Supply Chain Ecosystem Analysis now part of DBMR Reports
Market Size in USD Billion
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USD 13.60 Billion |
| USD 41.03 Billion | |
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Global Machine Learning (ML) Intelligent Process Automation Market, By Component (Solutions, Services), Type (Structured, Un Structured), Technology (Natural Language Processing, Machine and Deep Learning, Neural Networks, Virtual Agents, Mini Bots, Computer Vision, Others), Organization Size (Large Enterprises, SMEs), Application (IT Operations, Contact Centre Management, Business Process Automation, Application Management, Content Management, Security Management, Others), Business Function (Information Technology, Finance and Accounts, Human Resources, Operations and Supply Chain), Deployment Mode (On-Premises, Cloud), End User (Banking, Financial Services, Insurance (BFSI), Telecommunications and IT, Transport and Logistics, Media and Entertainment, Retail and E-Commerce, Manufacturing, Healthcare, and Life Sciences, Others) – Industry Trends and Forecast to 2030.
The need to boost business productivity and rising technological adoption across a variety of industry sectors are expected to drive machine learning (ML) intelligent process automation market growth. Intelligent process automation solutions give users cutting-edge tools and adaptable workflows to enable them to make decisions more quickly and with greater knowledge. These solutions manage interfaces and eliminate bottlenecks in workflow systems. It is anticipated that this factor will give the market momentum in the upcoming years.
Data Bridge Market Research analyses that the machine learning (ML) intelligent process automation Market, valued at USD 13.6 billion in 2022, will reach USD 41.03 billion by 2030, growing at a CAGR of 14.80% during the forecast period of 2023 to 2030. In addition to the market insights such as market value, growth rate, market segments, geographical coverage, market players, and market scenario, the market report curated by the Data Bridge Market Research team includes in-depth expert analysis, import/export analysis, pricing analysis, production consumption analysis, and pestle analysis.
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Report Metric
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Details
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Forecast Period
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2023 to 2030
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Base Year
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2022
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Historic Years
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2021 (Customizable to 2015 - 2020)
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Quantitative Units
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Revenue in USD Billion, Volumes in Units, Pricing in USD
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Segments Covered
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Component (Solutions, Services), Type (Structured, Un Structured), Technology (Natural Language Processing, Machine and Deep Learning, Neural Networks, Virtual Agents, Mini Bots, Computer Vision, Others), Organization Size (Large Enterprises, SMEs), Application (IT Operations, Contact Centre Management, Business Process Automation, Application Management, Content Management, Security Management, Others), Business Function (Information Technology, Finance and Accounts, Human Resources, Operations, and Supply Chain), Deployment Mode (On-Premises, Cloud), End User (Banking, Financial Services, Insurance (BFSI), Telecommunications and IT, Transport and Logistics, Media and Entertainment, Retail and E-Commerce, Manufacturing, Healthcare, and Life Sciences, Others)
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Countries Covered
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U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.
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Market Players Covered
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Automation Anywhere, Inc. (U.S.), UiPath (U.S.), Blue Prism Limited (U.K.), Pegasystems Inc. (U.S.), AntWorks (Singapore), NICE (Israel), Kofax Inc. (U.S.), SAP SE (Germany), AutomationEdge (U.S.), Larc AI (Pty) Ltd. (South Africa), Autologyx (U.K.), Sanbot Innovation Technology., Ltd (China), Cinnamon Inc. (Japan), Wipro (India), Xerox Corporation (U.S.), TATA Consultancy Services Limited. (India), IBM (U.S.), Atos SE (France), Capgemini (France), Accenture (Ireland)
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Market Opportunities
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Market Definition
Software applications are now able to make predictions more accurately through ML, a subfield of AI. Machine learning algorithms forecast new output values using historical data as input. Artificial intelligence (AI) technologies are used in cognitive process automation to speed up cognitive processes such as reasoning, machine learning, and natural language processing. Insight to cognitive process automation, these tasks will be finished more quickly and easily by both humans and machines.
Drivers
Businesses use RPA technology to automate manual data entry tasks, doing away with the need for human labor. The IPA workflow combines cognitive learning, RPA, ML, and AI. As a result, as RPA gains popularity, IPA demand increases as well. RPA offers effectiveness and quickness. Artificial intelligence (AI) is added to automation to analyze data in a way that a human could not, recognize patterns in data, and learn from previous decisions to make increasingly wise decisions. IPA reduces the time needed for tasks by doing away with the need for human data entry, information validation, and document sorting thus driving the market growth.
