Many businesses are still working through the basics of platform integration, data consistency and cross-functional execution. That makes the rise of Agentic AI especially significant. It does not replace the need for connected systems, but it changes what those systems need to do.

Speaking at HCLTech’s booth at Adobe Summit, Virender Singh, Vice President, Digital Business Services at HCLTech, explained how Agentic AI is reshaping the experience value chain, from platform design and personalization through to operating models and team structures.

From connected platforms to interoperable platforms

For years, organizations have focused on connecting platforms across marketing, commerce, sales and service. According to Singh, this work is still necessary, but the market is now moving beyond simple integration.

“I think connected platforms still make a lot of sense today, because not every customer has actually nailed down the art of connected platforms,” he said.

“There are customers who are still building their platforms. They're still integrating the platform, but with the AI intervention, everybody is moving into more interoperable platforms.”

That distinction matters. “Interoperable platforms means where the context, the intent, the user, data and the journey can be seamlessly moved or managed or exchanged across different capabilities, whether it's marketing, Ecommerce, sales and services,” said Singh.

In other words, the next phase of experience transformation is not just about making systems talk to each other. It is about making them capable of sharing context and acting on it.

What Agentic AI adds beyond automation

This is where Agentic AI starts to differ from earlier waves of automation and orchestration. Traditional automation has often been built around rules, workflows and approval processes. Agentic AI adds a more adaptive layer.

“Automation and orchestration were there even previously, which was more driven by workflows and approval processes,” said Singh.

“Agentic processes bring context and intern context and intern driven capabilities, where agents can run autonomously, they can reason and they can connect different applications and systems and data to deliver end-to-end workflows, to deliver end to end journey orchestration.”

That ability to reason across systems and journeys is what makes Agentic AI more than just another layer of process automation. It pushes experience platforms toward more autonomous, end-to-end decisioning.

What the leaders are doing differently

HCLTech’s Blueprint of AI Leadership research found that organizations delivering AI ROI are 50% more likely to deliver stronger customer experiences. Singh pointed to three common traits among those outperformers.

“One is they are unifying data. They're ensuring that data still is treated at the core of experiences,” he said. “Then, of course, they are operationalizing AI. They're not just doing POC or talking about AI, but they're making it part of the platforms and the processes.”

The third factor is organizational. “AI is changing how the operating models were built,” said Singh. “We used to talk about product line operating model with functional cross functional teams, but now we are talking about outcome driven teams.”

That shift from siloed capability ownership to outcome-led execution may prove just as important as the technology itself.

The push toward real-time, context-driven experiences

Singh was also clear that real-time personalization is no longer a future ambition. The underlying technology already exists, but AI is making it more intelligent and more dynamic.

“With AI now, it's becoming much more intelligent, because AI models can bring reasoning into it. They can bring context into it. They have a memory to contain your user journeys end to end.”

That, he argued, is enabling “real time, context driven and micro, moment-based journeys, or personalization, which was lacking previously.”

Where value is showing up now

In practice, Singh sees value emerging across marketing, commerce and services, although maturity still varies.

“We are seeing content supply use cases. We are seeing campaign execution use cases. We are seeing launch use cases,” he said of marketing. In commerce, “we are seeing product categorization, product pricing, promotion,” while in services, “we are seeing price management, proactive ticket resolution, proactive customer management.”

Building for scale

Scaling Agentic AI comes down to three changes: outcome-driven operating models, interoperable platforms and an AI-ready workforce.

“From a team standpoint, we are going toward outcome-driven models,” he said. “From a platform standpoint, interoperable platforms are going to be the key that can break silos between sales, service, marketing and commerce. And from a people standpoint, an AI-ready workforce is going to make a lot of difference.”

That combination is what will determine whether Agentic AI remains a promising capability or becomes the foundation of the next experience value chain.