Intelligent Process Automation Market

Market Overview 

Intelligent Process Automation (IPA) is a combination of technologies - Robotic Process Automation (RPA) and artificial intelligence (AI) - that work together to automate and optimize business processes end-to-end . IPA is designed to handle unstructured data and processes that previously required human involvement.

IPA can be used in a number of industries and applications, including human resources, finance, customer service, and healthcare. By automating manual processes and repetitive tasks, IPA frees employees to focus on high-value work, reduces errors, and improves efficiency. Additionally, using AI and machine learning, IPA can learn from past actions and suggest solutions for more complex problems.

The Intelligent Process Automation Market was estimated to be worth $12.8 billion in 2022. The Intelligent Process, Automation market industry is projected to grow from USD 14.42 Billion in 2023 to USD 29.52 billion by 2030, exhibiting a compound annual growth rate (CAGR) of 12.68% during the forecast period (2023 - 2030)

Intelligent process automation (IPA) stands for application technology. Its software is designed with extra features like artificial intelligence and related new technologies, including cognitive automation, machine learning, and computer vision, which are used for high volume handling and repeatable tasks. It is constructive in improving business processes by reducing the human hand to assist workers by eliminating repetitive, replicable, and routine tasks. Thus it enhances the customer experience by simplified methods and techniques.

Furthermore, this IPA helps in doing the work in a simple and better way within time and improves the processes' efficiency degree and response time. Hence, it increases the worker’s performance customer experience and also helps in reducing the operational risks. To design a new generation of hardware and software to automate industrial processes, there is a need for a few factors like improvement in sensors, advances in machine learning techniques, and increasing computing power is helpful.

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Market Segmentation 

The global intelligent process automation market has been divided into segments based on data type, component, deployment, technology, organization size, vertical, and region.

Based on Data Type:

Based on data type, the intelligence process automation is divided into two kinds Structured and Unstructured.

Based on Component:

The intelligence process automation based on components has two types Solutions and Services.

Based on Deployment:

The intelligence process automation by deployment is divided into On-Premise and Cloud.

Based on Technology:

The global intelligence process automation market by technology is segmented into three types Machine learning, Natural-Language Processing, and Cognitive Technology. Machine learning holds the largest share for its benefits, like allowing computers to automatic education without any human assistance or intervention—advanced technologies like voice recognition and image recognition systems based on machine learning fuel the market growth.

Regional Analysis 

Globally, the intelligence process automation based on the region is divided into four main geographies: North America, Asia-Pacific, Europe, and the rest of the world. During the review period, North America is expected to dominate the largest intelligent process automation market share due to its established infrastructure for developing advanced technologies in the region, which leads to fast-paced development.

Some prominent global companies are as follows:

• UiPath, Inc. (US)
• Blue Prism Group (UK)
• Dell EMC Corp. (US)
• SAP SE (Germany)
• IBM Corporation (US)

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Industry News

• In June 2019, to extend automation capabilities intelligently in the cloud, a major key player Blue Prism acquired an intelligent automation industry, Thoughtonomy. To combine AI, RPA, cloud deployment in a single platform, this acquisition proves to be futuristic and helpful and is for scaling automation strategy at a high rate.

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