Image source: By Teja Pavan Kalyan Tangellapalli
Early in my career, I worked as an Associate Consultant on a healthcare contact center project, helping migrate operations from Genesys to cloud CCaaS platforms such as Five9 and NICE CXone. I went in thinking it was a platform move. I came out understanding that a contact center is an ecosystem, and that AI only works well when that ecosystem is sound.
The contact center is more than a place where calls are answered
Contact centers used to revolve around IVR menus, queues, ACD, and agent availability. Today they involve cloud platforms, CRM systems, APIs, analytics, and AI. Through my work as an Associate Consultant on a healthcare contact center project, I saw the biggest shift firsthand: the move from routing a customer to the right agent toward understanding the customer before the interaction ever reaches an agent.
AI is not replacing the architecture that came before it. It is being added on top of it, and my project work made that very clear.
What moving from Genesys to CCaaS really involved
On that project, I supported the migration of contact center operations from an established Genesys environment to cloud platforms such as Five9 and NICE CXone. Working with the platforms side by side taught me how differently each one handles routing, scripting, and integrations.
My part of the work was not simply recreating call flows. It was understanding why they were built the way they were, which business rules were buried in them, and which systems depended on them. In healthcare, a poorly handled call is more than an inconvenience, so I treated continuity as just as important as the new technology.
A modern contact center rarely runs in isolation. It talks to CRMs, customer databases, knowledge bases, workforce management, identity systems, and third-party applications. The customer hears a voice prompt or an agent, but behind that conversation several systems are exchanging data in real time. That is why APIs and integrations became central to the work I did.
AI needs context to be useful
A common misconception is that adding an AI model automatically improves customer experience. From what I saw during the migration, it does not. A conversational AI can understand a question perfectly and still fail if it cannot reach current order status, prior interactions, or the right business rules.
In my experience, the next phase of contact center AI will be less about picking the most powerful model and more about connecting it to the right enterprise data.
Routing is getting smarter
Routing was a core part of the migration work I handled. Traditional routing uses queue, skill, priority, language, and hours. AI adds a layer: identifying intent before the call reaches an agent. A customer describing a payment problem in their own words often gives better information than pressing a billing option in a menu.
The goal is not faster routing. It is better routing. After working through routing logic across Genesys, Five9, and NICE CXone, I am convinced that a fast connection to the wrong resource is still a bad experience.
AI agents and agent assist
AI agents can handle account information, scheduling, order status, basic troubleshooting, and FAQs. But based on the IVR and self-service flows I worked with, the best model is not AI handling everything. It is AI handling what it can, and humans handling what needs judgment. When AI escalates, it should pass along the context and the reason, so the agent does not start from zero.
Agent assist deserves equal attention. AI can summarize conversations, surface knowledge articles, suggest next steps, flag compliance items, and draft post-call notes. The agent stays responsible, but the manual work shrinks.
CRM integration makes or breaks the experience
Excellent AI can still deliver a poor experience if the CRM integration is weak. While supporting the project, I saw how much agents depend on knowing who the customer is, their open cases, history, and account status. If that data is scattered or arrives late, agents spend time searching instead of helping.
New capabilities bring new risks
An AI that answers general questions is very different from one that can change records, issue refunds, or reschedule appointments. Organizations need clear controls around authentication, authorization, data access, auditability, escalation, monitoring, and privacy.
Least privilege matters most: an AI agent should access only the systems and actions it truly needs, and organizations should be able to trace why it did what it did. Working on a healthcare project made this very real for me.
AI will not fix poor architecture
If the IVR is confusing, AI will not fix the journey. If CRM data is wrong, AI will decide on wrong information. If APIs are unreliable, AI will not get what it needs. The migration taught me to map the whole journey first: where the interaction starts, what is collected, where it is stored, which systems need it, where it transfers, and what happens when something fails.
Measure more than containment
Containment is useful but incomplete. Having supported contact center operations through a platform change, I look at first-contact resolution, customer satisfaction, effort, transfer rate, handle time, repeat contacts, agent productivity, AI accuracy, and escalation quality as well. High containment means little if customers are not getting their problems solved.
The future is hybrid
AI will handle repetitive work, assist agents, identify intent, and summarize. Humans will handle complex, sensitive, empathy-driven situations. From my own project experience, AI should be treated as part of an ecosystem, not a feature you switch on.
What contact center professionals need to learn
IVR, ACD, and routing still matter, but they are no longer enough. Engineers now need to understand CCaaS, APIs, CRM, cloud architecture, automation, AI, data, and security. Having worked across Genesys, Five9, and NICE CXone, I have seen that professionals who understand how the pieces fit together shape the customer experience most.
The real opportunity
The opportunity is not a machine answering a question. It is a contact center that knows why the customer is calling, has the right information, chooses the right path, supports the employee, and knows when a human should take over. That takes good architecture, reliable integrations, thoughtful routing, and clean data, not just an AI model.
The objective stays simple, and it is what guided my work on the project: make it easier for customers to get help, and give employees the tools to provide it.
About the Author
Teja Pavan Kalyan Tangellapalli is a Contact Center Technology professional with experience in IVR, ACD, intelligent routing, CRM integrations, and cloud contact centers. He began his career as an Associate Consultant on a healthcare contact center project, where he supported the migration from Genesys to CCaaS platforms including Five9 and NICE CXone. His interests include CCaaS architecture, contact center modernization, and the practical application of AI in customer service.
