The process mining community has recently recognized the potential of large language models (LLMs) for tackling various process mining tasks. Initial studies report the capability of LLMs to support process analysis and even, to some extent, that they are able to reason about how processes work. This latter property suggests that LLMs could also be used to tackle process mining tasks that benefit from an understanding of process behavior. Examples of such tasks include (semantic) anomaly detection and next activity prediction, which both involve considerations of the meaning of activities and their inter-relations. In this paper, we investigate the capabilities of LLMs to tackle such semantics-aware process mining tasks. Furthermore, whereas most works on the intersection of LLMs and process mining only focus on testing these models out of the box, we provide a more principled investigation of the utility of LLMs for process mining, including their ability to obtain process mining knowledge post-hoc by means of in-context learning and supervised fine-tuning. Concretely, we define three process mining tasks that benefit from an understanding of process semantics and provide extensive benchmarking datasets for each of them. Our evaluation experiments reveal that (1) LLMs fail to solve challenging process mining tasks out of the box and when provided only a handful of in-context examples, (2) but they yield strong performance when fine-tuned for these tasks, consistently surpassing smaller, encoder-based language models.

Transformer-based Language Models are widely used in Natural Language Processing related tasks. Thanks to their pre-training, they have been successfully adapted to Information Extraction in business documents. However, most pre-training tasks proposed in the literature for business documents are too generic and not sufficient to learn more complex structures. In this paper, we use LayoutLM, a language model pre-trained on a collection of business documents, and introduce two new pre-training tasks that further improve its capacity to extract relevant information. The first is aimed at better understanding the complex layout of documents, and the second focuses on numeric values and their order of magnitude. These tasks force the model to learn better-contextualized representations of the scanned documents. We further introduce a new post-processing algorithm to decode BIESO tags in Information Extraction that performs better with complex entities. Our method significantly improves extraction performance on both public (from 93.88 to 95.50 F1 score) and private (from 84.35 to 84.84 F1 score) datasets composed of expense receipts, invoices, and purchase orders.

Much has been written about the importance of internal document digitisation and automation with the significant improvement in operational efficiencies it brings to organisations worldwide. The last few years has seen it quickly cement itself as vital for business efficiencies, productivity, and the knock-on effect of this on a business's bottom line. The automation of internal processes and operations is vital. However, we need to pay cognizance to the equal importance of ensuring that these same efficiencies are implemented across both inbound and outbound documents and processes. Manually retrieving data from inbound documents and entering it into your company's ERP system can be tedious and time-consuming.

Constraint monitoring aims to monitor the violation of constraints in business processes, e.g., an invoice should be cleared within 48 hours after the corresponding goods receipt, by analyzing event data. Existing techniques for constraint monitoring assume that a single case notion exists in a business process, e.g., a patient in a healthcare process, and each event is associated with the case notion. However, in reality, business processes are object-centric, i.e., multiple case notions (objects) exist, and an event may be associated with multiple objects. For instance, an Order-To-Cash (O2C) process involves order, item, delivery, etc., and they interact when executing an event, e.g., packing multiple items together for a delivery. The existing techniques produce misleading insights when applied to such object-centric business processes. In this work, we propose an approach to monitoring constraints in object-centric business processes. To this end, we introduce Object-Centric Constraint Graphs (OCCGs) to represent constraints that consider the interaction of objects. Next, we evaluate the constraints represented by OCCGs by analyzing Object-Centric Event Logs (OCELs) that store the interaction of different objects in events. We have implemented a web application to support the proposed approach and conducted two case studies using a real-life SAP ERP system.

Veryfi, using artificial intelligence (AI) technology to transform documents into structured data in just seconds, has announced continued strong business momentum and growth in the second quarter. As economic concerns increase, many companies begin to reduce their staff to control costs; 88 percent of job loss in routine occupations occurs within 12 months of a recession. While economic uncertainty continues, Veryfi has emerged as a trusted, reliable partner for companies seeking greater efficiency and stronger customer relationships, continuing its strong annual recurring revenue (ARR) growth. In the second quarter, Veryfi added over a dozen new logos and major accounts including a top supplier of enterprise resource planning software and one of the world's largest CRM/Direct Marketing Network companies. "As companies seek new ways to increase efficiency and manage costs to position themselves for a challenging economy, Veryfi is leading the way, applying AI to automate routine data entry and streamline business processes," said Ernest Semerda, co-founder and CEO of Veryfi.

Through pattern detection, machine learning is already transforming business processes by making sense of and automatically capturing and filing incoming content. Yet it is only when intelligent process automation is applied with broader enterprise context that global businesses will experience the full value of artificial intelligence, argues Dr John Bates, CEO of SER Group.

Digital transformation often entails small-scale changes to information systems supporting the execution of business processes. These changes may increase the operational frictions in process execution, which decreases the process performance. The contributions in the literature providing support to the tracking and impact analysis of small-scale changes are limited in scope and functionality. In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. More in detail, we model the updates using the configuration of DTOs and quantitatively assess different types of impacts of information system updates (structural, operational, and performance-related). We implemented a prototype of the proposed approach. Moreover, we discuss a case study involving a standard ERP procure-to-pay business process.

PO Matching is the process of connecting a purchase order (PO) issued by a client indicating types, quantities, and agreed prices for products/services to the invoice issued by a vendor for it's delivery. The goal of PO matching is to ensure timely vendor payments, correct accounting of costs and easy detection of fraudulent practices. PO matching involves several steps, including the receipt of invoice, capture of data, verification with purchase order, matching the parameters, and resolution based on various parameters. Invoice processing and PO matching are complex, time-consuming, and resource-intensive processes when performed manually, especially in scaled-up business activities. Even in departments where there is digitization of information in the form of Enterprise Resource Planning (ERP) applications, a significant amount of human labour is required; from the time an invoice is raised or received to its entry into the ERP application, accounts payable personnel perform a seemingly endless list of chores.

Historically, the term automation refers to the notion of work typically done by humans instead being conducted by machines operating within a self-governing system. In many industries, automation is not just commonplace, it's a necessity – there's no way that sectors such as manufacturing, automotive and medicine would be able keep up with demand without automated systems that perform tasks at a rate that would be impossible for human workforces. But today, automation can apply just as much to the services industry, allowing businesses to hand control of business processes over to bots. And that's largely thanks to the growth of Robotic Process Automation (RPA), something we revealed in a recent report published by The AI Journal. Robotic Process Automation (RPA) can automate and digitize the repetitive processes typically performed by human operators, and it is proving increasingly popular in the business world.

Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies.