Rethinking the Customer Journey in the Agentic Era

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Rethinking the Customer Journey in the Agentic Era

A significant amount of recent business investments are focused on managing increasingly complex customer journeys.

Organisations are well versed in identifying whether customers are pre-, during-, or post- purchase, and tailoring interactions with them accordingly. Yet these journeys became significantly more complex as customers gained new channels to engage with companies; websites, mobile apps, user-generated content, social media, VR/AR, influencers, live streaming, and the list goes on. As these channels proliferated it became significantly harder to identify how customers were interacting with the brand, where in their ‘journey’ they were, and how best to respond.

Organisations turned to technology to help, spurring the growth of the now >$700billion global industry of ‘marketing technology’ which today boasts over 15,000 different solutions.

A significant proportion of this industry is focused on managing, and even ‘orchestrating’, customer journeys in real-time; gathering real time data about customer behaviour across channels, creating automated rules for how to respond, and attempting to personalise the experience throughout. Pre-AI, this often relied on marketers using rules-based logic to map out dozens, or even hundreds, of automated workflows and pre-determining the appropriate way to respond to each predicted behavioural pattern.

Agentic AI is changing this equation for organisations. An AI agent with the right company and customer context can, in theory, determine the right response (or ‘next best action’) for every customer in real-time, removing a significant amount of effort for organisations while better personalising the experience for consumers. But one of the unique aspects of this emerging technology is that it is not only accessible to brands; it is increasingly in the hands of customers too. AI agents can now carry out much of the journey on a customer's behalf, researching products, comparing options, and even completing the purchase from a single instruction. The projected scale of this Agentic Commerce is significant: McKinsey estimates AI Agents will mediate US$3–5 trillion of transactions by 2030.

The changing customer journey

While the scale of Agentic AI commerce alone warrants attention, there is a bigger implication for how businesses understand customers. Even while customer journeys were getting more complex and crossing multiple channels, they were still largely completed by humans. Brands could focus on customer research and human psychology to understand customers and position themselves for success. Now, significant portions of the customer-business interaction might be mediated, or even autonomously completed, by AI agents.

As a result, foundational business concepts like brand perception, emotional resonance and consumer attitudes are no longer sufficient on their own. Businesses must now consider how the customer’s AI agents represents their brand; whether the brand is legible, retrievable and trusted by the machine increasingly doing the choosing. Essentially, brands now have two audiences to consider: the person and their human preferences, and the agent acting on that person's behalf.

This is already a reality across south-east Asia where ‘super-apps’ combine discovery, payment and delivery in one place, meaning customers’ AI agents can complete an entire journey without leaving the ecosystem. As example, Alibaba recently integrated its Qwen assistant directly with Taobao’s catalogue of over 4 billion products, making Agentic shopping a direct feature for shoppers.

Questions for future-ready businesses

Even as businesses and consumers adopt Agentic AI assistants, many aspects of how these tools work are still ‘black boxes’. So, while businesses have decades of research and experience dealing with human customers, it’s still relatively unknown how AI agents decide on which products to rank, recommend or choose for their human users.

So instead of searching for immediate specific answers, which may change as AI models evolve, businesses should consider a few major questions:

How to be visible when AI does the choosing? How does a business rank highly for both a human shopper and their AI agent? Do businesses need to position themselves differently for each? If so, how can this managed while maintaining brand consistency?
Which human moments now matter most? As customers increasingly delegate to AI agents, which parts of the journey are most relevant to the humans? How can brands maintain that human connection when it is most relevant?
Where is trust when relationships are mediated by machines? While transactions may be handled by technology, relationships still depend on trust. How can brands in high impact, or business-to-business sectors create and manage that trust when AI is mediating the relationships on both sides?

Technology impacting how businesses and customers interact is new. Yet the big change from Agentic AI is that the most capable technology is no longer only in the brand's hands. Businesses who learn to not only use AI for their own purposes, but also adapt for how customers use it too, will be the most future-ready and appealing to the new wave of AI-powered customers. The challenge is knowing how to do so.

About the Author

Dr Jason Pallant is a Senior Lecturer at RMIT University, researching the impact of emerging technologies on customer experience. He has conducted research studies and consulting work for major national retailers, shopping centre operators and industry associations.

Jason is a judge for retail industry awards hosted by Inside Retail and the National Retail Association, and was named on ReThink Retail’s global Top Retail Experts list for 2023 and 2024 – the only Australian academic to be included.

06 August 2026

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06 August 2026

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