A recent McKinsey study estimates that generative AI alone could add between $2 trillion and $4 trillion annually to the global economy, with a major impact on companies that can apply this technology effectively. Companies that successfully adopt generative AI see significant improvements in productivity, operational efficiency, and service personalization. However, the real value is only realized when these tools solve specific problems and deliver measurable results.
In episode 114 of #DoTheMATH, MATH's podcast, we discussed the topic with Rafael Alberti, Delivery Director at MATH , and we talk more about the topic in this content.
Keep reading!
How to generate value with Artificial Intelligence
Artificial intelligence (AI) is only truly effective when it generates real, measurable value for businesses. To achieve this goal, it’s essential to focus on areas where AI can solve specific problems, optimize operations, and personalize the customer experience.
According to the McKinsey study, companies that effectively implement AI can increase their profitability by up to 20%. This value is generated through improvements in operational efficiency, cost reduction and increased revenue through personalized services.
In episode 114 of the DoTheMATH podcast, Marcel Ghiraldini, CGO of MATH, emphasizes that the success of AI depends on the quality of data and the clarity of business objectives. Without accurate and well-governed data, any AI application may fail to deliver the desired results. This reflects the importance of robust data governance and a clear strategy for AI implementation.
Furthermore, Rafael Alberti, Director of Delivery at MATH, mentions that artificial intelligence must be applied in a way that truly meets the needs of customers and the market. He highlights that in the financial sector, for example, AI is particularly useful for personalization at scale, enabling banks to offer experiences and services tailored to each individual customer.
To ensure that it generates value, companies must:
Focus on quality data, as accurate and well-organized data is the foundation for any successful AI application.
Align AI with business objectives, considering that AI should be used to solve specific problems that have a direct impact on the company's results.
Invest in personalization. Personalization at scale, in this case, can be one of the most get russian phone number online effective ways to use AI to improve the customer experience and increase revenue.
Implementing these strategies not only maximizes the value generated by AI, but also ensures that companies remain competitive in an increasingly data-driven marketplace.
Regardless of the hype surrounding generative AI, its true value lies in its ability to solve real problems and deliver tangible benefits to businesses and their customers.
Listen to episode 114 of #DoTheMATH, the MATH podcast
Personalization at scale
Personalization is one of the biggest benefits that artificial intelligence (AI) can bring to businesses. With AI, institutions can go beyond traditional segmentations and offer a truly personalized experience to each customer. This translates into more relevant interactions and the delivery of solutions that precisely meet the individual needs of customers. When we talk about the banking universe, this is a perfect fit.
Also during episode 114 of the DoTheMATH podcast , we discussed how generative AI allows banks to personalize their customer experience on a scale previously unimaginable. Rafael Alberti emphasized that AI allows banks to treat each customer as a unique individual, analyzing real-time data to offer personalized and contextualized solutions.
This personalization at scale not only improves customer satisfaction, but also increases the operational efficiency of financial institutions. Fabiana Amaral , Executive Director of CX and Marketing at MATH , highlighted that AI enables each interaction to be tailored to customer preferences and behaviors, which radically transforms the experience. This ability to personalize at scale is essential to remain competitive in an increasingly data-driven market.
Also read: Hyper-Personalization in Banking Marketing: The Future with AI
Connecting Personalization, Scale and Customer Experience
The combination of personalization at scale and AI has the potential to redefine the relationship between banks and their customers. By using AI to analyze large volumes of data and apply real-time insights, banks can deliver service that not only meets, but exceeds customer expectations.
This level of personalization is key to building loyalty and trust, especially in an industry where customer experience can be the competitive differentiator. By integrating generative AI into their operations, banks can ensure that each transaction is meaningful, relevant, and perfectly tailored to the user’s needs, creating an experience that is both efficient and personalized.
Personalization at scale, in turn, powered by AI , represents a new frontier in customer experience, one that financial institutions cannot afford to ignore. As noted in the podcast, the ability to deliver a unique, personalized experience to each customer is what truly sets companies apart in the modern marketplace.
The AI revolution in banking marketing
When it comes to the financial sector, AI has been a central tool in transforming marketing in this area. Since the 1950s, the tool has evolved significantly, but it was with the arrival of generative AI that the most notable changes took place.
According to Ghiraldini, generative AI stands out for its learning capacity and the use of large-scale language models (LLMs) that allow it to process vast amounts of data quickly, creating original content based on pre-existing knowledge.
In the banking sector, Alberti points out that AI has been used for years to analyze risks and fraud. However, generative AI expands these capabilities, offering more accurate predictions and unprecedented personalization in customer interactions. This personalization at scale not only improves the customer experience, but also increases the efficiency and competitiveness of financial institutions.
Finally, we know that both artificial intelligence, and especially generative intelligence, has the potential to transform entire industries, but its success depends on its ability to generate real value. Companies that know how to apply this technology to solve specific problems and improve operational efficiency will see significant returns on their investments.
However, implementing AI must be done with a clear vision of how this technology will add value, otherwise efforts may be in vain.