It is not recent that Machine Learning (ML) has been prominent as one of the most transformative technologies in the corporate environment. The learning and adaptation capacity of machines, based on new data, has been revolutionising business predictability. With that, companies can adjust their operations and strategies in real time, reducing risks. The impact of this advance goes beyond simple automation; he is redefining how organizations interact with consumers, they optimize processes and identify new growth opportunities
One of the main advantages of machine learning is the ability to analyse large volumes of data and identify patterns accurately. In the current scenario, onde a alta competitividade e as tendências de mercado mudam rapidamente, keep insights up to date on consumer behaviour, the competitive dynamics and global trends are essential factors. Companies that dominate the use of this data gain an edge over the competition, because they can forecast demands, identify operational bottlenecks and respond swiftly to market fluctuations. It was like that before. From now on, will be even more
The integration of Machine Learning with Artificial Intelligence (AI) offers numerous opportunities for personalization and continuous innovation. This is particularly important in critical areas, como previsão de demanda e gestão da cadeia de abastecimento, in quais pequenos erros podem resultar em grandes prejuízos financeiros. The algorithms are more sophisticated, making machines more autonomous, efficient and capable of making complex decisions with minimal human intervention
The significant change that Machine Learning fosters in different sectors of the economy also directly impacts the financial performance of companies, que observam uma diminuição dos riscos de fraudes e um aumento na capacidade de operar em grande escala. It is a misconception to think that this advantage is exclusive to financial institutions. With technological support, retailers, industrias y servicios están creando cada vez más activos de seguridad y eficiencia, leaving competitors unprepared many miles away
One of the challenges for the widespread adoption of machine learning, however, the need for investments in infrastructure and training. As was to be expected, companies need well-structured data pipelines and qualified teams to program algorithms and interpret results. Furthermore, it is crucial to ensure data quality and avoid biases that could compromise the accuracy of models
Despite the financial barrier, a report of theFortune Business Insightsdemonstrates that the market is already organizing itself for this technological update.According to the study, globally, the recipes related to Machine Learning, which in 2022 hovered around US$ 19,20 mil milhões, should reach US$ 225,91 billion by 2030, with an annual growth rate close to 36,2%. That is to say, the companies that do not update themselves will face many difficulties in remaining competitive.
Machine Learning é um fator decisivo para a sobrevivência de muitos negócios. To be at the forefront of this transformation, organisations need to adopt a strategic approach, focused on real-time data collection and processing and the qualification of specialized talents. Those who overcome these challenges will be better qualified to stay ahead of the market, automating complex decisions and driving innovation
