AI-generated summary
Artificial intelligence (AI) is already deeply integrated into daily life and professional settings, offering companies the chance to enhance efficiency, automate complex tasks, and improve user experiences. Generative AI tools like ChatGPT are poised to revolutionize work by automating repetitive tasks, aiding data analysis, and streamlining communication. According to LinkedIn, nearly half of U.S. executives foresee significant productivity gains from AI adoption. Economically, Goldman Sachs projects generative AI could boost global GDP growth by up to 7%, driven largely by productivity increases, with high automation potential in administrative and legal roles.
However, challenges remain that temper AI’s rapid adoption. Issues include errors known as “hallucinations,” high implementation costs, and unresolved privacy and copyright concerns. Studies such as one by Boston Consulting Group (BCG) reveal AI excels at tasks like innovation and market analysis but struggles with strategic decision-making, sometimes providing misleading advice. This highlights the necessity of human oversight, especially where creativity and problem-solving are critical. While organizations are optimistic about AI’s potential, many exhibit a paradoxical distrust in areas where AI adds value and overreliance where it falters. Practical applications of generative AI span diverse fields from healthcare to marketing, with companies adopting strategies ranging from incremental improvements to full-scale transformation. Ultimately, success hinges on human-machine collaboration, adaptability, and leadership in navigating this evolving technological landscape.
The trends and real functionalities of generative AI applicable to organizations.
The widespread impact and advantages of artificial intelligence (AI) in our daily lives and professional environments is not just a shadow on the horizon, but an already present reality. Companies, meanwhile, are increasingly recognizing the transformative potential of these technologies to improve their efficiency, automate complex processes, and deliver more advanced user experiences. However, all that glitters is not gold, or at least it is not yet. As with other technologies, we must also understand when and how to use it to enjoy the advantages and avoid the disadvantages.
Generative AIs, such as Chat GPT, are set to revolutionize the way we work. According to LinkedIn’s ‘Future of Work Report: AI at Work‘, 47% of U.S. executives believe that introducing AI into processes will significantly increase productivity due to its ability to automate repetitive tasks, support data research and analysis, and improve the efficiency of communications and information processing.
From the point of view of the effects on global economic growth, Goldman Sachs estimates that generative AI could produce growth of up to 7% of annual global GDP, almost all of which is driven by increased productivity. The investment bank has predicted that in the United States a quarter of current work tasks could be automated, with especially high percentages in administrative (46%) and legal (44%) professions, as opposed to those of high physical intensity such as construction (6%) and maintenance (4%).
It is very likely that generative AI and Large Language Models (LLMs) will revolutionize the world in the medium and long term in the same way that the personal computer or smartphone did. However, a number of problems remain, ranging from so-called ‘hallucinations’, i.e. serious errors in Chat GPT responses and similar solutions, to high implementation costs, to unresolved issues related to privacy and copyright protection, slowing down the adoption of the technology.
Intrinsic limitations should also be noted, at least at this level of development. A study conducted by the Boston Consulting Group (BGC) examined the use of GPT-4 in real-world work environments, involving consultants from various companies in a number of tasks. Technology significantly improved the quantity, speed and quality of the work carried out by consultants, especially in productive innovation and market analysis tasks.
Instead, when asked to make strategic recommendations based on financial data and interviews, the AI provided incorrect advice, misleading people, who then performed worse than those who didn’t use the AI. Where humans excel (problem-solving and creativity), the BGC notes, it would be better to leave them in charge. In addition, the errors of humans are different from those of machines, which can be stranger and more unpredictable and therefore more difficult to verify, such as inventing studies or the jurisprudence of a court case.
These ups and downs, which limit the applicability of AI, are also reflected in the doubts that arose from the experience of the first companies that adopted these tools. The balance is positive, but not entirely. “Our findings describe a paradox: people seem to distrust technology in areas where it can bring enormous value and rely too much on it in areas where it is not competent,” writes the BCG.
That said, there are already many practical applications of generative AI. Cem Dilmegani, founder of AIMultiple, has tried to list them. They are divided into two macro categories: the general ones referring to the field of visual, audio, textual, code-based applications, etc. And specific applications ranging from healthcare to education, from marketing to customer service, from search engine optimization to human resources. It is a default list, constantly evolving.
For its part, McKinsey & Company, in a document entitled ‘What every CEO should know about generative AI‘, cites four areas where this technology can already make a difference in the business environment: software engineering, information management, automation of customer service, acceleration of the drug discovery phase.
Some organizations may decide that generative AI represents an opportunity to reinvent their entire structure, from R+D to marketing and sales and customer management. Others will prefer to opt for a series of incremental changes, starting with small innovations and then scaling them up on a larger scale. What is clear is that generative AI is an evolution that requires adaptability, foresight and, above all, synergy between humans and machines. The real winners will be those who not only adapt, but also lead this transformation.
