Dr. Pete Cornwell, Director of Contact Center, offers a fresh perspective on customer care and is sharing his wealth of knowledge at Customer Contact Week in Las Vegas. With over 35 years of experience spanning information systems, design, architecture, and consulting for industry leaders like Terazo and Blue Cross Blue Shield North Carolina, his expertise runs deep. Add to that a decade as a professor and chair of Engineering and Information Sciences at DeVry University, and it’s clear that Dr. Cornwell has plenty to say about the ever-evolving world of digital transformation.

Before heading west for the conference, I sat down with him to glean some insights he’ll be sharing with attendees and partners alike.

Take a Seat, Class is in Session

Our conversation was set to focus on AI and its applications in the contact center, but as I launched into my questions, Dr. Cornwell first asked me to examine a meme.

If you’re unfamiliar with the 1987 movie The Princess Bride, you’re not only missing out on a cherished piece of nostalgia, but you’ll also need a bit of background to understand his analogy. In the film, the protagonist delivers a famous line to a villain who repeatedly uses the word “inconceivable”, even when things are clearly very conceivable.

Pete followed up the meme by saying that if there’s any term that makes him grind his teeth more than Digital Transformation, it’s Agentic AI. It’s tossed around daily as a flashy, vague placeholder for everything from artificial intelligence and large language models (LLMs) to integrations and machine learning (ML)-driven workflows. This misuse is particularly troubling in the contact center space, where it has become a buzzword applied to almost anything.

Pete is intent on drawing a clear distinction between AI and Agentic AI from a customer contact perspective. Both are critical components of today’s AI-driven customer care, but Agentic AI is poised to unlock a wealth of future opportunities in this space.

“Simple” AI

While LLM-based models are incredibly complex, AI is often used for relatively simple applications in customer service, such as self-service, agent deflection, or assistance. Many companies begin their AI journey by deploying it to deflect calls from human agents, handling straightforward tasks like providing business hours, account balances, or credit card activation.

Additionally, voice and text-based chatbots can support intelligent routing, allowing customers to bypass frustrating IVR menus and connect directly through an Intelligent Virtual Agent (IVA). Yet, despite these capabilities, this is still not Agentic AI, these functions serve as filters between customers and human agents, managing deflection or routing rather than true autonomous decision-making.

The space continues to evolve as new AI-driven capabilities are added to CCaaS (Contact Center as a Service) offerings each year. AI-powered agent prompting, coaching, and even translation are all part of what’s possible. While Pete admits his skills as a clairvoyant aren’t highly rated, he predicts that these capabilities will become commoditized within five years, standard features in the arsenal of any CCaaS vendor. He invites anyone to call him out if he’s wrong, he’ll be waiting.

What Is Agentic AI?

If you want a simple definition of what agentic means, it’s actually embedded in the term. Agentic AI is used to describe LLM-driven software that can execute sufficiently complex workflows that it could replace an agent. This typically means that like a human agent, agentic AI will need to make decisions in a highly variable data environment, sourced from both the customer and via integration.

From an implementation perspective, like its human equivalent we want to give each unique agent type a well-defined set of responsibilities to achieve advantages of understandability, maintainability, error management and observability. Similarly, many of the communication and business metrics we use to measure the performance of human agents can apply to their agentic counterparts. For example, a credit card company could conceivably use agentic AI for everything from general service enquiries to fraud reporting, and even customer satisfaction surveys.

What we can draw from this example is that we open the possibility for collaboration with agents coordinating activity to fulfill extensive tasks that would often require multiple human representatives and frustrating delays as the customer is transferred between departments.

Agentic AI in Action

Pete expanded on this concept using a lost credit card scenario, illustrating how Agentic AI can streamline customer service. This process involves four AI components:

1. A simple AI IVA chatbot that will provide voice-based routing for a customer. The following agentic components (“bots”) that will use natural language processing and output to speak to the customer.
2. General Customer Service – agentic AI designed to handle a range of customer service scenarios from simple balance inquiries, new card verification and of course then going on to lose it and requiring a replacement.
3. Fraud Handling – a bot designed to establish and open a fraud investigation.
4. Customer Survey – a bot that will craft an optional customer satisfaction survey based on the workflow delivered to the customer.

These agentic bots, coupled with the aforementioned IVA have the capability to provide a seamless flow of interaction with the customer. Consider the following voice flow:

1. An anxious customer calls the customer service number, after a prompt, the caller simply says “I’ve lost my card.” The Simple AI IVA routes the call to the General Customer Service Bot.
2. The General Customer Service bot first validates the customers identity and the missing card number [omitted to save trees and your time]. The customer reciprocates with the correct information.
3. The General Customer Service bot then asks the customer when they believe they lost the card. “Sometime last week, I don’t use it much and when I looked in my wallet I couldn’t find it” the customer answers.
4. There are recent transactions on the card, so the card account is passed to the Fraud Handling bot. Meanwhile the General Customer Service verifies the address on file with the customer and calls back-end services to print and dispatch a new card.
5. The Fraud Handling bot then asks the customer to verify a predetermined number of recent transactions from the account.
6. The customer replies “I’ve never been to Cancún” when presented with a specific transaction involving a Mexican resort and spa. The Fraud Handling bot opens a fraud case, again using a back-end integration.
7. The General Customer Service bot gives the customer a claim fraud case number and a delivery time for the card.
8. Finally, with assent from the customer (hopefully now less stressed) a Customer Survey bot draws a set of questions drawn banks associated with the bots they interacted with. The customer responds, the data is logged and the call ends.

