The risk of the accelerating customer

As AI becomes a multipurpose technology that can enhance nearly any activity in our lives as humans, it is inevitably starting to play an increasingly larger role in the journeys we take as consumers and customers. This includes everything from planning a holiday to researching our next big purchase. This rapid and profound shift is a growing challenge for the marketer.

For decades, the onrush of digital technology has put marketing and IT teams under pressure to adapt to new channels and touchpoints to reach customers as they appeared – from websites to social media to apps – so this is nothing new.

However, AI is shifting consumer behavior to a new speed. It’s no longer just about the channels they use, but about creation itself, with the boundaries between “professional” and amateur content already becoming almost indistinguishable.

This is leading to marketing teams feeling outpaced by the people they’re meant to understand. They feel the urgency to match their speed of movement and raise the bar of content through the very same technology.

While marketing wrestles with optimizing for the current chaos of journeys that span channels, campaigns, and funnels, customers are moving in a different direction, turning to AI agents and large language model (LLM) platforms like ChatGPT and Google Gemini.

These tools go beyond traditional search engines to help them get results based not just on keywords but on intent.  

Showing up in these LLM searches is critical; equally important is ensuring your brand stands out in a market filled with content “slop” – low-quality AI-generated spam. How do you ensure you’re positioned to meet your customers’ expectations while maintaining authenticity and brand voice?

Reevaluating a knee-jerk reaction to AI disruption

Many brands hastily dove into the AI tool pool with expectations of fast outcomes, with no intention of carving out an AI-native marketing trajectory. What they got instead was integration complexity and unclear value paths.

As organizations strive to keep pace with AI capabilities that seem to get better by the day, they find themselves confronted with a paradox. The proliferation of tools and features that were supposed to improve marketing operations has instead increased its complexity.

Rather than simplifying workflows, the stacking of new AI tools often results in a more encumbered ecosystem that demands greater integration and oversight from marketing teams. As Katherine-Margaux Longest from beauty brand Naturium puts it: “Investing in AI and tools to drive efficiency is important, but it’s not the definitive solution. Real progress comes from clarity of purpose and a disciplined focus on how to achieve it.”1

A goal many organizations establish is automation – long hailed as one of AI’s greatest strengths, offering the potential to reduce manual effort and boost productivity. But we must be careful in deciding where AI eliminates manual effort altogether. It must not come at the expense of forgoing human creativity and ingenuity when it makes sense as an input.

Disciplined focus is all about lockstep coordination between departments. For instance, organizations must figure out how their AI integrations can satisfy both the technical requirements demanded of IT and the operational objectives sought by marketing.

Underpinning all of this is the new operating model, which may require marketers to adapt their roles, skill sets, and mindsets. Just as AI can transform marketing for the better, it can also become a source of major disruption to old ways of working. Imagine installing a state-of-the-art engine into a car that wasn’t built to withstand the extra power and speed. It can easily overwhelm the existing structure. A recent Capgemini Research Institute survey revealed that only 15% of marketing leaders think their current operating model enables high-value work.

Today’s CMO has a difficult job: 65% believe that AI will dramatically change their role within the next two years.[1] Yet, with fewer resources (marketing spend remains stuck at 5–6% of revenue),[2] they’re expected to innovate with AI and manage an increasingly complex marketing stack.

While there is strong enthusiasm for AI adoption (AI in marketing is on pace to surpass $107B by 2028, up from nearly $47B in 2025),[3] the mentality of many remains focused on incremental AI-enabled solutions, rather than deeper transformation.

So, what does AI-native really look like in practice?

Check out our follow-up article, “Activating AI-native marketing,” where we describe the transformation and how to get you moving.

Heading to #AdobeSummit? Contact us – we would love to show you a demo of AI-native marketing in action.

[1] https://www.gartner.com/en/newsroom/press-releases/2026-02-23-gartner-survey-reveals-cmo-ai-blind-spot-as-65-percent-expect-role-disruption-yet-only-32-percent-say-significant-skill-changes-are-needed

[2] https://www.capgemini.com/wp-content/uploads/2025/11/Final-Web-Version-Report-CMO-Playbook.pdf

[3] https://www.statista.com/topics/5017/ai-use-in-marketing/#topicOverview