AI gets personal, but can it get it right?

In a marketplace where customer expectations are sharply rising, the demand for hyper-personalised experiences is at its peak. Nearly 80% of consumers express a preference for more tailored interactions with brands, according to an Adobe survey. Delivering the right message to the right person at the right time is no longer a marketing ideal—it’s a baseline requirement.

Generative artificial intelligence (AI) is increasingly positioned as a transformative tool, enabling businesses to create contextually relevant and individualised interactions. From streamlining marketing workflows to enhancing internal productivity, generative AI is being promoted as a catalyst for rethinking customer engagement. Yet, the real-world integration of such technology remains a complex task, especially amid today’s fragmented digital ecosystem.

Recent developments in AI agents and conversational interfaces are pitched as the solution—tools that promise to empower teams regardless of technical expertise. These systems aim to orchestrate, automate, and personalise marketing and customer experience efforts at scale. In this narrative, AI agents become the mechanism through which generative AI’s potential can be unlocked.

Navigating personalisation at scale

Scaling personalisation is not straightforward. Fragmented data, legacy workflows, and the need for real-time responsiveness continue to obstruct progress. A unified data foundation is often cited as essential to solving these challenges. When in place, it allows businesses to develop actionable insights and personalise customer interactions more effectively.

Digital Experience Platforms (DXPs) have emerged as one route to achieve this. Designed to unify customer data and manage cross-channel experiences, DXPs help build coherent customer profiles from disparate data sources. When built with embedded privacy and security controls, such platforms aim to provide experienced marketers with a consistent, governed environment in which AI-driven assistants can operate safely and reliably.

Enhancing team productivity

One of the more practical promises of generative AI lies in its ability to improve team efficiency. Conversational AI interfaces can transform content and campaign data into digestible insights, eliminating the need for endless dashboard toggling. These insights can guide marketers to the most responsive segments, creative formats, or messaging based on historical performance.

Take Tapestry, for example—a brand that leveraged generative AI to create digital twins of its products. The result? Improved internal collaboration, faster decision-making, and products that better matched customer expectations.

Last year, IPG Health’s Studio Rx developed ‘Rxies’, a set of illustrated characters co-created with generative AI. From hand-drawn sketches to over 20 production-ready assets, the team was able to scale content output rapidly. Here, AI functioned less as an idea generator and more as a creative amplifier.

Another touted benefit of generative AI is its potential to democratise access. These tools can act as intelligent guides across enterprise platforms—particularly useful for junior staff or less technically adept teams. A new marketing executive at an apparel brand, for instance, could use AI tools to interpret customer data and initiate campaign planning without the need for analyst support.

With simple text prompts, marketers can access pre-built frameworks, auto-generated creative drafts, and audience insights—all designed to fast-track decision-making and reduce reliance on siloed departments. While the efficiency gains are clear, the challenge lies in ensuring that such ease does not come at the cost of strategic depth.

Managing the hype, meeting expectations

Despite the growing list of use cases, marketers should approach generative AI with a mix of curiosity and caution. The push for AI adoption is often led by technology providers with commercial interests, and the temptation to view it as a magic bullet is strong. In reality, effective deployment demands strategic intent, strong governance, and a robust data ecosystem.

Customer experience is increasingly being positioned as the key business differentiator. Generative AI may help deliver on this promise, but only if it is implemented with precision and clarity of purpose. The technology’s ability to anticipate, personalise and automate is useful—but it is no substitute for critical thinking or brand empathy.

While the future of customer experience may well include generative AI, its success depends on how businesses integrate it into their operations. Experimentation is necessary—but so is restraint. The focus should remain on solving real customer problems, not simply showcasing AI capability.

The call to action for marketers is not just to embrace generative AI, but to do so with a deliberate plan: one that includes testing, refinement and scaling based on clear, measurable outcomes. With a strong data foundation and thoughtful application, generative AI can offer value—but only if marketers remain clear-eyed about what it can and cannot do. 

— Vyshak Venugopalan, director—solution consulting, Adobe India.