Key takeaways
- Contact centers are shifting from reactive support hubs to AI-powered growth engines that drive revenue, loyalty and insight
- GenAI and LLMs turn every interaction into a contextual, problem-led opportunity for relevant upsell and cross-sell
- Unified, omnichannel data enables persistent customer profiles and proactive outreach instead of waiting for inbound calls
- Two dominant AI models emerge: fully contained self-service and human-assisted interactions via copilots
- Trust and change management are the hardest parts: Responsible AI, guardrails and incentives are key to adoption
- The future is digital-first, with fewer large human-only centers and more AI-led, revenue-generating experiences
For more than three decades, the contact center has been a central touchpoint between customers and brands. It’s a place where issues were resolved and questions answered. Traditionally, it was a world of hold music, scripted responses and siloed data systems.
But according to Rohit Ahlawat, Senior Vice President – CCaaS Sales at HCLTech, that world is rapidly transforming. “If you look at a traditional contact center,” he explains, “there was always a phone you could pick up and call to get an answer. You knew the limitations of that person — there were only specific questions you could ask and get an answer around.”
That basic, human-driven interaction gradually evolved into systems powered by speech-to-text and text-to-speech technologies, capturing customer history and improving continuity. But now, says Ahlawat, the industry is experiencing a “paradigm shift” that is defined by the rise of Generative AI (GenAI) and Large Language Models (LLMs).
“AI [is] becoming core to any contact center being developed right now. It is also a very natural, seamless adoption. Companies were already writing voice bots and chat bots — only the underlying platform has changed,” he says.
This evolution from Natural Language Understanding (NLU) systems to LLM-based architectures has redefined what contact centers can do. “Now you’re able to do a lot more with your contact center,” he adds. “That’s why this field, at least for us, is very exciting.”
Turning conversations into revenue opportunities
If the contact center was once a cost center, AI is now enabling it to become a growth engine by unlocking new revenue opportunities from everyday customer interactions. But, as Ahlawat cautions, the challenge lies in doing this without eroding trust.
“With AI infusion into the contact center, you know a lot more about your customers than you did a few years back,” he says. “It’s hard for an agent who’s solving your problem to suddenly say, ‘ I have this promotion — would you like to buy something?’ Most of us would hate that.”
Instead, Ahlawat advocates for contextual, problem-led upselling, where AI uses data insights to offer timely, relevant solutions. “For example, imagine a company selling perishables,” he explains. “You know their usage and that they’re running low. You might be solving another problem for them, but you can say, ‘I see you’re down to 10%. Would you like to reorder? We’re offering a 10% discount.’”
The key, he says, is that “you’re solving a problem for the customer without sounding like you’re trying to sell them something.” The results can be significant. “We’ve seen companies generate hundreds of millions in revenue just by following simple steps of problem-solving instead of overt selling.”
Building a unified, omnichannel experience
Delivering seamless, omnichannel customer experiences, where customers never need to repeat themselves, requires one foundational ingredient: unified data.
“The world of contact centers was always reactive,” Ahlawat notes. “You expected the customer to get in touch with you. That has been changing into what we call a proactive and reactive world.”
With AI and data integration, contact centers can now anticipate problems before they occur. “Proactive means, with all the data available, you can reach out to customers and say, ‘This is something we see happening — do you want to try something different?’”
Social data further strengthens that capability. “You know what social handles your customer uses, what FAQs they visit, what blogs they’re reading,” he explains. “By combining all this data, you build a persistent profile and experience across every interaction.”
This is what Ahlawat calls “bringing AI to the front of your business interaction.” Whether a customer speaks to an automated system or a human agent assisted by AI, the full context of their journey is instantly available. It ensures that the agent has the right information at the right time. “That’s one of the best AI use cases we’ve seen and it’s driving huge improvements in Net Promoter Scores because customers know their problems are actively being solved.”
Managing change in the age of AI-first contact centers
It’s well known that technology isn’t the hardest part of transformation. People are. Ahlawat emphasizes that change management and trust in AI are the biggest challenges in scaling AI-first contact centers.
“The same chat bots and voice bots have existed for years. What’s changing is the underlying capability of the platform. It’s now highly data-intensive and AI-driven, able to understand nuances and resolve issues,” he says.
In AI-enhanced contact centers, two interaction models dominate:
- Contained interactions, where an AI system resolves an issue without human intervention
- Human-assisted interactions, where AI tools like copilots support live agents with real-time recommendations
The biggest challenge, says Ahlawat, is that humans do not trust the information that is being surfaced.
