The relationship between brands and people is undergoing a true technological revolution in marketing . The combination of customer experience and artificial intelligence has gone from being a laboratory experiment to becoming the driving force that separates brands that inspire love from those that are merely "there." Today, consumers are no longer satisfied with simply receiving a good product: they want to feel that every interaction is designed for them, in their context, at their moment, and on their preferred channel.
In this new landscape, AI—especially generative AI—has permeated every aspect of the customer journey: from designing creative campaigns to handling complaints in chats or recommending the next product to buy. Companies that successfully combine automation, data, and human judgment are creating memorable, hyper-personalized, and, moreover, much more cost-effective experiences.
What is generative AI really and why is it changing the customer experience?
When we talk about generative AI, we're not just referring to "magical" tools that write texts or generate images; we're talking about models trained on huge volumes of data capable of identifying patterns, learning structures, and producing new content: text, images, audio, video, or even synthetic voices.
These models rely on machine learning and deep learning techniques that allow them not only to mimic human decisions, but also to combine and scale them in ways impossible for a purely human team. While traditional AI typically focuses on classifying, predicting, or segmenting, generative AI adds a creative layer: it writes, designs, composes, and proposes.
In marketing and customer experience, the most interesting thing happens when both layers are integrated: traditional AI analyzes who to reach, when, and on which channel; generative AI is responsible for producing the most appropriate content for that person and that context. One engine decides who; another creates the what and the how . This pairing is the foundation of many advanced personalization strategies in user experience.
There is no shortage of well-known examples: models such as GPT-4 or DALL·E have become popular, but in parallel many companies have started to develop their own or semi-customized models , trained with their internal data, their brand tone and their specific needs.
A clear example is IBM's Granite foundational model library, fine-tuned with business data from sectors such as legal, academic, and financial, so that AI behavior better aligns with real-world, regulated business scenarios. From there, each company can overlay its historical customer interactions and build specific layers for sales, support, marketing, or analytics.
Foundational models, proprietary data, and maturity in AI adoption
The major qualitative leap comes when organizations combine robust foundational models with their own data . That's where AI ceases to be a generic tool and becomes a strategic advantage that is very difficult for competitors to replicate.
In practice, this means taking a general-purpose model (trained on broad and diverse data) and "fine-tuning" it with internal information: purchase histories, chat conversations, support emails, survey feedback, complaints, etc. With this additional layer, the AI learns the brand's language, its customer profile, and the nuances of its industry.
According to recent studies—such as those by the IBM Institute for Business Value— more than half of marketing directors are already considering developing foundational models based on their organization's own data. They are not only looking to save time, but also to build differentiated AI assets that will deliver value for years to come.
In terms of maturity, we can generally distinguish three levels of AI adoption within companies: from the occasional use of pre-designed tools, through projects more integrated into marketing and service, to the digital transformation of the entire organization driven by AI , where technology permeates processes, culture, offerings and customer relations.
Although many companies started with "off-the-shelf" solutions—basic chatbots, FAQ assistants, email automation—it is becoming increasingly common to see projects that integrate real-time AI with multiple data sources and a strong business personalization component.
Intelligent automation: chatbots, assistants, and 24/7 support
One of the areas where AI has gained the most traction is customer service. Service departments handle enormous volumes of interactions , and a large portion of them are repetitive or low-complexity—precisely the kind of tasks that AI can handle with ease.
Chatbots and virtual assistants have been around for years, but generative AI has breathed new life into them: they can now offer much more natural, contextual, and accurate responses , remembering the thread of the conversation and adapting to the user's tone. It's no longer just about answering a frequently asked question, but about maintaining fluid dialogues that feel less robotic.
Salesforce data indicates that around 69% of high-performing customer service leaders already use AI, and it is estimated that, by 2025, around 95% of customer interactions will be managed in some way by AI systems, primarily through chatbots and conversational assistants.
This intensive use of AI in frontline care has a dual effect: on the one hand, it reduces costs and response times; on the other, it frees people from routine tasks so they can focus on complex or emotionally valuable cases, where the human touch remains irreplaceable.
In fact, IBM studies point to average reductions of 33% in response times when AI is integrated into service flows, resulting in less frustrated customers and less overwhelmed employees.
From data to insight: advanced analysis and behavioral prediction
AI doesn't just respond; it also observes. In a context where companies accumulate mountains of data—transactions, web browsing, social media, app interactions, reviews, etc.—the great challenge is to move from that chaos of information to actionable insights in real time.
Machine learning algorithms and predictive analytics make it possible to detect patterns impossible to see with the naked eye: correlations between times of day and probability of purchase, early signs of leakage, micro-segments with very specific behaviors, or elements of the experience that generate friction.
The numbers speak for themselves: nearly 84% of companies using AI for predictive analytics report a return on investment in less than a year. This is because, thanks to prediction, they can anticipate demand, optimize inventory, adjust prices, and launch campaigns precisely when the customer is most receptive.
