Automation has seen several iterations. From web scraping and macros to Robotic Process Automation (RPA), the paradigm shifting mechanism has come a long way owing to the substantial benefits, majorly related to cost and process efficiency. While these benefits sound promising, there are several challenges that an organization might face with an RPA-first approach. These challenges include limited cognitive abilities or abilities to mimic human intelligence, inability to scale unstructured and non-repetitive tasks, regular human intervention, and maintenance and monitoring. Also, alignment of business and IT objectives and task selection process is important in making automation effective. While it is not necessary to utilize RPA for all process, an appropriate selection criterion to choose the process should be implemented for realizing the benefits of RPA.

The latest iteration of automation, intelligent process automation (IPA) counters the above challenges through the use of AI, combining its supporting capabilities such as machine learning (ML), natural language processing (NLP), computer vision, and data mining, with RPA. The shift towards IPA is driven by its ability to handle multifaceted processes, ability to self-learn through data models, reduction in process errors, minimal human intervention, and the ability to improve process and business outcomes.

The cornerstones of IPA, automation and AI, have been top priorities for GCC countries showcased through their respective strategies, national programs, and initiatives. Authorities such as Saudi Data & AI Authority (SDAIA), UAE's Ministry of State for Artificial Intelligence, Digital Economy and Remote Work Applications, and programs such as Future Factories initiative (KSA), Dubai Robotics and Automation (R&A) Program, TASMU Smart Qatar, Factory Automation and AI (Oman), underscores the importance given to the two enablers of IPA.

While the private sector has also started exploring IPA use cases, its effective utilization depends on an effective IPA strategy that begins with assessment of internal capabilities and identifying the right processes for IPA. Our paper, Intelligent Process Automation with AI, explores the above topics in detail, listing out key challenges for organizations and how they can be countered leveraging a mix of internal and external collaborations.