AI는 제품 개발 주기를 단축하고 제조업의 혁신을 촉진하고 있습니다. 따라서 제품 개발 및 혁신의 가속화로 시장 규모는 2024년 23억 1,000만 달러를 돌파하고 2031년에는 359억 달러에 달할 것으로 예상됩니다.
AI는 보다 정확하고 효율적인 결함 감지를 가능하게 함으로써 제조업의 품질 관리에 혁명을 일으키고 있습니다. 따라서 품질 관리 프로세스 강화로 인해 시장은 2024년부터 2031년까지 연평균 복합 성장률(CAGR) 47.80%로 성장할 것으로 예상됩니다.
제조업 인공지능(AI) 시장 정의/개요
인공지능(AI)은 첨단 알고리즘과 머신러닝을 활용하여 효율성, 생산 및 의사결정을 향상시킴으로써 제조업을 변화시키고 있습니다. 신경망, 컴퓨터 비전, 로봇 공학 등의 기술은 예측 유지보수, 품질 관리, 공급망 최적화 등 인간의 지능을 모방한 작업을 기계가 수행할 수 있도록 합니다.
제조업에서 AI는 장비의 고장을 미리 예측하여 가동 중지 시간과 비용을 줄이고 전반적인 업무 효율성을 향상시킵니다. 머신러닝 모델은 결함을 감지하여 품질 관리를 보장하고, 로봇은 정확성과 일관성이 요구되는 정밀한 반복 작업에 도입됩니다. 또한 AI 기반 시스템은 수요 예측, 재고 관리 및 물류 간소화를 통해 공급망 관리를 최적화하여 낭비를 줄이고 효율성을 높입니다.
AI가 계속 발전함에 따라 인간의 개입을 최소화하는 자율적인 공장의 개발이 촉진될 것으로 보입니다. 사물인터넷(IoT)의 통합으로 촉진되는 실시간 데이터 수집 및 분석을 통해 제조업체는 보다 유연하고 신속하게 운영할 수 있습니다. 또한 민첩한 생산을 위한 고도의 맞춤화를 지원하여 기업이 변화하는 시장 수요에 빠르게 적응할 수 있도록 지원합니다. 궁극적으로 AI는 제조업의 혁신, 지속가능성, 회복력을 촉진하여 보다 효율적이고 적응력이 높은 생산 시스템을 구축할 수 있습니다.
머신러닝, 컴퓨터 비전, 빅데이터 분석과 같은 AI 기술은 제조업에서 빠르게 확산되고 있습니다. 이러한 기술은 실시간 데이터 처리 및 분석을 제공하여 더 나은 의사 결정, 최적화 된 운영 및 더 높은 제품 품질을 제공합니다. 맥킨지 월드 인스티튜트(McKinsey World Institute)의 보고서에 따르면, AI는 제조업과 공급망 계획 분야에서 1조 2,000억 달러에서 2조 달러의 가치를 창출할 수 있다고 합니다. 세계경제포럼(WEF)은 2025년까지 인간, 기계, 알고리즘의 분업으로 인해 9,700만 개의 새로운 일자리가 창출될 것으로 예측했습니다.
AI를 활용한 예지보전은 제조업에서 다운타임과 유지보수 비용을 줄이기 위해 필수적인 요소로 자리 잡고 있습니다. 미국 에너지부의 보고에 따르면, 예지보전을 통해 유지보수 비용을 30% 절감하고, 고장을 70% 제거하며, 다운타임을 40% 줄일 수 있다고 합니다. 미국 품질학회의 조사에 따르면, 품질 관리에 AI를 도입하면 불량률을 최대 50%까지 줄일 수 있다고 합니다. Capgemini Research Institute에 따르면, 유럽 제조업의 51%가 AI를 활용한 품질관리 솔루션을 도입했으며, 이 중 28%는 생산성을 30% 향상시켰습니다고 보고했습니다.
AI는 예측 정확도와 운영 효율성을 향상시켜 공급망 관리를 변화시키고 있으며, IBM의 조사에 따르면 공급망 리더의 85%는 AI가 향후 3-5년 내에 공급망 성과에 큰 영향을 미칠 것으로 예상하고 있습니다. 가트너에 따르면 2024년까지 공급망 조직의 50%가 인공지능과 고급 분석 기능을 지원하는 용도에 투자할 것이며, PwC의 조사에 따르면 현재 제조업체의 35%는 제품 혁신에 AI를 활용하고 있고, 42%는 곧 AI를 활용할 계획이라고 합니다. 사용할 예정이라고 합니다. 세계지적재산권기구(WIPO)에 따르면 2010년부터 2020년까지 AI 관련 특허 출원 건수가 400% 이상 증가해 이 분야의 빠른 기술 혁신이 이루어지고 있으며, AI는 제조 공정을 보다 에너지 효율적이고 지속가능하게 만드는 데 중요한 역할을 하고 있습니다. 미국 에너지부 보고서에 따르면, AI 기반 시스템은 제조 공장의 에너지 소비를 최대 20%까지 줄일 수 있다고 합니다.
