ChatGPT has become a topic of discussion, especially in the business world. Entrepreneurs and professionals from various segments tirelessly debate about how it will impact the labor market and how it can help organizations lower costs thereby increasing profitability.
But the truth is that ChatGPT is only the visible portion of a large and constantly developing field that has been already in operation among us for a long time. If we investigate the history of artificial intelligence as they are known today, we will find their origins in the mid-1950s, when technologies such as the Logic Theorist, developed by Allen Newell and Herbert Simon at RAND Corporation in the United States; and the Perceptron, created by psychologist Frank Rosenblatt in 1957.
While the first reproduced human reasoning and problem-solving, even proving mathematical theorems, the second was a network of artificial neurons capable of learning, being one of the main precursors of Machine Learning, which is now the driving force of mechanisms such as ChatGPT.
ChatGPT is a technology based on natural language processing (NLP), which enables it to understand text in multiple languages and generate natural language responses, without the need for specific programming to perform each task. In other words, it literally speaks our language.
And this is part of the reason it causes such a stir. Besides optimizing Web searches (posing a threat to powerful representatives of Big Techs, such as Google), promotes a conversational experience with the machine, without requiring the user to know any programming language. With the liberated access to its technology, those who are not feeling like the protagonist of a science fiction movie are living in the past.
But countless Ais work daily in other functions. In the financial market, for example, there are systems not only based on NLP but also on machine learning, fraud detection systems, as well as robo-advisors and trading algorithms. The latter two stand out because they act directly on transactions.
While robo-advisors offer automated investment guidance based on information provided by investors (being great allies for beginners in the financial market and for those who don’t have time to monitor the scenario), trading algorithms employ market data obtained in real-time to make critical decisions to buy and sell financial assets. This is possible because they are programmed to identify price patterns and market trends, enabling traders to make data-driven decisions and execute trades with much more agility and confidence.
And those who think this is new are wrong. The use of AIs in the financial market began in the 1970s, with systems like INGRES (Intelligent Graphic Reinvestment System). Developed by the investment company Dean Witter Reynolds (now part of Morgan Stanley, a world leader in financial services) it was a pioneer in the industry. By applying neural networks (in a Perceptron-like fashion), it analyzed transaction data and predicted market trends.
INGRA is no longer in use, but today systems like Sentieo, Kavout, Kensho, and Acorns are some of the AI technologies in application in stock buying and selling and investment advice.
Amidst so many fears about information security (and even a possible machine revolution), it is difficult to predict exactly where these technologies will go and what role they will play in our daily lives soon. However, the market expectation is that their use will become more and more massive, as a tool to boost results and reduce costs in the medium and long term.
According to research by Market Data Forecast, the AI market in the financial sector is expected to grow at a compound annual growth rate of 41.2% between 2020 and 2027, jumping from $6.7 billion to $15.8 billion over the period. This is in line with research by Tractica, whose estimate is that by 2025, AI-mediated e-commerce transactions worldwide will exceed $36 billion.
This growth is a result of the increased efficiency produced by these technologies. Nasdaq itself applies AI algorithms to accelerate and reduce trading costs, taking transactions to a new level.
Of course, such an advance would not be restricted to the financial market. Research indicates that the use of these technologies can also benefit companies and that this is why they will also play a greater role in the corporate environment.
According to Accenture, the AIs applied to business management can reduce costs by up to 30% and increase revenue by up to 38% in 16 different segments, such as Education, Food Service, Hospitality, Healthcare, Wholesale, Retail, and Manufacturing, among others. A true springboard of profitability for organizations that invest in these tools.
And businessmen are already keeping an eye on this trend. From a complementary perspective, data from Forbes indicates that by the end of this year, business process automation with AI systems is expected to grow by 57%.
Looking beyond ChatGPT, it is easy to note that the use of Artificial Intelligence has already become a giant competitive advantage aggregator for businesses across all industries. Thus, it’s up to CEOs and CFOs to be on the lookout for ways to get ahead in this race, investing in solutions that can make their businesses stand out from the competition.
