The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industrial settings where the focus is on end-to-end operational processes. Here, we consider generative, predictive, and prescriptive AI and elaborate on the challenges of diagnosing and improving such processes. We show that AI needs to be grounded using Object-Centric Process Mining (OCPM). Process-related data are structured and organization-specific and, unlike text, processes are often highly dynamic. OCPM is the missing link connecting data and processes and enables different forms of AI. We use the term Process Intelligence (PI) to refer to the amalgamation of process-centric data-driven techniques able to deal with a variety of object and event types, enabling AI in an organizational context. This paper explains why AI requires PI to improve operational processes and highlights opportunities for successfully combining OCPM and generative, predictive, and prescriptive AI.

Artificial intelligence (AI): AI technologies can perform tasks that previously required human intelligence, such as extracting meaning from images, text or speech, detecting patterns and anomalies, and making recommendations, predictions or decisions. They include machine learning, deep learning, natural language processing and generation technologies. AI enables the processing of unstructured data and the automation of specific tasks that traditionally require human judgment or tacit knowledge. Robotic process automation (RPA): RPA is business process automation in which software performs tasks that can be codified in computer code. It is often referred to as'robotics' or'robots'.

If you work in the operations, process improvement, or robotic process automation (RPA) spaces, chances are you've seen the phrase "intelligent automation" pop up many times. While this term seems like it encompasses a lot, the lack of clarity around it can render it meaningless. Is intelligent automation a rebranding of RPA? Is it RPA enhanced by AI? Or, is it simply a catch-all term for process automation software that aims to make technologies like business process management (BPM) sound new again? It starts with technologies we know and love, like BPM and RPA, and brings them together in a unified toolkit so that an enterprise's automation needs can be addressed by the most relevant tool.

The Fifth International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2020) aims to provide a high-quality forum for researchers and practitioners to exchange research findings and ideas on methods and practices in the corresponding areas. Process Querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of processes, both observed and recorded in the real-world and envisioned and designed in conceptual models, to systematically organize and extract process-related information for subsequent use. Process Manipulation studies inferences from real-world observations for augmenting, enhancing, and redesigning models of processes with the ultimate goal of improving real-world business processes. Process Intelligence looks into application of the representation models and approaches in Artificial Intelligence (AI), like knowledge representation, search, automated planning, reasoning, natural language processing, autonomous agents, and multi-agent systems, among others, for solving problems in process mining, that is automated process discovery, conformance checking, and process enhancement, and vice versa using process mining techniques to tackle problems in AI. Techniques, methods, and tools for process querying, manipulation, and intelligence have applications in Business Process Management and Process Mining.

A recent BAI Banking Outlook study identified three areas among the top business challenges and priorities for financial services leaders: acquiring new customers, enhancing the mobile channel experience and making better use of consumer data to improve products and service recommendations. Consequently, financial service organizations are digitally transforming every aspect of their business--from consumer-facing mobile applications to back-office workflow optimization--to meet the increasing demands of the market and maintain evolving service-level agreements and compliance regulations. But if innovation can increase productivity and save costs, many banks still fail to use artificial intelligence and automation to their full potential. Here, we look at how mobile onboarding and processes intelligence can improve customer acquisition and enhance the experience. Consumers told BAI that they want the ability to start in one channel and finish in another without starting over. This omnichannel experience typically begins with mobile; however, studies show 40 percent of consumers abandon applications after initiating the process.

This transformation often leads to businesses investing in digital solutions in order to stay relevant in an increasingly competitive and saturated market. The number of digital workers entering the workforce will increase by 50 percent by 2021, according to new IDC research. With the explosion of the robotic process automation market (RPA), there are now millions of digital workers employed at businesses around the world. So, don't be surprised to see a new robotic colleague at your next company meeting as organisations give at least one robot to every employee to augment their day-to-day activities. But despite the promise that trillions of dollars are expected to be saved by deploying digital workers, most RPA projects fail to fully deliver on that promise.