큰 걸림돌은 AI와 제조업 모두에서 필요한 전문 지식을 갖춘 인재가 부족하다는 점입니다. 이러한 기술 격차는 제조 분야에서 AI의 성장과 구현을 가로막고 있습니다. 세계경제포럼(WEF)의 '고용의 미래 보고서 2020'에 따르면, 기술 도입이 진행됨에 따라 2025년까지 전체 직원의 50%가 재교육이 필요하며, 데이터 분석가, 과학자, AI 및 머신러닝 전문가가 새로운 직업군으로 부상할 것으로 예상됩니다. 시스템 통합에 따른 막대한 초기 비용은 특히 중소기업(SME)에 큰 장벽이 될 수 있습니다. 정보 기술 혁신 재단(ITIF)의 보고서에 따르면 산업용 로봇의 평균 비용은 약 2만 7,000달러이며, 소프트웨어, 통합 및 유지보수 비용이 추가로 소요됩니다.
AI 시스템은 데이터에 크게 의존하기 때문에 데이터 보안, 프라이버시, 지적재산권 보호에 대한 우려로 인해 일부 제조업체가 AI 기술을 전면적으로 채택하는 것을 억제하고 있습니다. 미국 국립표준기술연구소(NIST)의 보고서에 따르면, 제조업은 전체 사이버 공격의 23.2%를 차지하는 두 번째로 많은 사이버 공격의 표적이 되는 산업입니다.
AI is speeding up product development cycles and fostering innovation in manufacturing. Thus, the acceleration of product development and innovation surged the growth of market size surpassing USD 2.31 Billion in 2024 to reach the valuation of USD 35.9 Billion by 2031.
AI is revolutionizing quality control in manufacturing by enabling more accurate and efficient defect detection. Thus, the enhancement of quality control processes enables the market to grow at a CAGR of 47.80% from 2024 to 2031.
Artificial Intelligence in Manufacturing Market: Definition/ Overview
Artificial Intelligence (AI) is transforming manufacturing by leveraging advanced algorithms and machine learning to enhance efficiency, production, and decision-making. Technologies such as neural networks, computer vision, and robotics empower machines to perform tasks that mimic human intelligence, including predictive maintenance, quality control, and supply chain optimization.
In manufacturing, AI helps reduce downtime and costs by predicting equipment failures before they occur, improving overall operational efficiency. Machine learning models can detect defects and ensure quality control, while robots are deployed for precise, repetitive tasks that require accuracy and consistency. AI-driven systems also optimize supply chain management by forecasting demand, managing inventory, and streamlining logistics, leading to reduced waste and enhanced efficiency.
As AI continues to evolve, it will drive the development of more autonomous factories with minimal human intervention. Real-time data collection and analysis, facilitated by Internet of Things (IoT) integration, will enable manufacturers to operate more flexibly and responsively. This will also support advanced customization for agile production, allowing companies to quickly adapt to changing market demands. Ultimately, AI will foster innovation, sustainability, and resilience in manufacturing, leading to more efficient, adaptable production systems.
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AI technologies such as machine learning, computer vision, and big data analytics are rapidly gaining traction in manufacturing. These technologies offer real-time data processing and analysis, resulting in better decision-making, optimized operations, and higher product quality. According to a McKinsey Global Institute report, AI has the potential to create between USD 1.2 Trillion and USD 2 Trillion in value in the manufacturing and supply chain planning sectors. The World Economic Forum predicts that by 2025, 97 million new jobs may emerge in the division of labor between humans, machines, and algorithms.
AI-powered predictive maintenance is becoming crucial in manufacturing to reduce downtime and maintenance costs. The U.S. Department of Energy reports that predictive maintenance can reduce maintenance costs by 30%, eliminate breakdowns by 70%, and reduce downtime by 40%. A study by the American Society for Quality found that implementing AI in quality control can reduce defect rates by up to 50%. According to Capgemini Research Institute, 51% of European manufacturers are implementing AI-powered quality control solutions, with 28% of them reporting a 30% increase in productivity.
AI is transforming supply chain management by improving forecasting accuracy and operational efficiency. A study by IBM found that 85% of supply chain leaders believe AI will significantly impact their supply chain performance in the next three to five years. According to Gartner, by 2024, 50% of supply chain organizations will invest in applications that support artificial intelligence and advanced analytics capabilities. A PwC study found that 35% of manufacturers are currently using AI to innovate products, with an additional 42% planning to do so shortly. According to the World Intellectual Property Organization (WIPO), AI-related patent applications increased by more than 400% from 2010 to 2020, indicating rapid innovation in the field. AI is playing a crucial role in making manufacturing processes more energy-efficient and sustainable. The U.S. Department of Energy reports that AI-powered systems can reduce energy consumption in manufacturing plants by up to 20%.
