The Biggest Industrial AI Trends In 2020
In 2020 the global trends in artificial intelligence will continue to affect the sectors of metal, mining, oil, and gas. The AI market is expected to grow at an accelerated speed in this upcoming year. This offers huge opportunities for costs savings and process optimization. The forecasts for the growth rate of AI markets range from a compound annual growth rate (CAGR) of 31% to 54% for the next years.
With a market size of $15 billion today, the global industrial AI market is expected to grow up to four times its size by 2025 and reach $72.5 billion. The digitalization of industries will continue, and the different future technologies will turn into commodities. Standard artificial intelligence application is going to be everywhere. Machines and systems are going to understand the context and generate value by themselves. This new AI integration hits all sectors and requires the individual industries to act. Only in this way can they be competitive in the future.
AI Job Loss Trend Reversed, AI Creates Jobs
With all this automation and intelligent work done by machines, in recent years, the influence on the job market was expected to lead to unemployment. As early as 2014 Gartner Research predicted, a third of the jobs were taken by smart robots by 2025. They estimated that by 2018, more than three million workers will be supervised by artificial intelligence systems.
This all changed, and AI is now expected to create more jobs than it eliminates. This is the general tone among experts from the World Economic Forum, McKinsey and Accenture. And in fact, it is unclear what will happen in the job market. However, new qualifications are needed to be competitive in the future.
The qualifications needed to install new smart robots in production lines are often not available in most companies. Facilities and factories lack free time and robot technicians to update their ongoing production. This leads them to a fall behind AI trends, as they are not capable of using the latest robotics technology. They are missing skills in integration, implementation and debugging artificial intelligence enhanced systems. So, the hindering factor in AI automation is workers’ qualification at the foremost front. Especially the training and customization of neural networks require deep specialists’ knowledge to dig the treasures of AI.
The oil and gas industries have been skipping digitalization in the past, and they are now facing the challenges of rapid adoption. For the next few years, they are expected to incorporate more digital technologies and artificial intelligence in their business. Through innovation and new technology, they aim mainly at costs savings. These investments need to be focused on workers qualification and knowledge building.
Industries Were Reluctant To Invest Despite The Success Of Pioneer Cases Of Applied AI
As technological debts are huge in the heavy industries, the investments made by leading players to adopt state-of-the-art AI technologies are significant. Successful pioneers in mining, metallurgy, and oil & gas industries have shown the potential of AI. For example, Big River Steel, from the US, applied AI to increase their profit in steelmaking. They utilized demand prediction, optimised sourcing and inventory management and optimised production to cut costs. The diamond mine Renard, in Quebec, developed a smart system for waste sorting and disposal which improves the quality and quantity of the diamond recovery process. The general costs saving aspects AI proven by industries with an earlier adoption have shown the potential. Despite these promising use cases, the majority of decision-makers are still reluctant to invest. We do not see the democratisation of AI and rapid adoption in the heavy industries.
Industrial Companies Team Up With Tech Giants To Solve AI
Due to the lack of own expertise, industrial companies are seeking out to IT leaders to cooperate. Around 40 major oil and gas companies were using Microsoft Azure’s cloud computing service to foster AI projects. Energy giants are seeking the help of technology companies to fulfil their AI needs. Exxon Mobil teams up with IBM to development of more realistic simulations with artificial intelligence. BP is utilizing Amazon’s AWS for its ERP system to create 40% faster response times. Schneider Electric is using machine learning capabilities to handle pumps in the oil and gas fields remotely with the help of Microsoft. Total oil started an agreement with Google Cloud, to create a system for the analysis of subsurface data that improves the exploration and production processes. Royal Dutch Shell utilizes artificial intelligence for autonomous vehicles and robotics in its operations. Shell depends on Microsoft for scaling AI and machine learning across its upstream and downstream businesses and improving operational performance.
