difference: faster resolution, less repetition, and fewer transfers.
Where AI falls flat is when organizations deploy it on fragmented infrastructure. A chatbot that doesn’t know what happened on the phone last week isn’t helpful; it’s just another thing customers have to work around. Those failures aren’t AI problems; they’re data problems.
“AI has reset expectations. People have experienced genuinely helpful automated interactions, so now they hold every contact center to that standard.” —Lisa Orford
The honest truth is AI will handle an increasing share of routine contacts. That’s not a threat; it’s an opportunity if you’re prepared for it.
It means live agents can focus on the interactions that actually benefit from human judgment and empathy. The contact centers winning right now are the ones using AI to make human interactions better, not just to reduce headcount.
SARIKA PRASAD:
AI in the contact center is maturing quickly, reshaping how we know it today.
AI is not just an automation process making back-office operations smoother; it is now a key business lever at the front lines of the customer CX journey. It’s highly effective at handling high-volume interactions, improving routing, and supporting agents with actionable insights in real-time.
That said, adoption rate and meaningful value are not the same thing. While over 80% of contact centers have adopted AI, according to our 2025 Business Leaders CX report, many are still closing the gap between deployment and real operational impact.
Unfortunately, urgency is outpacing readiness. Consequently, and not surprisingly, organizations rushing to implement without the right data infrastructure, integration maturity, or governance foundations are finding it difficult to scale beyond early pilots.
As AI continues to evolve, its usage is going to go much further, becoming more mature, reliable, and more widely adopted, making automated CX operations more efficient and seamless.
AI has multiple use cases within the contact center and across the CX journey, being most widely used to gather key information, quickly resolve routine tasks, and seamlessly route interactions to humans or other digital channels, when needed. So, organizations must get the implementation right to benefit.
The dominant model emerging is not AI replacing agents. It is automating where possible, with humans handling what matters most. AI handles volume. Humans handle value. Complex, emotionally nuanced conversations remain firmly in human hands.
Finally, how contact centers measure success is shifting. Handle time gives way to Customer Effort Score, Net Promoter Score, and real-time sentiment as the metrics that truly reflect AI’s impact on CX.
Q. What are your recommendations when choosing, deploying, and using inbound and outbound customer contact applications?
SARITA FERNANDES:
The biggest thing is to look at how everything works together, not just the individual tools, and to select a platform that’s open and does not lock you into a vendor.
A lot of organizations solve for specific use cases, but the systems don’t share context or connect well. That creates friction quickly, both for agents and customers.
Interoperability is key. Systems should be able to connect across channels, workflows, and AI models. Flexibility matters too, especially as the AI landscape continues to evolve. Supporting both private and public models helps avoid having to start over later.
Real-time context is another big factor. Applications perform better when they can understand what’s happening in the moment and adjust. More dynamic workflows tend to handle real-world variability better than rigid ones.
It’s also important to think about how interaction data is used. Every conversation is a signal. When that data is captured and used effectively, it helps the business make better decisions and spot trends earlier.
On the operational side, AI and human agents should be working from the same information. Clear handoffs and shared context go a long way in keeping the experience consistent.
Finally, deployment flexibility and governance still matter. Most organizations are balancing cloud and on-prem environments while meeting security and compliance requirements. The solution should support that without adding unnecessary complexity.
If those pieces are in place, it becomes much easier to build a system that can adapt and improve over time.
LISA ORFORD:
The biggest mistake I see is purchasing point solutions that solve today’s problem but create tomorrow’s headache. If your inbound, outbound, and AI systems don’t share the same customer data, every interaction starts from scratch. Unified data isn’t a feature: it’s the whole game.
Sequence your AI investments carefully. Automation works best on top of mature operations with real interaction data.
If you deploy AI before you understand your contact patterns, you’ll get low containment rates and frustrated customers. The data you collect in Year One is what makes AI actually useful in Year Two.
Finally, measure what customers experience, not just what agents do. Volume metrics tell you how your operation is running.
First contact resolution (FCR), customer effort, and loyalty tell you whether it’s actually working. Build your evaluation framework around outcomes from the start: that’s the only way to know if the technology is earning its keep.
SARIKA PRASAD:
Simply choosing a platform and setting it and forgetting it is no longer enough when managing customer contact applications.
For businesses to see true success, they need to have full visibility into their contact center operations, leaning on that data to develop informed, strategic plans based on their functions or product/service lines for CX decisions and deployment. As examples:
- Organizations that typically manage more complex interactions, such as financial services, healthcare, or government, need to focus their investment and automation strategy around information security, seamless escalation, and human agent empowerment.
- Organizations in the retail space must prioritize solutions that are built for managing high-volume inquiries, quickly and effectively.
To ensure customer contact platforms continue to align with company business goals and customer expectations, I recommend conducting regular audits, gathering real-time analytics, and regularly sourcing customer feedback and agent insights.
The more data leaders have into resolution, consumer behavior, and agent experience, the more successful they will be in optimizing their contact center strategies and turning CX into a revenue driver.