PUBLISHER: The Business Research Company | PRODUCT CODE: 1693204
PUBLISHER: The Business Research Company | PRODUCT CODE: 1693204
Machine learning intelligent process automation (MLIPA) refers to the utilization of machine learning algorithms and artificial intelligence (AI) technologies to automate and optimize business processes across various industries. By leveraging algorithms to analyze data, make decisions, and execute actions, MLIPA enables more efficient and intelligent automation of complex workflows and business processes.
The primary types of MLIPA can be categorized as structured and unstructured. Structured MLIPA involves the application of algorithms to automate repetitive tasks and workflows using structured data. This encompasses various components such as solutions, software tools, platforms, and services including professional services, advisory or consulting, design and implementation, training, support, and maintenance. These solutions find applications across diverse domains including information technology operations, contact center management, business process automation, application management, content management, security management, and others. MLIPA is utilized by a wide range of end-users including those in banking, financial services, insurance (BFSI), telecommunications and information technology (IT), transport and logistics, media and entertainment, retail and e-commerce, manufacturing, healthcare and life sciences, and human resource management.
The machine learning (ML) intelligent process automation research report is one of a series of new reports from The Business Research Company that provides machine learning (ML) intelligent process automation market statistics, including the machine learning (ML) intelligent process automation industry's global market size, regional shares, competitors with a machine learning (ML) intelligent process automation market share, detailed machine learning (ML) intelligent process automation market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning (ML) intelligent process automation industry. This machine learning (ML) intelligent process automation market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.
The machine learning (ML) intelligent process automation market size has grown rapidly in recent years. It will grow from $19.58 billion in 2024 to $23.19 billion in 2025 at a compound annual growth rate (CAGR) of 18.4%. The growth in the historic period can be attributed to increased spending on business operations optimization, technological adoption across various industry sectors, demand for intelligent process automation solutions and services, focus on automation to reduce manual efforts, and emphasis on improving operational efficiency.
The machine learning (ML) intelligent process automation market size is expected to see rapid growth in the next few years. It will grow to $45.09 billion in 2029 at a compound annual growth rate (CAGR) of 18.1%. The growth in the forecast period can be attributed to the integration of emerging technologies, expansion and enhancement of product portfolios, adoption of innovative automation solutions, increasing demand for automation technologies in developing countries, and focus on automation to enhance customer experience. Major trends in the forecast period include the integration of cloud-based technologies for automation, increased use of machine learning algorithms for efficiency, rise in virtual agents for customer service enhancement, adoption of natural language processing for improved interactions, and focus on AI-driven decision-making processes.
The increasing demand for digital transformation is expected to drive the growth of the machine learning (ML) intelligent process automation market in the coming years. Digital transformation involves integrating digital technologies into an organization's products, processes, and strategies to improve efficiency, productivity, customer engagement, innovation, and revenue. The demand for digital transformation is primarily driven by the need for greater efficiency, agility, and competitiveness in a rapidly evolving and interconnected business environment. Machine learning intelligent process automation plays a crucial role in digital transformation by automating repetitive tasks, optimizing processes, and enabling data-driven decisions across various business functions, ultimately boosting efficiency and productivity. For example, in November 2023, the Central Digital and Data Office, a UK-based government body, reported a 19% growth in the digital and data profession between April 2022 and April 2023, meeting critical demands for digital expertise. Moreover, in December 2023, the European Commission revealed that cloud-based solution adoption in the European Union increased by 4.2 percentage points in 2023, with 45.2% of businesses purchasing cloud computing services, reflecting a significant rise compared to 2021. As a result, the growing demand for digital transformation is fueling the growth of the machine learning intelligent process automation market.
Key players in the machine learning intelligent process automation market are focusing on the development of advanced solutions, such as artificial intelligence platforms, to optimize operations and boost productivity. Artificial intelligence platforms are software solutions that facilitate the creation and management of AI applications, enhancing decision-making and efficiency. For example, in March 2024, Cognizant, a US-based global technology company, unveiled the Advanced Artificial Intelligence Lab. This lab is dedicated to advancing AI through research and development, with a goal of accelerating innovation in the field. The initiative will focus on advancing AI science, creating intellectual property, and developing AI-enablement technologies.
In January 2023, McKinsey, a renowned management consulting firm, acquired Iguazio for an undisclosed sum. This strategic acquisition enables McKinsey to significantly accelerate and scale AI deployments. By leveraging Iguazio's MLOps Platform, McKinsey aims to expand its AI capabilities and deliver industry-specific AI solutions that are more productive, expedite the journey from proof-of-concept to production, and enhance reliability. Iguazio, an Israel-based software company, specializes in providing ML intelligent automation processes through its MLOps Platform, thus complementing McKinsey's offerings and bolstering its position in the AI-driven digital transformation landscape.
Major companies operating in the machine learning (ML) intelligent process automation market are Alibaba Group Holding Limited, Accenture plc, International Business Machines Corporation (IBM), SAP SE, Tata Consultancy Services Limited (TCS), Capgemini SE, Atos SE, Wipro Limited, Xerox Holdings Corporation, NICE Ltd., Blue Prism Group plc, Pegasystems Inc., BlueHalo LLC, UiPath Inc., Automation Anywhere Inc., Appian Corporation, Kofax Inc., Bright Machines Inc., Cove.Tool Inc., Larc AI (Pty) Ltd., Cinnamon Inc., AutomationEdge Technologies Inc., AntWorks Global Limited
North America was the largest region in the machine learning (ML) intelligent process automation market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the machine learning (ML) intelligent process automation market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
The countries covered in the machine learning (ML) intelligent process automation market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
The machine learning (ML) intelligent process automation market includes revenues earned by entities by providing services such as automated data extraction, predictive analytics, anomaly detection, and natural language processing (NLP) for text understanding. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
Machine Learning (ML) Intelligent Process Automation Global Market Report 2025 from The Business Research Company provides strategists, marketers and senior management with the critical information they need to assess the market.
This report focuses on machine learning (ml) intelligent process automation market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.
Where is the largest and fastest growing market for machine learning (ml) intelligent process automation ? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward? The machine learning (ml) intelligent process automation market global report from the Business Research Company answers all these questions and many more.
The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market's historic and forecast market growth by geography.
The forecasts are made after considering the major factors currently impacting the market. These include the Russia-Ukraine war, rising inflation, higher interest rates, and the legacy of the COVID-19 pandemic.