Personalization driven by Artificial Intelligence is transforming the way we interact with digital products. With increasingly sophisticated algorithms, companies can offer more intuitive, predictable experiences tailored to the individual needs of users.
McKinsey report indicates 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 the consumer experience.
Personalization has always been a differentiator in customer service, but until recently, it was a manual and laborious 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 biggest challenge lies in training specific models for each company. That's where the paradox of automation 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 need to be fed relevant and contextualized data so that they truly add value to the customer, and those who understand this movement and adapt quickly will have a huge competitive advantage.
Now, the great opportunity lies not only in process optimization, but in the creation of new business models. With AI, companies that previously lacked the scale to compete can now offer advanced personalization and even new forms of monetization, 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 are essential for users to understand how AI makes decisions. AI models cannot be "black boxes"; clarity is needed regarding the criteria used, avoiding distrust and questionable decisions.
- Privacy and security by design : data security and protection cannot be a "patch" after the product is ready. This has to be considered from the very beginning of development.
- Multidisciplinary teams and continuous learning : AI demands integration between technology, product, marketing, and customer service. If the teams don't work together, the implementation can become misaligned and ineffective.
Personalization and usability of digital products
The impact of AI on personalization comes from its ability to process and learn from large volumes of data in real time. Previously, personalization relied on static rules and fixed segmentations. 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 huge team making manual adjustments.
Furthermore, AI is improving the usability of digital products, making interactions more intuitive and fluid. Some practical applications include:
- Virtual assistants that truly understand the context of conversations and improve over time;
- Recommendation platforms that automatically adjust content and offers based on user preferences;
- Needs anticipation systems, where AI predicts what the user might need even before they search for it.
AI isn't just improving existing digital products; it's creating a new standard of experience. The challenge now is finding the balance: how to use this technology to create more human and efficient experiences at the same time?
The key to innovation lies in placing the user at the center of the strategy. Well-implemented AI should add value without the user feeling they have lost control over their data. Companies that balance innovation and responsibility will have a competitive advantage in the long term.