Organizations are addressing this through Responsible AI practices, incremental rollout and incentivized adoption. “It’s about taking smaller steps,” he says. “You incentivize the right adoption and show how customer satisfaction improves.” Once teams see reliable results and guardrails in place, confidence builds naturally.
At HCLTech, he adds: “We see the contact center as the spearhead of GenAI adoption in many organizations. Once you solve this problem here, it becomes easier to adopt AI to across other [business] processes.”
Proving the value: Where to start
When it comes to demonstrating ROI, Ahlawat suggests targeting edge cases first; interactions that fall outside standard scripts and typically escalate to human agents.
He illustrates this with a telecom example: “You’re talking about paying a bill and suddenly say, ‘By the way, the network near my house is dropping a lot.’ That’s an edge case: you’re in the finance workflow but asking a technical question. Traditionally, you’d be transferred to another agent.”
With GenAI-powered systems, that’s no longer necessary. “Now the contact center can understand that this is an edge case, pull the relevant information and contain the interaction within the same conversation,” he explains.
This approach drives measurable cost and efficiency gains. “Every human call is a high-cost call,” Ahlawat points out. The more edge cases organizations can resolve without escalation, the more value your AI contact center delivers.
Looking ahead to 2026: Maturity, design and growth
Asked for his predictions for 2026, Ahlawat is clear: the next phase is about execution maturity and digital-first design.
“The next couple of years will be about bringing maturity in execution,” he says. “Everyone is experimenting with GenAI-based contact centers, [including] Microsoft, Amazon [and] Google.” Crucially, it’s how organizations start using them that will define success.
He also anticipates a structural shift in the global contact center industry. Large, human-staffed contact centers, “especially in Asia, will start to come down quite a bit,” he predicts. This is because, increasingly, calls will be contained and resolved within AI-driven systems.
And perhaps most importantly, the contact center’s strategic identity will fundamentally change. “At the end of the day,” Ahlawat says, “the contact center, which has always been a cost center, will start to be a revenue generating center. When you’re resolving a customer’s pain and can bundle additional services that genuinely help them, you create value on both sides.”
This, he concludes, is made possible only through “the evolution of AI and the whole data fabric coming together.”
Conclusion: The future of intelligent engagement
The transformation of contact centers mirrors the larger enterprise AI journey — one that blends technology, trust and transformation at scale.
In Alhawat’s view, the future belongs to organizations that elevate the contact center from a reactive support function into a proactive engine for growth, loyalty and innovation. “Once you solve this problem,” he says, “it opens the door to AI adoption everywhere else.”
By reimagining how humans and machines collaborate, businesses are discovering that every customer conversation holds not just a cost — but a potential opportunity for connection, insight and growth.
FAQs
1. How is AI turning contact centers into growth engines rather than cost centers?
AI and large language models are turning reactive, phone-based contact centers into proactive, digital-first growth engines. Instead of just resolving tickets, they surface next-best-actions, enable contextual upsell and cross-sell and help agents personalize every interaction without feeling pushy or eroding customer trust.
2. What role does Generative AI play in everyday customer interactions?
Generative AI helps contact centers understand intent, pull knowledge from multiple systems and recommend next steps in real time. It powers both fully contained self-service interactions and human-assisted copilots, enabling faster resolution, fewer escalations and better experiences while still keeping humans in the loop for complex scenarios.
3. Where should organizations start when proving ROI from AI in contact centers?
Start by focusing AI on edge cases that normally require escalation, such as off-script questions during a billing or support call. When AI can understand context, fetch technical or account data and resolve these within a single interaction, you reduce expensive handoffs and quickly prove tangible ROI.
4. How can companies build trust in AI among agents and leaders?
Trust comes from clear guardrails, Responsible AI practices and gradual rollout. Organizations start with low-risk use cases, monitor results and show agents how AI raises customer satisfaction and first-contact resolution. Incentives, transparent performance metrics and using AI as a copilot, not a critic, help frontline teams adopt it confidently.
5. Why is unified data so important for modern, omnichannel contact centers?
By unifying data from CRM, billing, web, social and past interactions, AI builds persistent customer profiles that follow the customer across channels. This context lets systems and agents anticipate needs, personalize offers, proactively prevent issues and design journeys that feel consistent whether customers call, chat, message or self-serve.
