ARTIFICIAL INTELLIGENCE
likely to drive automation needs across the manufacturing sector . AI-based solutions could help manufacturers to digitally transform operations ( such as autonomous material movement using industrial robots ) and make prompt data-driven decisions . In addition , AI can play a crucial role in automating several stages of manufacturing and limit human involvement ,” explained the report .
For developing , managing and implementing AI systems that are complex in nature , companies require a workforce with certain skill sets i . e . personnel dealing with AI systems should be aware of technologies such as cognitive computing ; ML and machine intelligence ; deep learning and image recognition . In addition , the integration of AI solutions into existing systems is a difficult task requiring extensive data processing for replicating human brain behaviour . Even a minor error can result in system failure or can adversely affect the desired result .
“ The absence of professional standards and certifications in AI / ML technologies is restraining the growth of AI . Additionally , AI service providers are facing challenges re deploying / servicing their solutions at their customers ’ sites . This is because of a lack of technology awareness and expertise ,” added the report .
“ The development around AI and analytics has been exciting because , almost across all sectors , it has become one of
the most important technologies adopted as organisations are no longer using the traditional ways of doing things , as they did in the pre-COVID days . Therefore , the willingness to adopt new AI technologies / solutions and further investment in existing AI technologies / solutions has been the most significant outcome of the COVID-19 pandemic . Many organisations across multiple industries are increasingly implementing AI
KUKA robots in a smart cell .
AI technologies offer manufacturers the tools that would help them in better predictive maintenance and machinery inspection processes
technologies to uncover new efficiencies and , in some cases , to enable the mere survival of some businesses ,” explained Joanne Peplow , Artificial Intelligence ( AI ) Expert , CapGemini .
“ There are many lessons to learn from the events of COVID-19 . Perhaps , one of the most critical lessons is the importance of being able to use data to prepare for potential uses to enable informed decision-making . To remain relevant , most businesses are critically looking for tech talent and skills that can help them adapt to the new normal . For that , AI and machine learning have turned out to be the critical skills . Data management has become paramount , and therefore the requirement for people with skills in data collection and data governance is now increasing more than ever . These professionals are involved with understanding data sets and data sources and keep the quality of data in a way that it is ready for use ,” she added .
She also highlighted a key concern with deploying data-driven models – the question of data sources and quality . Ensuring the transparency and ethical practices of AI-driven systems is more critical than ever .
“ In short , coronavirus has supercharged the already fast-moving debate on ethical frameworks for AI ,” concluded Joanne Peplow .
Growth sectors
Markets and Markets expects AI in the hardware segment of the manufacturing market to dominate in the APAC region . Most of the AI hardware manufacturers have been providing the same hardware components for other technologies such as connected cars , machine vision cameras and IoT . This will enable them to transfer the technology easily and accordingly develop AI hardware . The increasing participation of start-ups in AI hardware is complementing the growth of the hardware segment . A large number of manufacturing plants , the huge presence of hardware manufacturers and high adoption rates of robotics in China , Japan and South Korea are driving the growth of AI in the manufacturing market in the APAC region , according to the report .
The analyst also expects predictive maintenance and machinery inspection applications to hold the largest share of AI in the manufacturing market from 2020- 2026 . The extensive use of computer vision cameras in machinery inspection , the adoption of Industrial Internet of Things ( IIoT ) and the use of Big Data in the manufacturing industry are factors driving the growth of AI in manufacturing for predictive maintenance and machinery inspection applications . The increasing demand for reducing operational costs and machine downtime is also supplementing its growth .
“ The market for deep learning technology is estimated to witness the highest growth throughout the forecast period . The growing adoption of deep learning in various manufacturing applications , the rapid adoption of robotics in the manufacturing industry and the huge volume of data generated by computer vision technology , as well as Big Data , are driving the growth of AI in the manufacturing market for deep learning technology ,” said the report .
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