A prime example is product recommendations in e-commerce. Systems no longer just look at what you've bought before, but also how you browse, what you discard, how much time you spend on each product page, and even your emotional state if more advanced interaction data is available. Every click feeds into the model , which refines its suggestions in a matter of seconds.
Meanwhile, technologies like sentiment analysis and emotion measurement are gaining traction. The global AI-powered sentiment analysis market, valued at $2,71 billion in 2020, could reach around $15,83 billion by 2026. Brands can use these tools to better understand the tone of conversations, identify customers on the verge of frustration, and proactively adapt their approach.
AI that feels: emotions, voice, face, and immersive experiences
The line between human and digital blurs when AI begins to recognize not only what the customer says, but how they say it and how they feel . Advances in computer vision and voice processing allow for the identification of emotional nuances in real time, connecting with sensory marketing.
Some companies already use systems that analyze intonation, voice rhythm, or facial expressions during a call or video call to adjust the response: if they detect anger, they prioritize empathy and quick solutions; if they perceive doubt, they offer more detailed explanations; if they see enthusiasm, they can take the opportunity to suggest an upsell without being intrusive.
Brands like Ikea and Duolingo have taken it a step further by creating virtual assistants with their own personalities . These aren't just neutral bots, but characters aligned with the brand's identity: approachable, casual, serious, educational… This makes interactions more memorable and strengthens the emotional connection with the user.
There are some very interesting experiments, such as systems that compose personalized hold music in real time, adapted to the customer's profile and the reason for their call, or AI-powered hyper-realistic digital avatars that answer calls via video and provide a sense of human presence in purely digital contexts.
Generative AI is also opening the door to immersive experiences in augmented reality (AR) and virtual reality (VR). In the metaverse and similar environments, AI can generate dynamic scenarios that adapt to user behavior and preferences: showrooms that change according to your tastes, interactive narratives where your decisions alter the story, or virtual assistants that remember what you've seen, what you've tried, and what you liked.
Extreme personalization: from segmentation to hyper-personalization
Personalization is no longer just about sending an email with the recipient's name in the subject line. Consumers expect to receive interactions that feel tailored specifically for them by 2025 , in real time and across all the channels they use—a demand that explains the value of a user experience course for brand managers.
Generative AI allows you to go far beyond traditional segmentation: it can create unique messages, recommendations, offers, and creatives for each person, taking into account their history, recent behavior, context, and, in some cases, their emotions.
Companies that use AI to personalize the customer experience have seen average revenue increases of around 19%, according to estimates such as those from McKinsey. Furthermore, nearly 63% of consumers believe that AI will improve their relationship with brands if used appropriately, reinforcing the idea that well-executed personalization builds trust.
There are many practical examples: from emails with recommendations perfectly aligned with your history and preferences, to dynamic, real-time offers that adjust if you've abandoned a shopping cart, haven't purchased in a while, or belong to a high-value segment. Even ads can be generated "on the fly" to adapt to the emotion detected on your face in an augmented reality environment.
This type of hyper-personalization not only improves the experience; it also increases conversion rates and strengthens loyalty. 86% of consumers say that personalized experiences increase their loyalty to certain brands, according to studies such as Twilio's State of Customer Engagement Report.
Predictive AI: anticipating the customer before they speak
The key difference between yesterday's analytics and today's AI is that we now know not only what has happened , but also what is likely to happen if we do nothing. Predictive AI has become a crucial ally in anticipating customer problems and desires.
By analyzing historical and real-time data, the models can predict purchase timing, abandonment rates, fraud risk, or the need for additional support. This allows for proactive actions: a preventative email before your points expire, a help message when you seem stuck in the registration process, or a human call when the system detects a spike in frustration.
In sectors like insurance, for example, AI-powered voice analysis is already being used during claims calls to detect patterns associated with potential fraud. The goal isn't to accuse the customer, but rather to identify cases that warrant closer review by the human team.
More advanced models, based on causal AI, are beginning to go beyond correlation: they seek to understand which factors truly drive certain behaviors. This enables more precise business actions, such as dynamically adjusting prices or designing fully adaptive experience paths based on what really motivates the customer's decision.
The result is an experience that the user perceives as smoother and more timely , while the company optimizes resources, reduces complaints and avoids unnecessary leaks.
Customer AI and the new generation of experience platforms
To fully exploit this potential, simply having "an AI" is not enough. Many brands are investing in platforms that unify data, models, and channels under a single umbrella, commonly known as Customer AI solutions or advanced personalization suites.
The approach of tools like Twilio, for example, involves combining large language models (LLM) with customer data and communication logs from their own platform. This allows them to capture real-time signals (clicks, opens, purchases, chat responses, etc.), interpret them with AI, and immediately trigger the next best action in marketing, sales, or service.
The key is that all these decisions are made with a unified view of the customer , breaking down the traditional silos between departments. Instead of marketing having one picture, sales another, and service yet another, the platform builds a dynamic profile that all teams can leverage.
