LONDON--(BUSINESS WIRE)--Technavio analysts forecast the global artificial intelligence in energy market to grow at a CAGR of over 49%, according to their latest market research report. However, the market is expected to decelerate on a year-on-year basis, during the forecast period 2019-2023.
Increasing adoption of cloud-based solutions is one of the major trends being witnessed in the global artificial intelligence in energy market 2019-2023. With the increasing applications of robotics in repetitive and risky tasks, end-users are increasingly seeking avenues to ensure the elimination of limitations of industrial automation and robotics technologies. These limitations arise due to factors such as cost, computational capacity, storage, size, power supply, motion mode, and working environment. Thus, the adoption of cloud-based Al solutions is increasing in the energy sector to enhance the capabilities of existing systems. The emergence of AI-as-a-service (AIaaS) is trending as it allows individuals and companies to access AI for various applications without large initial investment and with lower risk of failure. AIaaS can allow energy companies to experiment on samples of multiple public cloud platforms to test various machine learning algorithms. Companies such as Alphabet, IBM, and GENERAL ELECTRIC are investing heavily in the development of prediction and maintenance systems for energy industry and are planning to deploy these systems on the cloud soon.
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According to Technavio analysts, one of the key factors contributing to the growth of the global artificial intelligence in energy market is the growing demand for data integration and visual analytics:
Global artificial intelligence in energy market: Growing demand for data integration and visual analytics
Rising proliferation and complexity have made the process of deploying and maintaining reliable data interfaces difficult. Therefore, enterprises around the world are adopting data integration solutions. AI allows real-time synthesizing of data to facilitate real-time analysis for effective decision-making, thus enabling enterprises to monitor, transform, and deliver data; understand business processes; and bridge the gap between businesses and IT. Similarly, AI helps energy companies integrate technical and business process data from different sources and convert it into meaningful business insights. With the exponential increase in data volume, the need for analyzing, transforming, monitoring, and interpreting data has become a priority for business operations.
With globalization, customers, suppliers, and companies are scattered across the world and require real-time information exchange. To accomplish this, energy companies require AI platforms to link multiple enterprise systems with the web and cloud-based applications. Enterprises are also integrating business analytics software with their businesses for the dynamic representation of data. Hence, the demand for AI in the energy sector is likely to increase significantly during the forecast period.
According to a senior analyst at Technavio for research on enterprise application, “Apart from rising demand for data integration and visual analytics, the need for smart homes and development of smart cities and increase in number of smart connected devices is boosting the growth of the global artificial intelligence in energy market.”
Global artificial intelligence market: Segmentation analysis
The global artificial intelligence market research report provides market segmentation by solution (software, hardware, services), by end-user (hospitals and clinics, ASCs, dialysis centers, and others), and by region (the Americas, EMEA, and APAC). It provides an in-depth analysis of the prominent factors influencing the market, including drivers, opportunities, trends, and industry-specific challenges.
The Americas held the largest share of the market in 2018, accounting for over 49% share, followed by EMEA and APAC respectively. Although APAC held the smallest share of the market, it is expected to witness the maximum increase in its market share over the forecast period.
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