What is your job at Telefónica?
I work in the global B2B area at Telefónica, where we support strategic initiatives in all the countries where we operate.
In the case of artificial intelligence, my work focuses on creating a global framework that allows us to take advantage of the best practices developed in one country, adapt them and scale them to others, ensuring that we are all aligned.
I work directly with teams in different countries to align strategies, share best practices and make the most of technology in business and internal processes.
My responsibility is to help identify process pain points, lead proof-of-concepts (PoCs) alongside local teams and ensure that the AI solutions implemented are aligned with business objectives.
This approach enables continuous process optimisation and ensures that best practices in one country can be replicated in others, maximising impact globally.
What is generative artificial intelligence?
Generative AI is a branch of artificial intelligence designed to create new content – from text and audio to images and videos. This is achieved through advanced models such as transformers, which analyse patterns in large volumes of data to generate creative and contextually relevant results.
What makes this technology unique is not only its ability to ‘create’, but also its versatility to adapt to a wide variety of use cases in sectors such as healthcare, education, marketing and more. It is a powerful tool that has the potential to transform the way we work.
Beyond being an advanced technology, its real value lies in how, as experts in our own processes, we can leverage it to improve and extend what we already do well.
Generative AI not only analyses data and detects patterns, but also generates original content, from text and code to images or simulations. This not only simplifies everyday tasks, but also opens up new opportunities to optimise and reinvent our operations.
How does generative AI work?
Generative AI works by training deep learning models with massive datasets. It uses advanced neural networks, specifically transformers, to understand context and generate coherent and relevant answers or content.
A simple example is how a model like ChatGPT ‘learns’ language patterns by exposing itself to millions of textual examples. Upon receiving an input, it generates predictive responses based on that prior knowledge. However, its effectiveness depends directly on the quality of the dataset used and the clear objectives defined during its development.
As I mentioned, generative AI is based on deep neural networks trained to identify complex patterns in large volumes of data. But what really makes the difference is how we align this technology with our specific needs.
In my experience, successful implementation lies in thoroughly understanding our processes and correctly mapping the pains before applying the technology.
This allows AI to stop being just a generic tool and become a customised solution, designed to respond to our real needs, working collaboratively with all teams to deliver meaningful results.
How are generative AI models trained?
Training generative AI models consists of feeding deep neural networks with large volumes of data, a process that takes place in two main stages:
- Pre-training: in this initial phase, the model learns general patterns from the data, establishing a broad knowledge base.
- Fine-tuning: Here, the model adapts to specific use cases, refining its behaviour to provide more relevant results aligned with specific needs.
This process is not linear, but iterative, involving constant optimisation based on testing and fine-tuning. It is therefore critical to identify and prioritise key processes to ensure that the model is truly responsive to business needs.
While generative AI models start with massive data, the real value lies in contextualising that data. From my experience, we work to ensure that AI is trained not only with relevant information, but also with a deep understanding of each country’s processes and objectives.
This includes working closely with local teams to integrate specific insights into models, adapting them to the specifics of the context and ensuring that solutions are useful, personalised and effective.
What are the benefits of generative AI?
Generative AI stands out for its ability to automate routine tasks, personalise experiences and optimise operational efficiency. Its key advantages include:
- Creativity at scale: Generate content tailored to different audiences quickly and effectively.
- Time savings: Simplifies processes such as reporting, data analysis and other repetitive tasks.
- Versatility: Adapts to different sectors, offering significant results in contexts as varied as health, education or marketing.
However, beyond these advantages, its greatest strength lies in how it complements human knowledge. While generative AI can automate and create large-scale solutions, its true potential is manifested when we combine our experience and judgement to drive it. This allows us to:
- Identify and solve specific problems faster.
- Design personalised services and experiences that truly connect with customers.
- Reduce operational costs by optimising resources and processes.
- Improve consistency and quality of results in internal operations.
Generative AI is not only an advanced tool, but an amplifier of our capabilities, helping us to evolve and transform the way we work and create value.
What sectors is this type of artificial intelligence likely to affect in particular?
The impact of generative AI is broad and spans multiple key sectors, highlighted by its ability to transform processes and improve outcomes. Examples include:
- Marketing and advertising: Precise personalisation of campaigns and creation of content tailored to different audiences.
- Education: Development of customised learning materials, tailored to the needs of individual learners.
- Healthcare: Advanced medical data analysis, diagnostic support and detailed clinical reporting.
- Telecommunications: Process automation, network optimisation and improved customer experience.
- Industry: Optimisation of operational processes, predictive maintenance and generation of simulations to improve efficiency.
Although generative AI has applications in virtually all sectors, its impact depends directly on how each industry interprets its processes and detects pain points. Success lies in identifying and prioritising these key areas, adapting the technology to maximise the value it can bring.
How is this type of AI different from others?
While other forms of artificial intelligence, such as descriptive AI and predictive AI, focus on analysing historical data and anticipating future events, respectively, generative AI stands out for its ability to create new and original content.
It is a tool that goes beyond simple interpretation or prediction, providing a creative dimension designed to complement and enhance human capabilities.
- Descriptive AI: Analyses historical data to identify patterns and provide insight into what has already happened.
- Predictive AI: Uses that data to predict future trends or outcomes, aiding strategic decision-making.
- Generative AI: Goes one step further. Instead of interpreting or predicting, it creates something completely new, such as text, images, sounds or even simulations. This makes it a technology that is not only analytical, but also creative and innovative.
What really sets generative AI apart is its ability to adapt to our specific processes and needs. While descriptive and predictive AI provide information to optimise existing processes, generative AI allows us to innovate directly on top of them.
Its flexibility allows it not only to adjust to our objectives, but also to evolve according to changes in the environment.
However, the real impact of generative AI depends on our ability to define clear objectives and structure our processes well. When applied strategically, it not only transforms how we work, but also expands the possibilities of what we can create, allowing us to evolve and compete in an ever-changing world.
Which person working at Telefónica do you nominate for this interview who you consider to be excellent at their job?
I would like to nominate two great professionals who, from their expertise, can provide a technical and grounded perspective on the impact of AI:
- Carlos Rabazo: with extensive experience in digital transformation and innovation, his approach from Wayra and Open Future is key to understanding how to connect technology and business.
- Paulo Cocato: head of AI at Telefónica Brazil, who leads innovative initiatives in digital transformation. His experience in AI integration in different sectors adds immense value to this conversation.
