At Adobe Summit, much of the conversation centered on how AI is changing the way brands engage customers across commerce, marketing and service. For Taranjeet Khanuja, Associate Vice President, Digital Business Services at HCLTech, the shift is not simply about adding more automation. It is about responding to a customer environment that has become more complex, less linear and far harder to manage through traditional rules-based approaches.

Speaking at HCLTech’s booth, Khanuja described this as a moment when businesses need to rethink how they connect journeys, content and systems. “The supply and content chains have been going through a lot of changes,” he said.

“These funnels have become much more complicated [and the overall system is becoming harder to manage through traditional approaches].”

Why the traditional funnel no longer works

That idea of rising complexity is central to how Khanuja sees the modern commerce environment. Customers no longer move through neat, predictable stages from awareness to conversion. They gather information from multiple sources, switch between channels, make purchase decisions in different contexts and continue interacting with brands long after the transaction itself is complete.

“While we always talk about funnel, customer journeys have never been that straight,” he said. “There’s a continuous cycle of the touch points, even post purchase.”

For brands, that means channel-led thinking is no longer enough. What matters more is the ability to bring those interactions together into a coherent understanding of the customer and respond with the right message at the right time.

Khanuja gave a simple example. If a customer has already bought a product in store, should the brand still be serving them the same targeted product message through another channel? Or should it move on and adjust the journey accordingly? That is where connected experience starts to become contextual experience.

What must change before Agentic AI can scale

HCLTech’s Blueprint for AI Leadership research found that only 11% of organizations have the infrastructure ready to support AI, and only 16% are confident in rolling out AI agents that can perform tasks. Khanuja said retail and consumer goods organizations are facing very similar constraints.

“Everyone is interested in proving value,” he said. But moving from experimentation to scale depends on three things.

1. Data: Customer information still sits in too many disconnected places, from loyalty systems and POS environments to ecommerce platforms. “How is it that we are bringing all of those pieces together for a harmonized view of the customer and then taking it forward?” he asked.
2. Architectural readiness: Once that data is brought together, brands need real-time visibility across the different processes and touchpoints that shape the journey.
3. The operating model: Khanuja argued that success with Agentic AI depends on whether teams are aligned around shared business outcomes rather than isolated functions. “Your KPIs that each team is being measured on need to be aligned with what we as a group are going to be working toward,” he said.

From rules-based journeys to contextual journeys

For Khanuja, one of the biggest opportunities in Agentic AI is the move away from static business rules and toward more adaptive, contextual decisioning.

This is a pivotal moment where business rule-based journeys can start to evolve into far more contextual journeys.

The limitation of older approaches is that they tend to work within the boundaries of a single channel or campaign logic. An ecommerce campaign may not know that the customer has already made a purchase in store or that the same customer has recently expressed dissatisfaction with the product. The result is poor timing, irrelevant messaging and a weaker overall experience.

By bringing those data points together, brands can become more responsive and more precise. In Khanuja’s view, that is where experience will continue to evolve in a more connected commerce world.

Why value matters more than automation alone

Agentic AI should not be measured simply by how much it automates. Brands need to stay focused on value.

“We are bringing value instead of automation only,” he said. That starts with identifying the moments where automation can genuinely improve the experience or business outcome. It also means recognizing that not every interaction should be handed over to autonomous systems.

“There have to be certain touch points where human in the loop, as well as human intimacy, is needed,” he said.

The final piece is measurement. “The KPIs we use to measure operations need to be redefined so that automation does not come at the expense of losing touch with the customer.”

Building connected commerce at scale

In closing, Khanuja said that platforms such as Adobe can help organizations move toward a more unified experience environment, but technology on its own is not enough. To operationalize Agentic AI at scale, brands need a stronger data foundation, better AI use-case selection, a clearer Total Experience view and an operating model built for agent-based ways of working.

In practical terms, that means connecting customer expectations to the delivery mechanisms that shape end-to-end journeys, while making sure the business is set up to generate value from those journeys continuously.

For retail and consumer brands, that is the real promise of Agentic AI. It is not simply a new automation layer. It is a way of rebuilding connected commerce around context, continuity and more intelligent decisioning.