AI and personalization: how to balance innovation and responsibility?

AI-driven personalization is transforming how we interact with digital products. With increasingly sophisticated algorithms, companies can offer more intuitive, predictable, and tailored experiences to individual user needs. 

A report from Маккінсі Indicates that 71% consumers expect personalized interactions, and 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.

Personalisation has always been a key differentiator in customer service, but until recently it was a manual and laborious process. Now, AI isn't just following pre-set rules. It learns from every 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. This is where the paradox of automation comes in: AI can replace certain functions, but it doesn't eliminate the need for the human element – in fact, what happens is a reinvention of roles in the job market. These models need to be fed with relevant and contextualized data to truly add value to the client, and those who understand this shift and adapt quickly will have a huge competitive advantage.

Now, the great opportunity isn't just about optimizing processes, but about creating new business models. AI allows businesses that previously lacked the scale to compete to now offer advanced personalization and even new ways of monetization, such as on-demand artificial intelligence services.

How can businesses balance innovation and responsibility to ensure positive impacts?

AI must be a facilitator, not a controller. I outline three fundamental pillars:

• Transparency and ExplainabilityThese are essential for users to understand how AI makes decisions. AI models cannot be "black boxes"; clarity on the criteria used is needed to avoid mistrust and questionable decisions.
  
• Privacy and security from the design stageData security and protection can't be an "afterthought" once the product is finished. It needs to be considered from the outset of development.
  
• Multidisciplinary teams and continuous learningA IA requires integration between technology, product, marketing, and customer service. If teams don't work together, implementation can become misaligned and ineffective.
  

Digital product personalization and usability

The impact of AI on personalization comes from its ability to process and learn from vast amounts 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 dynamically adjust recommendations, tracking user behaviour.

This solves a critical problem: scalability. With AI, companies can offer hyper-personalised experiences without needing a massive team making manual adjustments.

Furthermore, AI is improving the usability of digital products, making interactions more intuitive and fluid. Some practical applications include:

• Assistentes virtuais that truly understand the context of conversations and improve over time;
• Recommendation platforms that adjust content and offers automatically based on user preferences;
• Need anticipation systems where AI predicts what the user might need before they even search.

AI isn't just improving existing digital products; it's establishing a new standard of experience. The challenge now is finding the balance: how to use this technology to create experiences that are both more human and more efficient? 

The key to innovation lies in putting the user at the heart of the strategy. Well-implemented AI should add value without the user feeling they've lost control of their data. Companies that balance innovation and responsibility will have a competitive advantage in the long term.