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Intelligence Process Automation refers to the subset of technologies which boost business value and productivity by providing automation capabilities.
Machine Learning, Artificial Intelligence, Cognitive Learning, and Robotic Process Automation (RPA) together make up the intelligence process automation workflow.
They enable businesses to improve their efficiency and cut down operating costs. On a simpler level, intelligent automation is technology that mimics the human brain and makes sound judgments using intelligent decision making. Unlike automated tasks which require no thinking, it decides how and in what ways to automate business processes using simulated human thought, intelligence, and analysis.
Intelligent Process Automation is the act of automating business workflows by adding intelligence to existing automation. The Global Data Protection Act (GDPA) defines intelligent process automation as an agile set of technologies which are used as a holistic approach for managing the flow of data for enterprises. GDPA gives business owners insights on how to identify areas of improvement and use RPA tools to automate various business processes and ensure data privacy.
RPA technology is used for automating manual data entry tasks in enterprises which eliminate the need for human effort. Artificial Intelligence uses neural nets which have self-learning networks that take in information and make intelligent decisions by analyzing past data models and learning from them. Cognitive Learning is the conjunction of Artificial Intelligence (AI) and Cognitive Computing and it is defined as adding intelligence to information-based tasks carried out by computer programs.
Unlike AI and Machine Learning techniques, Cognitive Learning mimics the human way of thinking. It blends a combination of technologies such as Natural Language Processing (NLP), Computer Vision, data mining, semantic learning, and speech recognition. All these technologies make up the umbrella term for intelligent process automation. Many cognitive learning applications mimic human intuition and make human judgments. Businesses make investments of upwards $2.5 billion dollars in the industry because of these reasons.
Automation can be thought of as moving away from repetitive high-volume tasks to activities which require finer cognitive thinking. Robotic Process Automation (RPA) is technology used for automating routine and repetitive tasks which are predictable and require a set of limited human interactions with digital interfaces.
RPA can be further divided into assisted and unassisted automation. In assisted RPA, bots are deployed to take care of manual tasks with human workers overseeing certain parts of the process. Unassisted RPA software automates end-to-end processes and is deployed in centralized servers that require scheduling specific workflows for automatically carrying out business processes.
Intelligent Process Automation simulates human behaviors and judgements, going beyond what modern RPA tools are capable of.
When comparing intelligent process automation and robotic process automation, it is important to note that the former adds layers of complexity to thought and human analysis. This means the technology performs at a higher level when carrying out various kinds of tasks and makes intelligent decisions on the go.
The intelligent process automation industry is forecasted to reach a valuation of USD 13.75 billion dollars by 2023 and grow at a CAGR rate of 12.9% until then every year. IT enterprises cut down operational employees, increase customer satisfaction, and get a myriad of benefits in the application and lifecycle development processes of software deployment. Organizations are catching on to the latest intelligent process automation trends because data compliance requirements and how easy they are to use.
Here is a list of the top market trends in intelligent process automation:
Intelligent Process Automation Ecosystems are expected to serve as the bridge for enterprises and their current technology stacks. Modern organizations drive business results using automation tools but this is proving to be simply not enough. More customizations are needed which are tailored specifically for brands and intelligent process automation ecosystems are being introduced to fill in these gaps.
If you’re asking yourself, “What is the IA ecosystem?” put simply – it is an ecosystem that consists of four main components - Automation, Digital Decisioning, Analytics, and Process Reengineering.
Business Process Reengineering is helping businesses make drastic improvements unlike Business Process Improvement (BPI) which simply tweaks existing processes. Digital Decisioning platforms use agile technologies and management frameworks to produce data-driven insights and rapid autonomy in business applications. Predictive Analytics is used to generate unbiased reports and solve business problems by bringing key insights from business data modeling.
Document digitization software is another emerging trend in intelligence process automation. Many businesses deal with different types of documents and in vast amounts. Project administration costs can be high and businesses realize that they simply don’t have the time to hire manual labor. Platforms like Docsumo are used by top companies like Hitachi and PaySense to process invoices, customer onboarding documents, tax receipts, and financial documents.
In-house document scanning doesn’t produce satisfactory results and businesses face problems related to document management, storage, planning, and organization. All these pain points are being addressed with document management and image capture solutions.
This year virtual assistants and chatbots are increasingly deployed by businesses to answer common customer questions. The role of chatbots is expected to increase by up to 40% and with intelligence process automation.
AI is revolutionizing ways in which customers are interacting with brands and transforming multiple industry verticals. The number one trend currently is using chatbots to resolve simple business queries but virtual assistants are being used for resolving escalations, custom product queries, and for saving up to 30% on customer support costs.
Intelligent Process Automation is a step above regular automation since it mimics human behaviors and thought patterns for intelligently automating business workflows and processes. Machines are able to understand and interpret unstructured data, analyze and solve business problems on the go. Use cases of intelligent process automation are growing in the business world and companies are realizing their numerous benefits.
Some of the major benefits of IPA for businesses are:
Employees are able to map out existing processes and see how they function as a single unit. They can identify bottlenecks in operations and find out value-added activities when implementing intelligent process automation solutions
Customers report higher rates of satisfaction when using intelligent automation solutions. This is due to improved response times, personalization in services, and 24/7 availability.
One of the biggest benefits of using intelligent process automation is the organization of raw and unstructured data. Banks and financial institutions use these solutions for automated data extraction and entry into information management systems.
Another huge advantage is that machine learning algorithms in intelligent process automation platforms are self-learning. Models extrapolate information from data sources, read data autonomously, and get better at it over time. It can spot anomalies, errors, and make appropriate action plans for them.
Businesses cannot afford to let their systems go offline during operations. Intelligent process automation improves infrastructure agility and provides automated data backup. With continuous monitoring and AI detecting threats, it responds quickly when incidents happen and eliminates downtimes, thus letting businesses continue their services.
Business intelligent process automation extends the capabilities of modern AI and machine learning applications. By delivering adaptable and continuous improvements to existing workflows, organizations are able to improve their operations and scale up without having to sacrifice processing times or operational costs.
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