A significant segment that shows growth is design and implementation. Therefore, the growth is attributed to a better understanding of the use of automation solutions to reduce manual labor. As intelligent process automation solutions are more widely used, there is an increase in demand for implementation and training services. The providers focus on providing a customized solution that satisfies business requirements. As a result, it is anticipated that demand for intelligent process automation services will rise over the forecast period.
Machine learning (ML) intelligence sees a rapid expansion of the scope and use of IT and automation globally with a high acceptance rate, minimizing human work and error with optimal resource utilization to earn greater business enterprise efficiency. Automation with artificial intelligence helps in better customer experience and faster decision-making across the organization, which drives market growth.
Opportunities
Businesses are switching towards work-from-home policies, which has a significant impact on the amount invested in operational process automation. Through increased investment in application areas such as telemedicine, predictive maintenance, and virtual healthcare management, the market is growing. The adoption of IPA solutions has increased across most non-IT verticals, thus the market sees an expansion in the machine learning (ML) intelligent process automation market during the aforementioned projected timeframe.
Emerging intelligent automation technologies such as virtual agents and natural language processing, among others, are bringing in opportunities to improve the customer experience, and machine learning solutions are significantly improving efficiency. Systems can automatically learn from experience and get better with machine learning, which eliminates the need for explicit programming, thus advancement in technology can create lucrative opportunities in the market.
Restraints/Challenges
Skilled labor is required to run a newly automated operational model, but it's crucial to find individuals with RPA and AI expertise. Technical proficiency, an understanding of the business procedures of the organization, and the ability to adapt management techniques are all part of machine learning intelligence. Assigning people to ongoing maintenance, support, and troubleshooting is equally important to develop automation, and the absence of such skills can limit growth.
Cybersecurity is one of the most significant concerns in the digital age. Malware and ransomware attacks are becoming increasingly organized forms of cybercrime. Every day, businesses receive an increasing number of security notifications. According to CERT-In, more than 6.07 lakh cybersecurity incidents were reported in the first half of 2021. As a result, using cybersecurity for IPA is necessary to create an efficient security architecture that shields the organization from rising risks. 77% of businesses intend to increase automation in their security ecosystems in the ensuing years, according to a Cisco survey, which restricts the market.
This machine learning (ML) intelligent process automation market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the machine learning (ML) intelligent process automation market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.
Recent Developments
The machine learning (ML) intelligent process automation market is segmented on the basis of components, type, technology, organizational size, application, business function, deployment mode, and end user. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.
Component
Type
Technology
Organization Size
Application
Travel and Hospitality
Business Function
Deployment Mode
End User
The machine learning (ML) intelligent process automation market is analysed and market size insights and trends are provided by components, type, technology, organizational size, application, business function, deployment mode, and end user as referenced above.
The countries covered in the machine learning (ML) intelligent process automation market report are U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.
North America dominates the market and will continue to flourish its trend of dominance during the forecast period. The major factors attributable to the region’s dominance are Along with the prevalence of various market players in the area, process management and automation solutions are becoming more widely adopted across the enterprise. Additionally, regional growth is being fueled by the growing enterprise adoption of process management and automation solutions in the United States. The primary drivers of the segmental growth are increased spending on business operations optimization and widespread adoption of cutting-edge technologies such as AI, machine learning, and RPA.
Asia-Pacific will undergo the highest growth rate during the forecast period owing to the region's adoption of cloud-based technologies and growing awareness of automation, machine learning, and artificial intelligence. The demand for intelligent process automation solutions and services is also being further boosted by growing awareness of automation, machine learning, and artificial intelligence. The major drivers of the regional market growth are globalization, economic development, digitalization, and increased adoption of cloud-based technologies.
The country section of the report also provides individual market impacting factors and changes in regulation in the market domestically that impacts the current and future trends of the market. Data points such as down-stream and upstream value chain analysis, technical trends and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of global brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.
The machine learning (ML) intelligent process automation market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to machine learning (ML) intelligent process automation market.
Some of the major players operating in the machine learning (ML) intelligent process automation market are:
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Data collection and base year analysis are done using data collection modules with large sample sizes. The stage includes obtaining market information or related data through various sources and strategies. It includes examining and planning all the data acquired from the past in advance. It likewise envelops the examination of information inconsistencies seen across different information sources. The market data is analysed and estimated using market statistical and coherent models. Also, market share analysis and key trend analysis are the major success factors in the market report. To know more, please request an analyst call or drop down your inquiry.
The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market and primary (industry expert) validation. Data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Patent Analysis, Pricing Analysis, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis. To know more about the research methodology, drop in an inquiry to speak to our industry experts.
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