Beyond Buzzwords: What It Takes to Build an Intelligent Customer Experience

Forget the hype. Here’s the real architecture powering fast, personalised, AI-driven customer experience…but without real-time activation, even the smartest models won’t move the needle.

A 2025 Verint research revealed that 86% of consumers now expect AI for rapid problem-solving in customer service, with 98% of 18 to 34-year-olds particularly enthusiastic.

No wonder, the competition to appease customer expectations is brutal. And brands, driven by this urgency and ambition, are reimagining their entire CX engine. In the race to deliver faster, more personalised experiences, they’re being forced to overhaul their data infrastructure and rethink how machine learning (ML) can be applied in real time.

From chatbots that actually understand customers to intelligent recommendation engines that adapt as one browses, the customer experience (CX) stack is getting a serious AI upgrade. 

“The most missed step is connecting the AI outcome to the customer experience channel. Without the activation piece, AI is basically just an expensive science project,” says Ali Behnam, Co-Founder of Tealium.

So what’s really under the hood of these smart systems? And how can brands deploy these models without sacrificing speed, data integrity, or user trust?

The Models Moving CX Forward

At the foundation of today’s smarter CX systems are several advanced ML models that go beyond traditional analytics.

“Retrieval-Augmented Generation (RAG) is one of the biggest breakthroughs,” says Anshul Gandhi, AI evangelist and former Senior Machine Learning Engineer at Dell Technologies. “It combines large language models with real-time data retrieval to deliver personalised, contextually relevant responses—improving both customer support and product discovery.”

Meanwhile, multi-modal models like GPT-4V and Gemini are giving machines the ability to understand not just text, but also images and audio. “This helps brands understand customer preferences more holistically by incorporating rich, multi-sensory interactions,” he adds.

He also points to Mixture-of-Experts (MoE) models, such as Google’s Switch Transformer, that optimise both relevance and efficiency. “These models intelligently route inputs to specialised pathways based on factors like geography or intent, allowing precise personalisation without bloating compute usage.”

On the cutting edge? Agentic AI systems. Research states that 68% of customer support interactions are projected to be managed by agentic AI by 2028  “These agents can perform complex tasks autonomously, adapting recommendations and workflows in real time as customer behavior changes,” he says. It’s the difference between a reactive chatbot and a proactive digital concierge.

But Real-Time Isn’t Easy

For all their potential, deploying ML models in real-time CX environments, like ecommerce platforms or service chatbots, is no small feat.

“Latency is a critical constraint,” Anshul notes. “Responses need to be near-instant, which requires techniques like model quantisation, pruning, and deployment on edge or cloud-native infrastructure.”

Data quality also poses a challenge. “Streaming pipelines must have real-time validation and anomaly detection. One flawed input can degrade performance.”

Anshul stresses that adaptability is key. “Customer behaviour shifts constantly, so models need regular monitoring, A/B testing, and retraining.” He adds that many brands reduce costs using model distillation and autoscaling, which make powerful models more efficient without losing accuracy.

Integration is another puzzle especially in legacy environments. “Most companies still rely on microservices and event-driven architectures like Kafka to make everything talk to each other,” he explains.

And then there’s ethics and explainability. “Cold-start problems in personalisation are mitigated using hybrid approaches, collaborative filtering, contextual models, reinforcement learning—but fairness audits and explainability frameworks are non-negotiable now.”

Looking Ahead: Autonomous Agents and Predictive Experiences

So what’s next? Anshul believes we’re about to see a fundamental shift in how brands engage customers.

“The next frontier is autonomous AI agents. Systems that don’t just respond to customers but can plan, reason, and take action across platforms,” he says. “They’ll interpret voice, visuals, emotional tone, and then curate products, resolve issues, or schedule services—all in real time.”

Major players are already moving in this direction. For instance, NICE recently unveiled CXone Mpower Orchestrator, the first end-to-end AI automation platform integrating front- and back-office orchestration. Adobe launched Agent Orchestrator and Brand Concierge within Adobe Experience Platform. These agentic AI tools can optimise websites, manage daily content tasks, refine audiences, and deliver conversational, multimodal experiences.

He points to emerging frameworks like OpenAI’s Agent SDK, LangChain, and Microsoft’s Semantic Kernel as core enablers. “Memory, planning, tool use, multi-agent collaboration… these aren’t just buzzwords. They’re how you build hyper-personalised experiences that learn and evolve continuously.”

In his view, this is not just a technology transformation—it’s a redefinition of what customer experience even means. “We’re moving from CX as a support function to CX as a value engine—anticipating needs, acting autonomously, and doing it at scale.”

Bottom Line for Brands

The shift is underway, but brands still have work to do. Anshul warns against chasing hype without preparation.

“Most companies still struggle with data foundations. Without clean, unified, and accessible data, even the best models won’t deliver,” he says. “You need to fix the pipes before you install the AI.”

““Relevant data is number one—data that’s filtered and without the noise. Number two, it’s timely. Number three, it’s consented. And finally, it’s contextualised with proper semantic clarity,” adds Ali. 

For brands looking to future-proof their CX, the message is clear: treat AI not as a magic layer, but as a deeply integrated stack from infrastructure to intelligence. And, above all, build with both performance and people in mind.

ALSO READ: AI in Martech: The Hidden Engine Powering Modern Customer Experience