The AI market is expected to be worth $407 billion by 2027, research says
Artificial intelligence is not a current term, but it has become very relevant in the last decade due to the rapid advancement of technology in the routine of people and companies. When you hear about this topic, it may sound distant or futuristic, but smart TVs, cell phones with facial recognition, virtual assistants, and self-driving cars are some of the current technologies created from AI.
Artificial intelligence is an interdisciplinary science with several approaches, where the goal is to build a computer system that is capable of simulating human behavior, allowing the use of similar “thinking” processes to solve complex problems. In order to create such technology, it is necessary to program algorithms that process large amounts of data, and then learn, reason and self-improve from them.
Organizations from a wide variety of industries are embracing artificial intelligence to gain a competitive edge, esp Great technicians (Google, Microsoft, Amazon, among others). They used the vast amount of data generated by their users to feed their algorithms, learning the behavior of each audience to develop highly assertive and targeted services.
Artificial intelligence in companies
Currently, artificial intelligence in business is used mainly to increase productivity and improve results. It can be implemented in different sectors of the company to improve performance, eliminate work steps and help manage and monitor operations.
Generally speaking, running an AI project starts with data compilation, data preparation, AI training and finally implementation. The “life cycle” of artificial intelligence includes several stages, and companies that want to invest in this technology must ensure that all of them are fulfilled.
For experts like Andrew Ng, co-founder of Google Brain, AI will only ever be as good as the data that feeds it, and high-quality data preparation represents at least 80% of the AI lifecycle. This is where data engineering comes in, responsible for building a good infrastructure that supports the scalability of Artificial Intelligence.
Data infrastructure refers to all the resources involved in collecting, storing, maintaining and sharing data between companies, including hardware, software, networking, internal policies and more. Also, according to MarketsandMarkets research, the AI segment that tends to grow the most through 2027 is cloud infrastructure servers, as it offers greater operational flexibility and facilitates real-time analysis.
For Daniel Luz, COO of beAnalytic (a technology consulting firm focused on Data Engineering and Business Intelligence), “it’s very common for companies to start analytics and artificial intelligence without worrying about data infrastructure. As a result, when projects go into production and start to gain scale, the infrastructure becomes a bottleneck. That is the role of data engineering, to ensure the availability and quality of data so that scientists and analysts can act.”
After data preparation comes analysis, training and testing of artificial intelligence, which takes place through algorithms – instructions that will guide the work of artificial intelligence, teaching the machine what to do, when to do it and how to do it -, finally culminating in the implementation of the service.
MarketsandMarkets estimates that artificial intelligence primarily benefits the finance, IT/ITES, telecom, healthcare, manufacturing, retail, e-commerce, government and defense, automotive transport and logistics, energy, travel and education verticals. In Brazil alone, technology consultancy IDC estimates that companies will invest US$504 million (about R$2.61 billion) in artificial intelligence during 2022. As a data-driven technology, AI is considered one of the most promising tools for the business world, able to adapt to constant changes in market trends.