The near future of IA

The global Intelligent Automation market is projected to reach 29.5 billion USD by the end of 2027, growing at a CAGR of 13.8% during the period 2022 to 2027.

Intelligent process automation (IPA) is the implementation of Artificial Intelligence (AI) and linked new technologies to Robotic Process Automation (RPA), such as Computer Vision (CV), Cognitive Automation, and Machine Learning (ML). Intelligent automation offers humans extensive advanced technologies and flexible processes, allowing them to make faster and more intelligent decisions. It offers benefits such as increasing process efficiency, improving the consumer experience, optimizing back-office operations, optimizing workforce productivity, reducing costs and risks, product and service innovation, and effective monitoring and fraud detection.

These days, BoTs are generally viewed in one of two ways: as digital employees designed to replace human jobs, or as agents designed to augment work performed by humans, ostensibly freeing them up from “mind-numbing” tasks to focus on work that provides greater business value.

Forrester’s Le Clair acknowledges that automation will contribute to some headcount attrition, but says that when a bot or a machine takes over a task previously performed by a human, the enterprise has those labor hours at its disposal to deploy elsewhere or bank as profit. So ideally, IA won’t be held up much on that account.

This image above explains various components converging together to build a robust and efficient Intelligent Automation platform, in the near future.

1. Use Cases Surge: Intelligent Automation will move beyond repetitive tasks and basic decision-making processes to more complex functions. This could include cognitive automation, where systems can understand, interpret, and learn from unstructured data like images, text, and voice.
2. Convergence of AI and RPA: There will be a closer integration between Artificial Intelligence (AI) technologies such as machine learning, natural language processing, and robotic process automation (RPA). This convergence will enable more sophisticated automation solutions capable of handling diverse tasks and scenarios.
3. Human-Machine Interaction (HMI): Rather than replacing humans, IA will increasingly augment human capabilities, leading to more efficient and productive collaborations between humans and machines. This collaboration will require a shift in organizational culture and the development of new skill sets.
4. Ethical and Regulatory Compliance: As IA becomes more pervasive, there will be increased scrutiny on ethical considerations such as data privacy, algorithmic bias, and job displacement. Regulatory frameworks will evolve to address these concerns and ensure responsible deployment of automation technologies.
5. Hyperautomation: Hyperautomation involves the use of advanced technologies like AI, machine learning, RPA, and process mining to automate end-to-end business processes with minimal human intervention. This approach will lead to greater operational efficiency and agility.
6. Industry-Specific IA Solutions: IA solutions will be tailored to specific industries, addressing unique challenges and requirements. For example, in healthcare, IA can streamline administrative tasks and improve patient care, while in manufacturing, it can optimize production processes and predictive maintenance.
7. Cloud-Based IA Solutions: Cloud-based IA platforms will gain traction, offering scalability, flexibility, and accessibility to organizations of all sizes. These platforms will enable rapid deployment and integration with existing systems, driving faster ROI.
8. Priority to User Experience: User experience will become a critical factor in the design and implementation of IA solutions. Intuitive interfaces, natural language processing capabilities, and personalized interactions will enhance user adoption and satisfaction.
9. Computer Vision as Super Supplement: Computer vision would help IA in multiple ways, in terms of how BoTs visualize what’s on computer screens and how to parse the documents for processing, as follows: See better, Read better’, "Analyze better" and finally "Report better" to seamlessly connect all concerned better.

Inference: Intelligent automation?(IA) is a combination of components, including AI, robotic process automation, business process management and other complementary technologies that enable companies to advance workflows and streamline end-to-end processes. In a digital-first world, intelligent automation adoption can boost customer satisfaction by giving insurance workers the time they need to focus on complex customer cases.

Looking beyond RPA, previously hidden opportunities are revealing themselves. A combination of automation, AI, and data analytics are allowing companies to realize the benefits of intelligent automation. In this new era, companies are enhancing automation with learning and judgment strategies to deliver cognition similar to the human brain.

For example, take Google's AlphaGo program. The program mastered the ancient board game Go in a matter of days. A game that many people believed only humans could excel in, since it requires intuition and abstract thinking. But, through machine learning, AlphaGo was able to recognize patterns and determine the next best moves on its own. Most surprising of all, by forming its own strategies, AlphaGo went on to defeat some of the world's best human players.

The END card: So, the future of IA is technically a "click away treasure", to grab, for those who strongly believe in Automated typing with simple clicks and drops, Human Machine Interaction, Cognitive thinking, and Computer Vision.