Key takeaways

• AI-powered contact centers are shifting from handling simple queries to enabling rich, value-added customer transactions and upsell opportunities
• Within 12 months, leaders should expect their contact centers to contribute measurable growth, not just lower handle times and costs
• Data-driven prioritization of use cases allows organizations to prove ROI quickly, then scale into deeper automation and self-service
• AI-derived insights from millions of interactions can guide product, CX and operational decisions across the enterprise
• Strong, collaborative governance between enterprise and technology partners is essential to ensure safe, secure and reliable AI at scale

Contact centers have long been treated as a necessary cost of doing business. They are the channels that must exist to handle complaints, queries and issues as efficiently as possible. Traditional interactive voice response (IVR) and basic chatbots were built to deflect calls and contain volume, not to delight customers or unlock new revenue.

Generative AI has changed that. With large language models now capable of understanding intent, maintaining context and executing complex workflows, enterprises can reimagine the contact center as a strategic asset.

As Anthony Mancini, Vice President, Professional Services at HCLTech, explains, the “scope of what can be done is dramatically improved,” whether customers are calling, chatting or engaging through other digital channels.

Drawing on insights from a recent HCLTech Trends & Insights podcast with Mancini, the following practical guide outlines how leaders can harness AI-powered contact centers to drive measurable enterprise value, from redefining success metrics to putting robust governance around new AI-enabled experiences.

What AI-powered contact centers must deliver in 12 months

Executives are increasingly clear: service operations must contribute to growth, not just efficiency. For Mancini, the emerging role of AI in the contact center fundamentally changes what leaders should expect from these channels.

He notes that enterprises are “moving away from things like voice response applications and chat systems – somewhat of a necessary evil –” that handle only simple tasks. With generative AI, organizations can now support “value added transactions” that go far beyond basic FAQs or status checks.

Over the next 12 months, AI-powered contact centers should be accountable for:

• Revenue and upsell: Mancini expects enterprises “to be looking…to these end customer touch points as ways to upsell, truly upsell, their products and their services”
• Deeper resolution in-channel: AI agents can handle more complex journeys end-to-end, reducing transfers and repeat contacts
• Improved customer satisfaction: Richer, more conversational interactions create a much more “pleasant…customer experience” and strengthen loyalty
• Operational productivity: Automation, smarter routing and AI-assist tools increase agent efficiency and reduce handle time without compromising quality

In short, the contact center is evolving from a defensive function to a proactive, AI-powered growth platform.

Designing for value: New operating models and incentives

If AI is transforming what contact centers can achieve, it must also transform how they operate. From conservative adopters who “let the technology stabilize” to those who want to “make full use of the innovation that’s out there,” organizations span a spectrum in their approach to generative AI.

Regardless of where they sit on that spectrum, leaders now need a deliberate strategy for combining humans and AI:

• Human in the loop by design: Enterprises must decide “where they’d like to have human in the loop, where in the workflow, where in the business process, how it gets involved”
• End-to-end orchestration: Success depends on “how that’s managed seamlessly across technology any sort of human involvement,” ensuring customers move fluidly between automated and human support
• Rethinking metrics and rewards: As AI deflects routine contacts and surfaces richer opportunities, agents and leaders should be rewarded for value creation, such as conversion, retention and NPS, not just handle time or volume.

It's important to have “a holistic end to end strategy” that covers automation, self-service and human escalation, rather than chasing isolated tools. This is where partners like HCLTech help organizations “think about what the technology can do and how it might fit into that plan” in a way that’s specific to their business model and workforce.

Proving value fast: Data-driven use cases to prioritize first

When it comes to unlocking enterprise value, leaders often ask whether they should pursue small, compounding wins or bold “moonshot” projects. For Mancini, the answer starts with the data.

“The beauty of where we’re at today,” he explains, “is we can let the data tell us where to go focus.” By analyzing agent and chat transcripts across thousands of agents and millions of interactions, organizations can identify:

• The most frequent intents and pain points
• High-effort journeys that drive dissatisfaction
• Repetitive tasks ripe for automation
• Moments of truth where a better experience or offer has outsized impact

Through a phased, data-driven approach, this insight allows enterprises to pinpoint “where is the opportunity to really make a difference in either customer satisfaction, in productivity…whatever the metric is that’s important to the organization.”

1. Start with rich informational self-service: Begin with “very basic but very rich and powerful informational type self-service systems.” These foundational experiences build customer and internal confidence in AI.
2. Stabilize and learn: “Let that stabilize, get comfortable with the technology and the approach. Have the end customer get comfortable with the approach.” Use this phase to refine journeys and address any issues.
3. Layer in integrated self-service: Then add “very meaningful self-service and highly integrated solutions” that connect into core systems and “really start to move the needle around whatever the key metric is.”

In other words, use data to choose high ROI use cases, prove value quickly, then systematically expand into deeper, more integrated automation.

