AI in CX: The Comet Has Returned—And This Time, It Exploded
Back in January 2017, I wrote an article – AI in CX – likening artificial intelligence (AI) to a comet—repeatedly flaring into view with dazzling promise, only to fade into the cold darkness of unmet expectations. At the time, AI had delivered some real value, mostly in the form of automation and predictive analytics, but much of it still felt superficial. The tools were impressive, but not yet transformative. I concluded that while AI had made progress, the real revolution was still ahead.
That revolution arrived in November 2022.
When OpenAI released ChatGPT to the public, it marked a seismic shift in how businesses and consumers perceived and interacted with AI. For the first time, AI wasn’t just a backend tool for marketers or a silent assistant in your smartphone. It was front and center—conversational, creative, and shockingly capable. The AI comet didn’t just return; it crash landed.
From Automation to Augmentation
In 2017, AI in CX was largely about automation and using traditional statistical models for predictions. We saw gains in intelligent routing, basic chatbots, and predictive recommendations. These were (and still are) valuable, but they didn’t fundamentally change the ability to create content at scale for 1 to 1 personalization and to achieve better customer experience.
Fast forward to 2025, and we’re in a different world. AI is no longer just powering decision making at run-time, improving customer experience in digital and physical channels, and powering content production at design time. Large language models (LLMs) like GPT-4 and its successors are now embedded in customer service platforms, sales enablement tools, marketing engines, and in marketing content operations solutions. They don’t just deflect simple queries—they resolve complex issues through dynamic conversations, generate personalized content, and even detect customer sentiment in real time.
Consider the modern contact center. AI agents now handle the majority of customer interactions, not with rigid scripts, but with fluid, empathetic dialogue. They understand nuance, context, and intent. When escalation is needed, they summarize the issue for human agents, reducing resolution time and improving satisfaction. This isn’t superficial intelligence—it’s deeply integrated, high-value augmentation.
The Rise of Personalization at Scale
One of the most profound shifts since 2017 is the rise of personalization at scale – meaning having something unique and differentiated to say and show to each customer – and figuring that out and delivering it in real time. Back then, personalization meant inserting a customer’s name into an email or recommending products based on past purchases. Today, AI crafts entire customer journeys tailored to individual preferences, behaviors, and even emotional states. AI also detects sentiment, context, and intent – synthesizes these insights, and factors them into decisions – again all in real time.
LLMs analyze vast amounts of unstructured data—emails, chat logs, social media posts—to build dynamic customer profiles. These profiles inform every touchpoint, from marketing messages to service interactions. The result? Customers feel seen, understood, and valued.
This level of personalization was once the holy grail of CX – a theory in books. Now, it’s attainable in practice.
Conversational Interfaces: The New Front Door
In 2017, I pointed to early chatbot experiments and voice assistants as promising but limited. They often frustrated more than they helped. Today, conversational interfaces are the new front door to many brands.
Whether it’s a voice assistant embedded in a car, a chatbot on a website, or a virtual agent in a mobile app, customers increasingly prefer to “talk” to businesses. And thanks to LLMs, those conversations feel natural, helpful, and even enjoyable.
These interfaces are also multimodal—capable of understanding text, voice, images, and even video. A customer can snap a photo of a broken product, describe the issue in natural language, and receive a resolution—all without speaking to a human.
Trust, Transparency, and the Human Touch
Despite these advances, the human element remains critical. In fact, as AI becomes more capable, the need for trust and transparency grows. Customers want to know when they’re interacting with AI. When that experience is frustrating – taking too long – they want to be quickly connected to a human – and they want context to be remembered and factored in. They want explanations for decisions – especially AI-driven ones.
Leading brands are embracing this by designing hybrid experiences—AI handles the routine, while humans focus on empathy, quality control, creativity, and complex problem-solving. This synergy is where the real magic happens. Great brands get this, and have expert humans monitoring AI, and quickly course correcting it – always with the consumer’s experience in mind.
The Organizational Challenge
As I noted in 2017, the biggest barrier to AI adoption isn’t the technology—it’s the organization. That’s still true today. Many companies struggle to integrate AI into their workflows, data systems, and culture. They chase shiny tools without rethinking their processes, understanding the use cases, or upskilling their teams.
The winners in this new era are those who treat AI not as a bolt-on, but as a core capability. They invest in data governance, cross-functional collaboration, and continuous learning. They empower employees to work alongside AI, not fear it.
What’s Next?
So, where do we go from here – now that all of the AI children are growing up?
One of the next frontiers is emotional intelligence. AI is getting better at detecting and responding to human emotions. Sentiment analysis is evolving into affective computing—systems that can sense frustration, joy, or confusion and adapt accordingly. This will unlock new levels of empathy in digital interactions.
We’ll also see more proactive AI. Instead of waiting for customers to reach out, AI will anticipate needs and offer help before problems arise. Think of it as predictive CX—where the best service is the one you never needed to ask for.
And we’ll continue to see AI as a content production assistant, helping brands put more of the right content on the inventory shelf, and then using AI to help just-in-time assemble the right piece of content for the right person, and quickly test and learn which ones resonate.
And finally, we’ll see AI become more ethical and accountable. As regulations evolve and public scrutiny increases, businesses will need to ensure their AI systems are fair, explainable, and aligned with human values.
Conclusion: The Comet Has Become a Constant
In 2017, I ended with a prediction: the AI comet would return. It did—and this time, it’s not fading away into an AI winter – it’s here to stay. AI is no longer a novelty or a niche. It’s a foundational technology reshaping how we live, work, and connect.
For customer experience professionals, the message is clear: embrace the new normal. Invest in real intelligence, not superficial gimmicks. Focus on outcomes, not just outputs. And above all, remember that the goal of AI isn’t to replace humans—it’s to help us be more human to more people – at scale.
The comet has crash landed. Let’s build something extraordinary with the rare materials that came with it.