In the current digital age, artificial intelligence (AI) has driven numerous innovations. However, it has been the arrival of generative AI that has brought about a true digital revolution in record time. This is confirmed by the Ascendant Digital Maturity Report 2024 from Minsait (Indra Group), which analyses the degree of adoption of AI in more than 900 organisations in 15 sectors. The study confirms that many companies have increased the deployment of generative artificial intelligence in their businesses over the last year, which boosts the presence of this type of technology in the business world.
Compared to his sister Unlike the more traditional AI – ideal for optimising internal processes, improving operational efficiency or revolutionising customer service – generative AI stands out for its multimodality. That is, for its ability to understand, interpret and generate any type of information format: free text, structured data, graphics, diagrams, videos, audios… These qualities have made it a great ally in interaction with humans or in the development of softwarewhere it has incorporated terms such as the so-called “copiloting”, which accompanies programmers in their daily tasks.
“Artificial intelligence promises significant productivity gains in technological management, and it plays a prominent role in the roadmap of various companies as a tool for accelerating efficiency,” says Natalia Clavero, global director of AI at Minsait. This Spanish company specializes in data management and governance, in the deployment of customized AI solutions, and in consulting services for their implementation in its clients’ business processes. In this regard, Minsait It is the most advanced company at a national level in the deployment of co-pilot tools for the development of software.
Clavero points out that the key lies in the approach phase. “In the first stage of adoption, these productivity gains will only be achieved if companies have a robust framework for adoption and measurement of efficiencies that considers all aspects of impact on jobs,” she explains. Later on, this expert continues, these instruments will become a regular resource in operations, “and only companies that have measured their impact will enjoy high competitiveness in the market.”
Generative AI can also make a difference in human interaction. How? By exploiting its power to understand the needs of customers, citizens and employees in natural language. To achieve this, this technology generates the most appropriate content, with informative features whose success was unthinkable just a year and a half ago. In these cases, it is also possible to go one step further: a conversation can be established at any time and on any channel (even through WhatsApp or collaborative environments), and even incorporate a face, eyes and voice into that conversation through adapted and personalized hyperrealistic avatars.
In the first stage of adoption, these productivities will only be achieved if companies have a robust adoption and efficiency measurement framework that considers all aspects of impact on jobs.
Natalia Clavero, global director of AI at Minsait
Regulation and governance of knowledge
The recent approval of the European Law on Artificial Intelligence, which will control the uses and biases of this resource, has contributed to providing certainty about this type of digitalisation. Even so, the governance of AI and, above all, the privacy of the information it manages to operate are issues that concern citizens and administrations.
In the last decade, the management of this personal data has become one of the most valuable assets of companies. With the arrival of generative AI, adapting this information to new language models makes a substantial difference in the ability to obtain precise answers. According to Natalia Clavero, this reality “further highlights the need to incorporate effective policies that facilitate converting this data into the appropriate fuel for the consumption of these models.” It is necessary, this engineer continues, “to establish a balance between the free consumption of said information and the rigorous regulatory policies regarding ethics and privacy” that are beginning to appear. In a context of business information, generally disconnected, in which the treatment of all information is managed in a disaggregated way, and with the proliferation of use cases and generative agents in organizations, the need arises more than ever “not only to govern the data, but also the knowledge,” says Clavero.
Experts argue that this is one of the basic premises for AI to advance at a good pace in the business or institutional environment. In fact, good governance of artificial intelligence facilitates alignment with the global strategy and offers insight into the return on investment. At the same time, it ensures “that the entire life cycle of intelligence is monitored and adapted to procedures, as well as aligned with associated regulations such as the European AI Act.”
Both modalities are necessary
The truth is that the use of artificial intelligence, whether traditional or generative, is advancing at full speed in the heart of organizations at an unprecedented pace. Forecasts predict that this growth will continue in the future. Another Minsait study estimates that AI has the potential to impact 25% of GDP (about 100 billion dollars worldwide) and, therefore, to increase global wealth by around that same percentage in a couple of decades.
One thing is clear. Today, together or separately, both artificial intelligence models accelerate the evolution of processes and operations in companies and institutions in all sectors of activity, especially those with a relevant sustainable development component: they can act on environmental protection, knowledge generation, the fight against disinformation or closing the digital divide.
Beyond generative AI, it is essential to recognize the role played by traditional intelligence, capable of creating computer systems that learn and adapt through experience, using algorithms and mathematical models. “Their evolution continues today. They are no longer simple logic programs, but complex neural networks and deep learning algorithms that continue to generate the largest volumes of activity in this field due to their robustness and precision,” concludes the director of Artificial Intelligence at Minsait.
