But today, we ' re seeing AI not only enhance existing products but also create an entirely new class of intelligent systems.
In the IVR space, we ' re now seeing the emergence of AI agents, which are virtual assistants capable of having natural, dynamic conversations with customers. These aren ' t just menu-driven bots. They understand intent, access back-end systems, and provide contextual responses in real time.
AI also plays a critical role in intelligent call routing, which is much different from static skills-based routing, by enabling smart, real-time decision-making that matches the customer with the most suitable human agent based on predicted outcomes, sentiment, and agent performance data.
On the agent side, AI is being tightly integrated into CRM and desktop applications, as I noted earlier. It provides real-time assistance by surfacing relevant customer insights, suggesting responses, and even summarizing interactions, which helps agents have more meaningful and productive conversations.
So, to answer the question, AI is being deployed both within existing contact center platforms and through the creation of new AI-native solutions. Together, they are shaping a more proactive, intelligent, and customer-centric contact center ecosystem.
"... AI IS BEING DEPLOYED BOTH WITHIN EXISTING CONTACT CENTER PLATFORMS AND THROUGH THE CREATION OF NEW AI-NATIVE SOLUTIONS."
CONVERSELY, ARE THERE CONTACT CENTER PRODUCTS THAT YOU DO NOT BELIEVE WILL USE AI AND IF SO, WHAT ARE THEY AND WHY?
A: Basic configuration tools and agent desktop settings typically don’ t gain much from AI since they are more static and rule-based. However, I believe most other contact center products will increasingly leverage AI capabilities.
That said, certain industries— like healthcare— may limit the use of AI due to strict compliance requirements and the sensitive nature of customer data, such as protected or personal health information( PHI). In these cases, organizations often balance AI adoption with regulatory constraints and privacy concerns.
AI VALUE MEASUREMENT
IS THERE AN ADDED PREMIUM WITH THIS TECHNOLOGY TO RECOUP THE COSTS ON DE- VELOPING IT ON THE PRICES OF CONTACT CENTER SOLUTIONS? OR ARE THE VENDORS PASSING THEM ON? OR ARE THEY ABSORBING THEIR OUTLAYS?
A: I don’ t believe there’ s a distinct premium charged specifically for AI in contact center solutions, at least not today.
AI capabilities are increasingly integrated as standard features across major platforms like Genesys, AWS Connect, and NICE, because companies expect these intelligent functions as part of the offering. Vendors include AI to stay competitive, so it’ s usually not a separate cost line item.
HOW CAN THE BUSINESS CASE BE MADE BY CONTACT CENTERS FROM THE ADDED COSTS / INVESTMENT IN AI TOOLS? HOW CAN THE GAINS ATTRIBUTABLE TO AI BE MEASURED AND TRACKED?
A: From a business standpoint, the value of AI shows up in better CXs, higher agent productivity, and greater operational efficiency, which lead to cost savings and revenue growth. To measure AI’ s impact, companies track metrics such as average handle time( AHT), first call resolution( FCR), customer satisfaction, and self-service adoption.
AI IS PROMISING AND, BY SOME ACCOUNTS, IS DELIVERING IMPROVED AGENT PRODUCTIVITY AND WORKFORCE REDUCTION( ALSO SEE BOX“ WILL AI SHRINK THE CONTACT CENTER?”). BUT HOW DO YOU MEASURE AND TRANSLATE PRODUCTIVITY IMPROVEMENTS TO STAFFING E. G., FULL-TIME EQUIVALENTS( FTES) TO PLAN AND SIZE WORKFORCES?
A: Measuring AI-driven productivity improvements and translating them into staffing needs like FTEs involves a combination of quantitative metrics and real-world observation.
Key performance indicators( KPIs) such as AHT, FCR, and call containment rates help quantify efficiency gains. For example, if AI-powered self-service or agent assistance reduces AHT by 20 %, you can estimate the corresponding reduction in workload and adjust FTE requirements accordingly.
However, it’ s important to combine these metrics with continuous monitoring because AI’ s impact can vary by contact type and customer segment. Workforce planning should also consider factors like call volume fluctuations, complexity of issues, and the need for human empathy in certain interactions.
In essence, productivity gains from AI provide a strong starting point, but smart workforce sizing is an ongoing, dynamic process that blends data with human judgment.
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