By 2024, organizations will lower operational costs by 30% by combining hyper-automation technologies with redesigned operational processes.
Further according to Gartner, “Hyper-automation refers to an approach in which organizations rapidly identify and automate as many business processes as possible. It involves the use of a combination of technology tools, including but not limited to machine learning, packaged software and automation tools to deliver work.” In simple words, it’s end-to-end automation beyond RPA by combining complementary technologies to augment business processes.
“Everything that can be automated will be automated”, said Harvard Professor Shoshana Zuboff in her book “In the Age of the Smart Machine: The Future of Work and Power” in 1988, which is true to RPA, leading to hyper-automation!
The convergence of RPA, artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and cognitive platforms is potentially so disruptive that Klaus Schwab, founder of the World Economic Forum, calls it the “Fourth Industrial Revolution.”
Hyper-automation using drones, intelligent operational systems, and robots will improve the efficiency, necessitating the CIOs to leverage on the following 6 key technology component synergies, which are also the critical points for Robotic Evolution.
1. Sensors
The Internet of Things is the eyes and ears of the supply chain-a real-time data collector and display. IoT sensors can track the stage of data processing automatically with RPA, but they cannot give a logical sequence. With the integration of Artificial Intelligence, sensor data can relate to the stage of processes and extract information from non-sensor sources, and arrive at interpretations and analysis turning it to an intelligent machine!
2. Software
Intelligent Business Process Management Software (iBPMS) works when a Business Process Management (BPM) tool is enriched with additional AI, cloud capabilities, message-oriented middleware, IoT integration, business activity monitoring, and more, resulting in a service extension, expansion of analytical capabilities, and real-time complex activities processing. While leading to an agile, easy, and enterprise-grade performance with unified data and minimal investment, it also provides a graphical user interface with a seamless combination of structured, unstructured, and case-management process patterns, and hybrid deployment. Software intelligence creates an alternate future for the workforce and brings new skill sets to the organization.
3. AI and Algorithms
The advancement in AI has housed the enhanced performance of all technologies leading to the evolution of Industry 4.0. AI augments human-machine interaction and changes the logic of business models. The success generated by AI algorithms, which in turn is fueled by the availability of big data and hardware accelerators, transforms the global market and reshapes the business context. The adoption of AI-driven systems, and machine or software intelligent agents (IA) affects customer interactions, sales platform, and employee skill set.
4. Data Integrity
Machine learning can be supervised, unsupervised, semi-supervised, or reinforcement learning by systems on its own without explicit programming. As against the traditional programming, where data and program is run on a system for output, machine learning creates a program with data and output, which can be used in traditional programming. Accurate machine learning models necessitate accurate integrated training data, as inaccurate data may result in serious repercussions. Connecting ML with RPA in integral when business automation is pursued in an integrated and strategic manner.
5. Artificial Neural Network
Deep learning is similar to machine learning function, except that there are multiple layers of algorithms each providing different interpretations of the data fed. The network of algorithms is called artificial neural networks, resulting in the defining of specifics.
6. Anticipatory system
AI-powered intelligent cognitive process automation (CPA) and analytics aids in information automation and management, geo-spatial visualization, cognitive analysis, amplified intelligence, big data in work, enterprise data sovereignty, and finding the face of the data. While RPA relies on process-oriented basic technologies such as screen scraping, macro scripts, and workflow automation, CPA uses knowledge-oriented advanced technologies such as data mining, semantic technology, natural language processing (NLP), and text analysis, making informed business decisions easier and processing unstructured data.