The standard application of AI will become ubiquitous. Machines will begin to understand the associated factors and generate value for themselves. This new AI integration requires action from certain industries.
In 2020 and in the latest technology developments, global trends in AI will continue to influence metallurgy as well as the mining, oil and gas industries. Accelerated growth is expected in the AI market in the coming year. In fact, there is a great opportunity to reduce costs and optimize the industrial process. AI market growth forecasts range from a cumulative annual growth rate (CAGR) of 31% to 54% for the following years.
The global AI market
To date, the global AI market volume represents $15 billion, but growth forecasts show that it will quadruple to reach $72.5 billion by 2025. The digitization of industry sectors will continue and various technologies of the future will be transformed into goods. The standard application of artificial intelligence will become ubiquitous. Machines and systems will begin to understand the associated factors and generate value on their own. This new AI integration covers all sectors and requires action by certain industries. This is the only way for them to remain competitive in the future.
Reversal of the trend and the latest developments in job loss technology (in spanish: las últimas novedades en tecnología y la pérdida de trabajo debido a ésta) due to AI development. On the contrary, the development of AI contributes to the creation of new jobs.
In recent years, the development of automation and artificial intelligence has tended to contribute to the increase in unemployment in the labor market. In 2014, Gartner Research consultants predicted that by 2025, intellectual robots will occupy one third of all jobs. They also predicted that in 2018 more than three million employees would be working under the supervision of an artificial intelligence system.
Today, the situation has changed and it is expected that the development of AI will make it possible to achieve a positive balance between jobs lost and jobs created. This unanimous opinion is shared by the experts of the World Economic Forum, as well as those of the McKinsey and Accenture companies. Indeed, the future of the labor market is still very uncertain. However, new skills will be required to remain competitive in the future.
The skills needed to install new intelligent robots on production lines are often lacking in most companies. Industrial facilities and factories lack the time and robotics specialists to modernize the current production process. Their inability to use the latest robotic technologies lags behind AI trends. They lack experience in the integration and implementation of advanced artificial intelligence systems. Therefore, the factor that delays AI automation is, above all, the qualification of the workers.
In the past, the oil and gas industry ignored any digitization process, and now industrial sites face the need to quickly move to new standards. In the coming years, this sector should use more digital technologies and artificial intelligence systems. The main use of innovations and new technologies is to reduce costs. These investments should be focused on worker training and knowledge accumulation.
Industry sectors refused to invest, despite the first successful introductions of AI
In view of the huge technical debts affecting heavy industry, the major players have invested heavily in introducing the most advanced AI technologies. Successful start-ups in the mining, metals, oil and gas sectors have demonstrated the true potential of AI. For example, Big River Steel in the United States used AI to increase profits in the metals sector. To reduce costs, the company relied on demand forecasting, optimized supply management and material and technical resources, as well as optimized production. Renard's diamond quarry in Quebec has developed an intelligent system for sorting and recycling waste that improves quality and quantity during the diamond mining process. Overall cost savings through the introduction of AI at an early stage, savings confirmed by the industrial sectors, have demonstrated their potential. Despite these promising examples of use, most decision makers still refuse to make investments. We are not witnessing the democratization of AI and its rapid introduction in heavy industry.
Industrial companies work closely with technology giants to introduce artificial intelligence solutions.
Due to their lack of qualification, industrial companies seek to collaborate with leaders in the Information Technology sector. Nearly 40 of the largest oil and gas companies have used Microsoft Azure cloud computing to promote AI-related projects. Large energy companies are seeking help from technology companies to meet their AI needs. Exxon Mobil asked IBM to develop a more realistic model of artificial intelligence. BP uses Amazon AWS for its business management system, to increase system response time by 40%. Schneider Electric uses self-learning resources to remotely manage pumps in the oil and gas sector with Microsoft. Total oïl concluded a contract with Google Cloud to create an earth structure data analysis system to improve exploration and extraction processes. Royal Dutch Shell uses artificial intelligence for its unmanned vehicles and robotics. Shell used Microsoft to develop artificial intelligence and machine learning during exploration, mining, processing and marketing to improve its operational performance.
The main objective is to save costs.
The main objective of artificial intelligence is cost savings, especially seen under this prerequisite. According to McKinsey's forecasts, over the next decade the economy in the oil and gas sector will grow to $50 billion. Machine learning and AI applications create the prerequisites for this prediction.
For the oil and gas sectors, the low prices in 2019 are a driving force. Therefore, reducing production costs will provide any company with a competitive advantage. Preventing blowouts and reducing drilling costs will allow them to obtain a cheaper barrel of oil. Technical maintenance and early detection of pump failures will help avoid equipment downtime that can last several weeks. The cost of repairing incidental failures is usually in the millions of dollars. All of this can be verified if we look for a page where all the technology news (noticias de tecnología) can be found.