With these types of solutions, companies can move from massive "let's see what happens" campaigns to highly orchestrated journeys where every interaction is adjusted based on the most recent signal: whether you've opened an email, chatted with support, visited the landing page, etc. Every user action is a clue for the AI to refine its next move.
This shift from retroactive marketing (reports on what has already happened) to proactive, AI-driven marketing also changes customer expectations, who begin to perceive the brand as more consistent, more attentive, and more human , even though much of the operation is automated.
Internal challenges: talent, organization, and omnichannel ecosystems
Implementing AI in customer experience isn't just about technology; it also involves redesigning how we work. One of the biggest challenges is talent management , especially in environments like contact centers, where turnover has skyrocketed in recent years.
Reports such as those from Cresta Insights indicate that agent turnover rates have reached nearly 80% since the pandemic, compared to much lower figures beforehand. This instability hinders the implementation of strategic AI projects, increases training costs, and can seriously affect the consistency of the customer experience.
Another challenge is the changing role of brands: in many sectors, customers no longer contact them simply to inquire about an order, but to ask for advice and support . Companies are ceasing to be mere product providers and are becoming trusted advisors, present well before the purchase, in the initial stages of the sales funnel.
This requires building a robust omnichannel ecosystem where the customer chooses their interaction channel at any given time, without the brand forcing them to use the cheapest option. To achieve this, silos must be eliminated , data connected, and it must be ensured that the user can be tracked throughout their entire journey, recognizing them even when they switch channels.
In this context, concepts such as "the right channel" become important: it's not about being on all channels at any price, but about having the ability to decide, in real time, which channel is most appropriate for that person, at that moment and with that intention.
From "I" specialists to "T" talent: the new agency in the AI era
For creatives, agencies, and marketing teams, the rise of generative AI has democratized many technical tasks. Any professional can now generate draft texts, basic reports, or initial proposals using accessible tools. This forces a rethinking of what truly differentiates an agency or creative team, as seen in graphic design portfolios.
The ability to offer strategic clarity, integrated thinking, and intelligent, real-time data analysis is becoming increasingly important. Instead of simply "creating pieces," the best-adapted agencies are taking on the role of business partners, helping their clients decide what to do, why to do it, and how to measure it.
In this context, many are talking about the shift from "I"-shaped talent to "T"-shaped talent. The vertical bar of the T represents deep expertise (SEO, SEM, social ads, UX, data, etc.), which remains critical. The horizontal bar reflects the ability to connect different areas , understand the client's overall business, and translate data and technology into decisions with real impact.
Some teams formulate this idea with value equations where AI and data multiply the potential, but the decisive factor is human judgment. Technology allows us to go from data to conclusion in seconds, but we need someone capable of asking the right question, interpreting the context, and prioritizing among all possible actions.
In this new environment, prompting—the art of giving instructions to AI—becomes a strategic skill. A good prompt isn't a random phrase: it's a blend of creative synthesis, customer knowledge, and a vision of the desired outcome. AI ceases to be just a tool and becomes an intellectual partner that must be guided.
Privacy, ethics and transparency: the price of trust
All this personalization, analysis, and prediction relies on a vast amount of data, much of it sensitive. It's no wonder that one of people's biggest concerns is privacy and the responsible use of information.
Companies that want to leverage AI to improve customer experience absolutely need robust frameworks for data protection, regulatory compliance, and transparent communication. Users must know what data is being collected, how it's being used, and what control they have over it.
In parallel, there is a growing focus on explaining algorithms. Platforms like Meta, as well as some banks and insurance companies, are beginning to offer mechanisms to help users understand, in a basic way, why they receive a recommendation , how an offer was calculated, or what factors influenced an automated decision.
This transparency is key to preventing personalization from being perceived as invasive. The line between “they understand me” and “they’re monitoring me” is very fine; crossing it can erode trust and damage a brand’s reputation in the long run, no matter how sophisticated its AI is.
Furthermore, ethics also involves finding the balance between automation and humanity. Delegating too many sensitive interactions to machines can lead to impersonal or frustrating experiences, especially during delicate situations (complex claims, financial issues, health matters, etc.). Knowing when a human should be involved in the customer journey is just as important as designing well-designed automated processes.
The economic potential is enormous: AI-driven automation is estimated to save companies trillions of dollars in labor costs in the coming years. But if these savings are pursued without considering customer perceptions and their right to privacy, it could backfire.
Everything indicates that the brands that will be best positioned will be those that combine powerful AI with a genuinely human-centered approach , making decisions based not only on what is technically possible, but also on what is ethically desirable and legally sustainable.
In light of all the above, customer experience in the age of AI has become a terrain where creativity, technology, data, and human judgment are constantly intertwined; brands that learn to orchestrate these elements with intelligence and sensitivity will not only offer faster and more personalized interactions, but will also build deeper and more lasting relationships in a market where patience is scarce and the alternatives, endless.