Agentic AI and Brand Transformation

New Value for New Growth

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The rules of growth have radically changed. Post-digital brands are no longer built on a linear funnel of communication, purchase and use, but on a continuous and real time cycle of customer experience and expectation.

These brands have true agency. Meaningful customer experiences are underpinned by intelligent, human-centred services, moving on from yesterday’s relevance and personalisation to deliver tomorrow’s Recognition, Recall and Response.

Many of the building blocks required to deliver this brand intelligence already exist. The missing piece is Agentic AI, which transforms a marketing ecosystem that just communicates value, into one that creates and delivers it.

Engagement and loyalty are revolutionised. Brand equity is revitalised. Customer and enterprise value come together to enable growth.

How We Got Here

Inventing The Invisible Brand

25 years ago, working with my colleague and friend David Stoughton, I wrote a book that accurately predicted, among other developments, the arrival of automated digital services and their profound transformation of customer value, brand loyalty, and enterprise growth.

Promiscuous Customers: Invisible Brands was the first in-depth strategic analysis of digital customer experience (DCX). One of its foundational claims was that, as digital advertising proliferated while customer service expectations increased, value - and thus growth - would come to be driven far less by the much-revered ‘relevance’ of brand communications, and far more by the intelligence of data-enabled brand services.

The concept of ‘the invisible brand’ was, at the time of publication and for some time thereafter, not far from a kind of heresy. After all, isn’t the main job of marketing to attract, engage and retain the attention of customers?

We argued then - and I continue to hold this line a quarter century later - that behind every satisfying customer experience lies a meticulously crafted service. With some sector exceptions, the success of such services is founded on reducing - and where possible and appropriate, removing - both work and risk for the customer.

This led to a radical, counter-intuitive and disruptive, yet entirely logical, conclusion.

Brands that apply digital technologies and data to create intelligent services that recognise and respond to the user’s current ‘mode, purpose and tasks’, are able to command unprecedented levels of loyalty, equity and price resilience.

The arrival of Agentic AI

Such predictions take a longer time to land than one would hope, but when their time comes, they tend to arrive hard and fast. Much of marketing’s AI discourse has, disappointingly to date, revolved around increasing efficiencies in the creation, distribution and measurement of brand communications.

The emergence of Agentic AI, however, brings to compelling and achievable clarity the original promise of the invisible brand. Generically, Agentic AI can act independently to achieve goals, make decisions, and interact with their environment in a more autonomous way than general purpose AI.

Goal-directed behaviour

Unlike reactive systems, agentic AI can formulate and pursue longer-term objectives, adjusting strategies based on changing circumstances.

Environmental interaction

These systems can engage with their environment more comprehensively, understanding context and making relevant decisions rather than simply following fixed patterns.

Flexible decision-making

Agentic AI can choose between multiple approaches to achieve goals, weighing options and adapting to new situations.

Reduced human oversight

While still operating within defined parameters, these systems require less direct human guidance for many tasks.

Real brand intelligence

Enabling customers to pay less attention to messaging, while at the same time trusting the brand to increasingly take on and execute tasks with minimal time and effort - and, over time, ‘invisibly’ in the background on their behalf - opens the door wide to powerful and competitive value innovations, and thus enterprise growth.

To bring the agentic opportunity to life, it’s helpful to look at some use case snapshots.

Contextual intelligence systems

Synthesising and responding in real time to:

• Current location, time and environmental factors;
• Calendar events and commitments;
• Recent activities and interactions;
• Emotional and physical state indicators;
• Task progress and blockers.

Purpose-aligned support

Understanding and supporting higher-level goals:

• Connecting immediate tasks to longer-term objectives;
• Balancing competing priorities;
• Identifying opportunity costs and tradeoffs;
• Suggesting alternative approaches.

Relationship memory networks

Building a rich understanding of:

• Personal preferences and patterns;
• Important relationships and dynamics;
• Past experiences and outcomes;
• Learning and adaptation patterns.

Proactive task orchestration

Anticipating and coordinating needs:

• Breaking complex goals into manageable steps;
• Identifying dependencies and constraints;
• Orchestrating services and resources;
• Adapting plans as circumstances change.

Ambient intelligence integration

Creating seamless support through:

• IoT device coordination;
• Environmental adaptation;
• Service provider integration;
• Privacy-preserving data sharing.