From insight to action: Turning AI-derived signals into enterprise decisions

Contact centers are one of the richest sources of real-time customer insight in any organization. With Generative AI, enterprises can finally tap into that data at scale.

The technology is incredibly powerful and leveraged in the right way, across both self-service and agent-assisted experiences, AI can:

• Summarize calls and chats automatically
• Detect sentiment and friction points
• Reveal emerging issues and unmet needs
• Recommend next-best actions to agents in real time

This “shifts the paradigm” from forcing customers down rigid, scripted paths to enabling “something that’s much more holistic” and conversational. When “context switching is immediate” and “that context is saved,” organizations gain a continuous, structured view of customer journeys.

Critically, when “harnessed correctly and done in a responsible way…it can be done very, very quickly” and “provide impact in ways that we’ve never seen before,” says Mancini. The insights don’t only improve service; they can inform:

• Product and feature roadmaps
• Pricing and offer strategies
• Channel and journey design
• Training and knowledge management

By closing the loop between contact center data and enterprise decision-making, AI turns every interaction into a learning opportunity.

Making AI trustworthy: Governance for safe, scalable experiences

As AI becomes more central to customer interactions, trust, safety and compliance become critical differentiators. "Companies everywhere have developed these Responsible AI practices,” and providers like HCLTech have done the same.

However, governance cannot live in silos. “The beauty,” he says, “is in marrying those two things together;” the enterprise’s risk and responsibility framework on one side, and the provider’s secure AI practices on the other.

Effective governance of AI-enabled contact centers should include:

• Joint responsibility models: “Not necessarily work in isolation, but work hand in hand,” with clear roles for business, IT, risk and the technology provider
• Alignment on priorities and constraints: The enterprise “representing what their priorities are, what’s important to them and how they want to address this responsibility and the secure AI construct”
• End-to-end oversight: Governance that spans “from…business priority to solution, and how those meet in the middle,” rather than just reviewing individual models or tools
• Controls for safety and consistency: In a world of hallucinations and probabilistic models, enterprises must ensure “consistency of message” and reliability across regions and segments

Governance must be a collaborative, continuous process between the enterprise and its AI partners to ensure safe, scalable experiences.

The year ahead for AI-powered contact centers

Looking toward 2026, Mancini expects three major trends to shape AI-powered contact centers.

First, enterprises will move quickly from experimentation to scaled adoption as they gain confidence in the safety and reliability of AI. Concerns about hallucinations and security are “becoming something that is manageable” and “something we can control,” clearing the way for broader deployment.

Second, customers will increasingly prefer AI-powered automation for many tasks. With “absolutely enriching dialog” across channels and “real…customer experience” that preserves context, automation will feel less like a constraint and more like the fastest path to value.

Third, there will be a mind shift away from viewing IVRs, chat systems and email as cost-containment mechanisms. Instead, organizations will “start to see value out of these channels and true high level, rich self-service with a very pleasant experience.”

For leaders, the opportunity is clear: by combining robust governance, a data-driven roadmap and a human-in-the-loop operating model, AI-powered contact centers can become one of the most powerful levers for unlocking enterprise value in the years ahead.

FAQs

1. What is an AI-powered contact center?
An AI-powered contact center uses technologies like Generative AI and large language models to understand customer intent, maintain context across interactions and automate tasks. Instead of relying solely on scripts and basic IVRs, it can handle complex queries, support rich self-service, assist agents in real time and surface insights that improve products, services and customer journeys.

2. How quickly can organizations see ROI from AI in the contact center?
ROI timelines vary, but impact can be generated quickly when organizations start with data-driven use cases. By analyzing existing transcripts to identify high-volume, high-friction journeys, enterprises can launch targeted informational self-service and agent-assist tools that improve satisfaction and productivity within months, then scale into deeper, integrated automation.

3. How does AI change the role of agents and contact center leaders?
AI reduces repetitive work and surfaces richer customer opportunities, meaning agents spend more time on higher-value interactions. Leaders should rethink metrics and incentives to focus on value creation, such as upsell, retention and customer satisfaction, rather than just handle time. Human-in-the-loop design remains crucial, with agents handling complex cases and building relationships supported by AI.

4. What governance do we need before rolling out AI across regions?
Organizations should align their internal Responsible AI policies with those of their technology partners, creating a joint governance model. This includes clear roles for business, IT, risk and providers; standards for data security and privacy; controls to manage hallucinations and messaging consistency; and ongoing monitoring across regions and segments to ensure AI behaves safely and reliably at scale.

5. Where should we start if we’re new to AI in the contact center?
Begin with your data. Use call and chat transcripts to identify common intents and pain points, then prioritize a small set of high-impact, low-risk use cases; often informational self-service and AI-assist for agents. Launch, measure and stabilize these experiences, build comfort among customers and staff, and then progressively expand into more integrated, end-to-end automated journeys.