The Main Goal Is Cost Saving
Driven by the goal to save money, artificial intelligence is evaluated under this prerequisite. McKinsey predicts a $50 billion savings for the oil and gas industry in the coming decade. Machine learning and AI applications provide the potential for this outlook.
For oil and gas, low prices in 2019 are the driving force. So, reducing production costs would give the company a competitive advantage. The prevention of outtakes and lowering of drilling costs will give them an advantage to produce a barrel of oil cheaper. Predictive maintenance and early detection of pump failures can avoid the equipment being out of commission for weeks. Usually, repair costs of unforeseen outtakes could easily create costs in the scale of millions of dollars.
In the mining sector, the digital mine stays a hot topic. With upgrades of the digital infrastructure, new optimization strategies are possible. According to Accenture, 82 percent of executives in the global mining industry are planning for higher investments in digital technologies over the next three years. Part of the smart mine efforts are Intelligent drones and autonomous machines. The harsh environment at mines offers the best opportunities for costs saving through self-controlling machines. These machines could go to many places which humans just can’t physically enter. They are capable to work around the clock and as they are not alive there is no need for immediate action in case of failure or accident.
Automated supply chain activities will be enhanced by AI because they are mainly time consuming and repetitive tasks. Due diligence of suppliers or prequalification could be automated. This leads to the reduction of time and effort by the company’s employees. In addition to that, cognitive procurement consultants, which are able to provide audit summaries and to proof recommendations, could be deployed. This all could happen in real-time and can be combined with all historical data company-wide.
Two of the biggest strategic technology trends for 2020 from Gartner’s report will have a special impact on the industrial sector. Some of the futuristic perspectives seem far away, others are just around the corner. At the moment we have fierce competition in the industrial sector, and every advantage gained has to be analyzed carefully. Ignoring trends could lead to huge disadvantages for the laggard. As training and fostering a culture of artificial intelligence adoption takes time, waiting to take steps in 2020 needs to be carefully assessed.
Hyperautomation Goes Deeper Inside The Guts of the company
As automation is almost a classic, hyperautomation takes the enterprise to the next level of applied artificial intelligence. Hyperautomation covers the detailed automation of processes and the augmentation of humans. In this was human work or human supervision will become obsolete. The automation mantra goes much deeper into details and departments of the company, that were before unthinkable to automate. Further, it fosters the connectivity of tools. These connected tolls could discover problems, analyze their situation, design new solutions, automate their communication, measure precisely, monitor themselves and other machines around them, and reassess their performance.
Transforming Staff Into A Superhuman Workforce
Human augmentation enables employees to see, hear, and feel better. Their performance is increasing as they have access to an extended pool of information. Physical augmentation changes the physical abilities of the employee directly. These enhancements could be worn, hoisted, or implanted of the worker. The new, younger generation will have less prejudice in augmenting their bodies, as they are comfortable with them in their private lives. Today’s consumers are closely connected to their smartphones and environment augmentation like voice assistants are already widespread. More active and augmented surroundings will become common in the upcoming years.
Interesting AI technologies are coming up and they are ready for adoption. They allow the industrial sector to save money and increase productivity. However, the industry is reluctant to invest and is giving a lot of value to established technology companies. Further, there will be space for new industrial tech entrepreneurs. The artificial intelligence market stays open with a lot of underserved areas. In this wide space, newcomers could find their niches.
Industrial companies should not get distracted with the numerous buzzwords and hype created by the AI in IIoT community. The main focus should be on developing basic systems (e.g., network connections) and implementing sensors as it is the first steps before using AI-powered solutions. AI IIoT is an increasing business and there are several implementation methods and technologies, which can make choosing one overwhelming. Reach out to existing vendors for AI IIoT solutions and go down the rabbit hole with a reliable partner.
Ekaterina Lyapina, AI and IIoT Consultant at Zyfra
Zyfra develops turnkey solutions for the mining, oil and gas, machinery and metallurgy industries that drive digital transformation and enhance operational efficiency for businesses across the globe.
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This article was originally published on Forbes Middle East.
