Paula Garnero explains the difference between generative and analytical AI and why data is key to unlocking its potential in industry.
In recent years, generative artificial intelligence has brought this technology within reach of anyone with an internet-connected device. What was once exclusive to engineers and large corporations is now available through simple, accessible interfaces capable of generating texts, images, analyses, and solutions in seconds.
This leap has not only democratized the use of AI, but also triggered a profound cultural shift: more and more people, from freelancers to companies, are discovering that they can enhance their creativity, optimize tasks, and explore new ways of generating value.
However, when it comes to competitiveness, generative AI is not an immediate factor that improves a company’s market position. Instead, it acts as a first cultural and technological stepping stone. Bringing it into work teams sparks interest and curiosity, but the real productivity gains come from other applications: predictive AI, analytics, and automation. These technologies require investment, data, and leadership, but they are the ones that can truly transform processes, optimize resources, and open new business opportunities.
On this matter, Paula Garnero, co-founder of Insight-Lac, explained on Ser Industria Radio that the rise of generative AI has brought renewed attention to other forms of artificial intelligence—more analytical, predictive, and automation-based—which tend to be more valuable for industrial productivity.
However, she clarified: “The first thing a company needs to do is think about what problem it has, what pain point, what opportunity it wants to seize, and then see whether the solution involves AI or not.”
Differences between generative and analytical AI in SMEs
The specialist in innovation and digital transformation emphasized that, in the case of SMEs, the adoption of generative AI is usually spontaneous. “Workers start using them on their own initiative—tools like ChatGPT, Gemini, Notebook LM, or Claude—because they realize they can speed up part of their work, slightly improve their products, or whatever task they are assigned,” she said.
However, this informal adoption creates productivity gaps between employees and raises challenges regarding data protection and report quality. For Garnero, the solution requires training and clear business decisions, and even the participation of labor unions to ensure a more uniform spread of the technology.
On the other hand, the predictive AI, analytical, and automation-based AI represents a true productivity leap, but it requires investment, leadership, and data management. “To perform quality control through images that identify a defective part and automatically separate it, the company necessarily needs to invest in cameras, sensors, an assembly line that moves the parts from one stage to another, as well as AI software,” she explained.
These projects involve learning and adaptation periods. In Argentine dairy farms using robotics and AI in milking systems, initial productivity may decrease, but later it increases significantly, going from 33 to 42 liters per cow per day, with more daily milking sessions.
Financing and adoption barriers
When it comes to investing in AI implementation in industry, costs vary depending on the type of technology. Software- and data-based solutions are generally more affordable, while robotics is more expensive. Garnero explains that financing does exist, mainly through private banks, but high interest rates and market contraction make investment decisions difficult.
“Credit is available, but rates are still high, and the problem of non-investment is more about demand than business decision-making. Entrepreneurs would like to invest, but in many cases the numbers simply don’t add up,” the economist noted.
A critical factor in using AI to improve competitiveness is data culture. “In Argentina we are systematically wasting data; especially in SMEs, there is no data culture,” she warned.
For this reason, Garnero stressed that “business opportunities in the coming years are precisely about putting data to work. That means applying data-based AI technologies that improve efficiency, productivity, and more.”
Generative AI can act as a cultural catalyst, bringing technology closer to employees and raising awareness of the importance of data. However, “it does not necessarily add value to company data, because generative AI requires interaction with a person. But what it does generate is a cultural shift,” she said.
The challenge of adopting AI
The industrial adoption of AI differs significantly between large companies and SMEs. In large firms, “there is a strong business decision that says we need to start using AI,” Garnero noted. Some companies have even stated in their HR departments that they will no longer hire for tasks that can be replaced by AI. In contrast, in small and medium-sized enterprises, adoption tends to flow from employees upward.
In this technological transition, not everything depends on business willingness or market progress. Creating a supportive environment—with active public policies, accessible financing, and specialized support—is essential for AI to move from curiosity to strategic tool.
“There is a lot to be done. From a public policy perspective, there are many areas for improvement. Providing people who can guide SMEs through this process is very important. And of course, bringing back cheap, well-targeted, and accessible credit is essential, as is management training,” Garnero said.
In her view, generational change is key to technology adoption, since in the SME world, “these ideas and needs are better received when younger people are in charge of the company. These technologies are changing radically how companies are organized, how work is done, and how tasks are assigned. If we are not open to change, it will be very difficult for AI to enter companies,” she concluded.