AI-driven personalization transforms how we interact with digital products. With increasingly sophisticated algorithms, companies can offer more intuitive, predictable, and tailored experiences to meet users’ individual needs.
A report from McKinsey points out that 71% of consumers expect personalized interactions and that brands investing in this can increase their revenue by up to 40%. However, this scenario also raises questions about privacy, technological dependence, and the limits of automation in consumer experience.
Personalization has always been a differentiator in customer service, but until recently, it was a manual and labor-intensive process. Today, AI doesn’t just follow fixed rules. It learns from each interaction, dynamically adjusting recommendations to better understand user preferences.
But that doesn’t mean it’s easy. The major challenge lies in training specific models for each company. This is where the automation paradox comes in: AI can replace certain functions, but it doesn’t eliminate the need for the human factor—in fact, what happens is a reinvention of roles in the job market. These models must be fed relevant and contextualized data to truly add value to the customer, and those who understand this shift and adapt quickly will gain a huge competitive edge.
Now, the big opportunity isn’t just in process optimization but in creating new business models. With AI, companies that previously lacked the scale to compete can now offer advanced personalization and even new monetization methods, such as on-demand AI-based services.
How can companies balance innovation and responsibility to ensure positive impacts?
AI must be an enabler, not a controller. I outline three fundamental pillars:
- Transparency and explainability: these are essential so users understand how AI makes decisions. AI models can’t be ‘black boxes’; clarity about the criteria used is necessary to avoid distrust and questionable decisions;
- Privacy and security by design: security and data protection can’t be an ‘afterthought’ once the product is ready. This must be considered from the start of development;
- Multidisciplinary teams and continuous learning: AI requires integration between technology, product, marketing, and customer service. If teams don’t work together, implementation can become misaligned and ineffective.
Personalization and usability of digital products
AI’s impact on personalization stems from its ability to process and learn from large volumes of data in real time. Previously, personalization relied on static rules and fixed segments. Now, with Linear Regression combined with Neural Networks, systems learn and adjust recommendations dynamically, tracking user behavior.
This solves a critical problem: scalability. With AI, companies can offer hyper-personalized experiences without needing a massive team making manual adjustments.
Furthermore, AI is improving the usability of digital products, making interactions more intuitive and seamless. Some practical applications include:
- Virtual assistants that truly understand conversation context and improve over time;
- Recommendation platforms that automatically adjust content and offers based on user preferences;
- Anticipatory systems, where AI predicts what users might need before they even search for it.
AI isn’t just improving existing digital products—it’s setting a new standard for experience. The challenge now is finding the balance: how to use this technology to create more human and efficient experiences simultaneously?
The key to innovation lies in placing the user at the center of the strategy. Well-implemented AI should add value without making users feel they’ve lost control over their data. Companies that balance innovation and responsibility will gain a long-term competitive advantage.