The significant restraint is the shortage of personnel with the necessary expertise in both AI and manufacturing. This skills gap is hampering the growth and implementation of AI in the manufacturing sector. The World Economic Forum's "Future of Jobs Report 2020" found that 50% of all employees will need reskilling by 2025 as the adoption of technology increases, with data analysts and scientists, AI and machine learning specialists among the top emerging jobs. The substantial upfront costs associated with AI technologies and their integration into existing manufacturing systems pose a significant barrier, especially for small and medium-sized enterprises (SMEs). A report by the Information Technology and Innovation Foundation (ITIF) states that the average cost of an industrial robot is around $27,000, with additional costs for software, integration, and maintenance.
As AI systems rely heavily on data, concerns about data security, privacy, and intellectual property protection are restraining some manufacturers from fully embracing AI technologies. The U.S. National Institute of Standards and Technology (NIST) reported that manufacturing is the second most targeted industry for cyber-attacks, accounting for 23.2% of all incidents.
The computer vision segment is poised for significant growth in artificial intelligence in the manufacturing market, driven by its ability to provide accurate and actionable insights for various manufacturing processes. The increasing demand for advanced automation and efficiency in manufacturing. Computer vision's integration with robotics plays a crucial role in process optimization, as it enables robots to "see" and interpret their environment, making production more efficient and precise.
In addition, the growing adoption of robotics across multiple industries, including automotive, electronics, and consumer goods, has further fueled the application of computer vision for process improvement and quality control. As industries continue to embrace automation and intelligent systems, computer vision is expected to play an increasingly vital role in driving efficiency, safety, and optimization within manufacturing environments.
The medical devices segment is emerging as a dominant segment in the artificial intelligence (AI) manufacturing market, driven by the rising prevalence of diseases globally and the growing need for advanced medical equipment. As healthcare systems expand and modernize, there is increasing demand for innovative, efficient, and reliable medical devices that can enhance patient outcomes and streamline medical processes. AI plays a pivotal role in this transformation, offering opportunities to manufacture cutting-edge devices that disrupt traditional methods and improve diagnostic and treatment capabilities.
AI integration in the manufacturing of medical equipment allows for the development of smarter, more precise devices that can operate with greater efficiency. From surgical robots to AI-driven diagnostic tools, these advancements are enabling manufacturers to create equipment that delivers real-time insights and enhances patient care. One notable example is Australia-based EMVision, which has harnessed NVIDIA's AI platform and DGX systems to develop a lightweight, portable brain scanner. This AI-powered device can diagnose brain strokes within minutes, revolutionizing stroke care by providing quick, accurate diagnoses in emergencies.
North America substantially dominates artificial intelligence in the manufacturing market owing to the strong presence of tech giants and AI startups. North America, particularly the United States, is home to many of the world's leading tech companies and AI startups, driving innovation and adoption in AI manufacturing solutions. According to the National Science Foundation, the United States leads the world in AI research output, producing 27% of all AI research papers globally in 2020. A report by the Center for Data Innovation shows that the US has 1,393 AI companies, compared to 736 in China and 521 in the EU.
Both the U.S. and Canadian governments are making significant investments in AI research and development, as well as in modernizing the manufacturing sector. The U.S. National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) announced over USD 201 Million in funding for artificial intelligence research institutes in 2021.
According to the U.S. Government Accountability Office, federal agencies obligated USD 1.5 Billion in AI-related research and development spending in fiscal year 2020. North American manufacturers are increasingly embracing Industry 4.0 technologies, including AI, to improve efficiency and competitiveness. A survey by the National Association of Manufacturers found that 77% of manufacturers say increasing productivity is the top reason to adopt new technologies, including AI.
Asia Pacific is anticipated to witness the fastest growth in artificial intelligence in the manufacturing market. The Asia Pacific region is experiencing a swift transition towards digitization and Industry 4.0, driving the adoption of AI in manufacturing. According to a report by McKinsey, Asia could account for 40% of the world's total Industry 4.0 market by 2030. The Asian Development Bank Institute states that the digital economy in Asia Pacific is expected to reach USD 1.7 Trillion by 2025, up from USD 1.35 Trillion in 2019. Many countries in the Asia Pacific region have launched national AI strategies and are heavily investing in smart manufacturing initiatives. China's State Council announced plans to build a USD 150 Billion AI industry by 2030. According to the International Federation of Robotics, five major Asian markets China, Japan, South Korea, Taiwan, and India accounted for 74% of global industrial robot installations in 2020.
The Asia Pacific region's significant manufacturing base, coupled with rising labor costs, is driving the adoption of AI to improve efficiency and reduce expenses. The United Nations Conference on Trade and Development (UNCTAD) reports that Asia's share of global manufacturing output increased from 31.6% in 1990 to 51.1% in 2018. According to the International Labour Organization, average wages in Asia and the Pacific grew by 3.5% in 2019, the highest among all regions globally.
The competitive landscape of the Artificial Intelligence in Manufacturing Market is dynamic and evolving, with a growing number of players vying for market share. The ability to develop and deliver innovative AI solutions that address the specific needs of manufacturing customers will be critical for success in this competitive market.
The organizations are focusing on innovating their product line to serve the vast population in diverse regions. Some of the prominent players operating in the artificial intelligence in the manufacturing market include: