AI has been dominating the contact center conversation (as seen by the articles in our publication): and for good reason. The technology’s integration of vast data processing power with human-like learning, reasoning, and decision-making abilities, has given it considerable value.
AI’s attributes have the potential, now being gradually realized, to bolster organizations’ bottom lines on both sides of the ledger through the contact center:
- Revenue generation by delivering enhanced loyalty-and-sales-generating personalized customer experiences (CXs). This includes with better-informed and helpful contact center agents and more accurately targeted proactive customer and prospect outreach.
- Cost reduction through successfully deflecting more customer interactions to self-service. Also by enabling agents to handle challenging issues and sales opportunities faster with greater productivity.
As with any technological change, AI has been the subject of massive hype, including “me-too” marketing. There have been – and will continue to be - errors and mistakes, and lessons learned with them as well.
To get a handle on AI in the contact center and how organizations can best benefit from it, we spoke virtually with Vigneshwaran Jagadeesan Pugazhenthi.
Vigneshwaran is a contact center technology architect with a leading global company. He is also Chair of the Institute of Electrical and Electronics Engineers (IEEE) Computer Society, Richmond Chapter and a reviewer for the prestigious IEEE and Springer journals.
Vigneshwaran’s projects include:
- Led SIP migration during the COVID-19 pandemic while with Accenture that enabled 5,000-plus agents to work remotely.
- Developed AI-powered testing frameworks, also with Accenture, using Cyara, IBM Watson, and AWS Lex, which improved quality assurance (QA) accuracy across IVR and CRM systems.
Here is our interview:
Q. Let’s give some perspective on AI. I understand that AI is not new. When and how did AI become incorporated into contact center applications?
AI has been part of the contact center conversation for several years, initially more as a concept than a fully realized solution. Every time organizations faced challenges with call routing, customer satisfaction, or call containment, the ideas that surfaced often pointed toward intelligent automation: essentially early forms or precursors of AI.
“...it’s only with the maturity of AI technologies and the need for deeper automation that contact centers have started to embrace AI as a core, transformative capability.” —Vigneshwaran Jagadeesan Pugazhenthi
The early adoption phases involved integrating smarter routing strategies, experimenting with more adaptive IVR voices, and implementing natural language understanding (NLU). These were clear signals that AI was gradually entering the picture.
However, the real adoption of AI, in the forms of machine learning, dynamic workflows, and real-time contextual intelligence has taken off only in the more recent years.
While NLU and conversational elements began emerging around 2019, it’s only with the maturity of AI technologies and the need for deeper automation that contact centers have started to embrace AI as a core, transformative capability.
Q. What then happened to bring AI to the forefront and when?
Several factors pushed AI to the forefront in contact centers, and it really came down to a combination of business need and technological readiness.
Customer satisfaction, call containment, and cost optimization have always been core drivers. One consistent insight is that customers feel more satisfied when they can speak to someone or something that understands their issue. Traditional IVRs with rigid menu options fell short of this, so the demand for more intelligent, conversational systems naturally led to AI.
Then came the COVID-19 pandemic, which acted as a major accelerator. With the shift to remote work, organizations had to rethink how they supported customers and empowered agents. At the same time, customer expectations became more personalized; they wanted faster, more relevant, and more human-like experiences.
“Unlike earlier technologies like ACDs or IVRs that extended existing functions, AI is fundamentally transforming these systems.”
Finally, the rapid migration to cloud platforms, combined with leading providers offering powerful out-of-the-box AI models, made these capabilities not only possible but scalable. This convergence of business pressure and technological maturity is what truly brought AI to the center of the contact center strategy.
Q. Is AI turning out to be another iteration of automation in a long stream of them, going back to the first ACDs some 50 years ago? Or is it different and if so, how?
I believe AI is not just another step in automation but a true paradigm shift. Unlike earlier technologies like ACDs or IVRs that extended existing functions, AI is fundamentally transforming these systems. As examples:
- IVRs powered by generative AI are becoming conversational and context-aware, offering dynamic, natural interactions rather than repetitive prompts.
- On the routing side, AI moves beyond static, rules-based ACDs to enable real-time, intelligent call distribution and even assist agents by pulling knowledge directly from CRM applications.
- CRM software itself is evolving from task-driven tools to AI-powered assistants that reduce cognitive load and speed up decision-making.
In short, AI is redefining contact center technology into intelligent, adaptive platforms, marking a clear departure from traditional automation.
AI Application Usage
Q. Given what you said, does AI change contact center software and if so, how? What does it add or replace? Does AI alter how these programs operate? Including how contact center agents use them and how customers engage with them through self-service?
Absolutely. AI is fundamentally reshaping the future of contact centers. At its core, a contact center is all about CX: and AI brings a transformative potential to elevate that experience.
Traditionally, IVR systems have been rule-based and standardized, offering limited flexibility and failing to deliver truly conversational interactions. Customers often end up navigating rigid menus that don’t reflect the intent behind their calls.
AI changes that by enabling more natural, context-aware, and dynamic conversations. Technologies like natural language processing (NLP) and machine learning allow IVRs and digital assistants to understand intent and engage in human-like dialogs, dramatically improving self-service capabilities.
From the agent’s perspective, AI augments their role rather than replacing it. It can surface relevant customer profiles, suggest next-best actions, and even assist in responding while back-end systems are still loading. This not only improves productivity, but it also enables more personalized and informed conversations: something that directly impacts customer satisfaction and loyalty.
So yes, AI does change how contact center software operates -- both in how systems interact with customers -- and how agents use them. The result is a more intelligent, efficient, and customer-centric contact center.
Q. How do you see AI being employed in contact center solutions? Added to and/or bolted onto existing products? Or in a new class of AI-from-the-ground-up solutions?
AI is quickly becoming embedded across the entire end-to-end flow of modern contact centers. Traditionally, AI started with add-ons to existing systems, for example enhancing IVR with natural language capabilities or adding sentiment analysis to QA tools.
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 is being deployed both within existing contact center platforms and through the creation of new AI-native solutions.”
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.
Q. Conversely, are there contact center products that you do not believe will use AI and if so, what are they and why?
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
Q. Is there an added premium with this technology to recoup the costs on developing it on the prices of contact center solutions? Or are the vendors passing them on? Or are they absorbing their outlays?
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.
Q. 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?
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.
Q. 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?
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.
Will AI Shrink the Contact Center?
Few issues around AI adoption have understandably been as hotly debated as to whether the technology will lead to workforce reductions, including in the contact center. So, we posed two related questions to Vigneshwaran Jagadeesan Pugazhenthi.
1. Is it true that workforce reduction is the prime reason why the C-suite is interested in AI? After all, is it not simpler, achieve results faster, and ultimately more profitable to lay off or not hire staff than to grow revenues, especially in an uncertain economy?
“It’s true that workforce reduction is often a key driver behind C-suite interest in AI, especially in uncertain economic times where controlling costs is critical,” says Vigneshwaran. “Automating routine tasks and increasing call containment through AI can lead to fewer agents being needed, which offers immediate cost savings and improves profitability.
“However, the story doesn’t end there. Smart organizations recognize that AI’s real value lies not just in cutting headcount but in enhancing CX and driving revenue growth through more personalized, efficient interactions.
“So, while workforce optimization is a prime motivator, most forward-thinking leaders see AI as both a cost and revenue lever, not just a tool for downsizing.”
2. So, how much impact do you believe AI adoption will actually have on the size of, and skills needed from the contact center workforce?
Noting the shift of spending from lower/middle-income 90% of consumers to the 10% elite buyers (aka K-shaped economy): who are also often technology first-adopters?
Noting also that the margins for covering overhead costs, like for agents, are less for lower-tiered products and services and conversely greater for higher-tiered ones?
“AI adoption will certainly impact both the size and skills of the contact center workforce, but the exact extent remains to be seen,” says Vigneshwaran. “With the current economic uncertainty and slow growth, companies will be cautious about workforce expansion.
“Additionally, as spending shifts towards the top 10% of elite, tech-savvy consumers, contact centers will need to cater more to their sophisticated expectations.
“For lower-tier products and services, where margins are tighter, AI-driven automation will likely drive greater call containment and reduce the need for large human teams.
“Conversely, higher-tier services, which have better margins and serve demanding customers, will require highly skilled agents who can handle complex and nuanced interactions, supported by AI tools.
“In summary, AI will likely shrink the overall size of the workforce in cost-sensitive segments while increasing demand for advanced skills in premium segments, creating a more specialized and efficient contact center landscape.”
AI Issues
Q. There are reports that too many so-called AI products are fluff, gimmicks, rebrands, to capitalize on the hype. Are you seeing this with the AI products being pitched to contact centers and if so, which types? How should contact centers separate the real from the fake?
Yes, there is definitely hype around AI, and some products in the market feel more like fluff or rebrands trying to capitalize on that hype.
However, within the contact center space specifically, I haven’t seen too many of these gimmicky solutions gaining real traction. Most vendors are focused on delivering genuine AI capabilities that add value, such as improving customer interactions or agent productivity.
To separate real AI from hype, contact centers should look for solutions with proven results, clear use cases, and measurable impact, rather than just marketing claims.
Q. There have been reports of failing AI projects, with accuracy, bias, hallucinations, privacy, and security among the issues.
Is AI truly ready for prime time in the contact center? Given the risks of and brand, loyalty, and potential revenue impacts of poor customer service from AI mishaps, plus the added costs to correct them? Shouldn’t contact centers hold off like a year or two, until the technology matures?
Yes, challenges like bias, hallucinations, and privacy concerns are real, and companies are actively working to address them. But AI is evolving fast: and many contact center vendors are already building guardrails to improve reliability and accountability.
Waiting a year or two might feel safer. But by then, it could be too late. The organizations adopting AI now are learning, iterating, and pulling ahead. The better approach is to start small, implement AI where it adds clear value, and build maturity gradually: rather than sitting out and risking falling behind.
AI, Coaching, and Supervision
The AI conversation in contact centers has mostly, it appears, been around agents. But what are and will be its impacts on coaching and supervision, including qualifications, skills, and how to coach, train, and supervise in an AI environment?
And finally, on how many coaches and supervisors are needed (e.g., agent-to-supervisor ratios)?
We raised these points and questions with Vigneshwaran Jagadeesan Pugazhenthi. Here are his answers:
“AI’s impact extends well beyond agents: it’s transforming coaching and supervision in contact centers too,” says Vigneshwaran.
“With AI-driven analytics, supervisors gain deeper insights into agent performance in real time, such as sentiment analysis, conversation effectiveness, and compliance adherence. This allows for more personalized, timely, and data-backed coaching rather than relying on periodic reviews.
“Training also evolves as AI can identify skill gaps more precisely and recommend tailored learning paths for agents, making upskilling more efficient and targeted.
“Regarding workforce ratios, AI can improve agent efficiency and reduce the need for high supervisor-to-agent ratios. But the exact numbers will depend on the complexity of interactions and organizational goals.”
Recommendations for Contact Centers
Q. What are your recommendations for contact centers that are seeking to buy and use AI-based solutions?
My key recommendation is don’t adopt AI just because it’s trending. Contact centers should first identify the specific business problem they’re trying to solve, whether it’s improving call containment, reducing agent workload, enhancing CX, or boosting operational efficiency.
Once that’s clear, they should focus on implementing AI in that targeted area first. Start small, measure the outcomes, and expand based on results. This approach ensures that AI is aligned with real business impact and avoids unnecessary complexity or wasted investment.
AI isn’t a magic solution. It’s a powerful tool when applied with purpose and clarity.