Pete recalled a similar experience that required three different human agents, each with a long wait time between transfers. This AI-driven approach achieves the same outcome but with major advantages:

• No human were required for the workflow.
• The customer perceives interacting with a single entity throughout the call.
• Reduced wait times and minimized anxiety about disconnections.

To Recap

Agentic AI has the potential to completely replace an agent through the provision of complex workflows driven by complex inputs and integrations. Simple AI provides simple self-service for deflection purposes and supports a live agent and/or can provide natural language driven call routing.  Finally both have a critical role to play in building an AI-driven contact center self-service experience.

Migrating to a cloud-delivered Contact Center as a Service (CCaaS) solution can revolutionize how your organization delivers customer service. However, this transition requires careful planning and execution to avoid disruptions. Assuming you have selected a CCaaS platform that aligns with your organizational needs, the following best practices outline key considerations for a seamless migration.

A successful migration to CCaaS not only enhances operational efficiency and scalability but also ensures a significant improvement in service delivery, directly impacting customer satisfaction and retention. Organizations should consider the risks of not embracing modern cloud-based customer care solutions, which can

include diminished customer service capabilities and potential costs due to outdated or inflexible systems. Moreover, organizations that delay this shift risk falling behind competitors who can adapt more quickly to market demands and customer needs. Thus, embarking on a well-planned migration journey is imperative for companies aiming to optimize their customer care operations and secure a competitive advantage in their respective markets.

1. Physical Infrastructure Migration

Understanding your current environment is critical for a successful transition. Start with a thorough site review to document the infrastructure and identify unique user requirements. Engage with call center managers, team leaders, and power users to uncover specific needs and configured features such as whisper settings, omnichannel components, call management, etc.

Factors such as bandwidth and latency are paramount for seamless operations. Evaluate your facility’s connectivity for both on-site and remote users, ensuring it aligns with the CCaaS product requirements. Fortunately, modern CCaaS solutions such as Amazon Connect, Twilio Flex and Five9 supply agent connectivity tools to verify that workers have sufficient resources to provide good customer service over various channels.

Additionally, document call treatments and station-specific configurations like call coverage paths. Legacy components requiring continued functionality should be cataloged to prepare for integration.

2. Change Management Planning

Change management is essential to mitigate risks and maximize adoption. A staged cutover strategy is recommended over a single-event migration, allowing for gradual testing and adjustments.

Develop a robust testing strategy to validate the platform’s performance under real-world conditions. Complement this with an organizational enablement strategy to train users and ensure they are comfortable with the new system. Adoption by your business units and users is one of the most critical factors which will determine the success of your CCaaS migration.

3. Operational Considerations

Operational continuity is vital during migration. Start by understanding the reporting requirements for business managers to ensure no loss of visibility into critical metrics. Additionally, review monitoring processes to maintain visibility into system performance post-migration.

4. Integration Planning

Integrating legacy infrastructure with the new CCaaS platform can present significant challenges. Document existing components, including FXO/FXS interfaces, Workforce Management solutions, FAX systems, wallboards, and specialty dialers. Verify that integrations comply with any regulatory requirements, such as HIPAA or FINRA.

Interactive Voice Response (IVR) systems often require specific integrations with local data sources or enterprise middleware. Assess these integrations to ensure call flows function as intended. For specialized applications, verify that they meet operational needs within the new environment.

5. Fault Tolerance and Disaster Recovery

Testing fault tolerance and disaster recovery capabilities are critical steps in any CCaaS migration. Develop and execute a failsafe testing plan to ensure resilience against both premise-level and carrier-level failures. It is important to align to your IT organization’s standards for recovery time objective (RTO) and business up-time expectations. Disaster recovery plans must reflect these measures and be tested to protect against potential downtime.

6. Scalability and Compliance

CCaaS solutions must scale with your business. Validate scalability by conducting load tests and documenting performance metrics. Compliance is equally important—ensure your migration adheres to industry standards like HIPAA, FedRAMP, or FINRA through thorough compliance testing and documentation.

Conclusion

A successful CCaaS migration hinges on meticulous planning, comprehensive testing, and strong change management. By following these best practices, you can minimize risks, ensure operational continuity, and set your organization up for long-term success with its new contact center platform. The result? An enhanced customer experience and a contact center infrastructure that grows with your business.