Purpose- The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning Artificial Intelligence (AI) and Machine Learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field. Design/methodology/approach- In this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology, to analyze related papers. Findings- In business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models, and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis. Research limitations/implications- While this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024. Originality/value- This paper adopts a pioneering approach by conducting an extensive examination of the integration of AI/ML techniques across the entire process management lifecycle. Additionally, it presents groundbreaking research and introduces AI/ML-enabled integrated tools, further enhancing the insights for future research.

Blockers can take a tiny task and stretch it over days or weeks. Taking a moment at the beginning of a project to look for and prevent possible blockers can improve productivity. These examples of personal, team, and organizational levels show how gathering the right information and performing preflight checks can save hours of wasted time later. Two IT workers--Andrew and Bertie (not their real names)--were assigned the same task. While this task should have taken about an hour of hands-on keyboard work, it took Andrew four days. Andrew began the task one sunny Monday morning. Work went well until he hit a speed bump and needed to ask the requester (who we will call Roger) a question. Andrew tried to find him on the company chat system, only to learn Roger was out of the office. Andrew sent an email instead.

The usage and impact of deep learning for cleaner production and sustainability purposes remain little explored. This work shows how deep learning can be harnessed to increase sustainability in production and product usage. Specifically, we utilize deep learning-based computer vision to determine the wear states of products. The resulting insights serve as a basis for novel product-service systems with improved integration and result orientation. Moreover, these insights are expected to facilitate product usage improvements and R&D innovations. We demonstrate our approach on two products: machining tools and rotating X-ray anodes. From a technical standpoint, we show that it is possible to recognize the wear state of these products using deep-learning-based computer vision. In particular, we detect wear through microscopic images of the two products. We utilize a U-Net for semantic segmentation to detect wear based on pixel granularity. The resulting mean dice coefficients of 0.631 and 0.603 demonstrate the feasibility of the proposed approach. Consequently, experts can now make better decisions, for example, to improve the machining process parameters. To assess the impact of the proposed approach on environmental sustainability, we perform life cycle assessments that show gains for both products. The results indicate that the emissions of CO2 equivalents are reduced by 12% for machining tools and by 44% for rotating anodes. This work can serve as a guideline and inspire researchers and practitioners to utilize computer vision in similar scenarios to develop sustainable smart product-service systems and enable cleaner production.

Business process management is now a mature discipline. It has formal approaches, methods, techniques and a rich set of concepts. It has also evolved to the point where it is applied to projects of all sizes and supports both business process improvement and business transformation. As BPM evolved, so did the enterprise's business processes. They became too large and complex to be managed without automated tool support.

Recent years have brought a stream of exciting developments in the field of Business Process Management (BPM). We have seen a breathtaking uptake of business process automation technology, such as Robotic Process Automation (RPA). We have witnessed the rise of process mining, and promising evolutions in the areas of predictive process analytics and digital process twins. In the eyes of a business analyst, each of these technologies offers compelling opportunities to enhance operational excellence. However, if we look at these technologies in isolation, it is easy to miss the bigger picture and the wider space of opportunities that these technologies open when used jointly rather than applied in individual projects or silos.

Manufacturing is an increasingly competitive industry as new technologies unlock previously impossible standards. Investing in cutting-edge improvements to processes can lead to impressive results, but not every investment sees a significant or quick ROI in every case. Consequently, many manufacturers are hesitant to embrace new methods. New technologies and processes must have a demonstrable impact on a company's bottom line to make a convincing argument for adoption. In that spirit, here are seven recent manufacturing process improvements and how they affect profits.

If you like disrupting the norm and are looking to join a company revolutionizing an industry then you will LOVE what Carvana has done for the car buying experience. Buying a car the old fashioned way sucks and we are working hard to make it NOT suck. I mean, have you seen our vending machine?! We are looking for people who are excited to thrive in an environment of impactful change. Team spirit is evident at Carvana and everyday we let our passions and creativity foster innovation. We take big swings, set ambitious goals, and challenge each other to make data- and process-driven decisions in everything we do.

According to McKinsey, 70 percent of businesses worldwide will be using at least one tool powered by artificial intelligence by 2030. The consulting company's research says that the AI global market can grow up to 13 billion USD in a decade giving a 16 percent boost to the economy. Artificial intelligence applications are rapidly expanding across all industries helping them recover after the pandemic. Here is the list of the most high-potential AI uses for the next few years. Intelligent Process Automation, or IPA, allows automating digital processes using artificial intelligence.

When it comes to embracing new technology and digitising entire sectors of business, look no further than the Industry 4.0 revolution. All over the world, companies from the likes of manufacturing, warehousing and logistics are embracing Industry 4.0's key technologies to open up new values and benefits. However, Industry 4.0 trends shift and evolve as time goes on. As such, we want to take a look at Industry 4.0 technologies and projects in more detail. We've previously discussed the topic of Industry 4.0 before, but here's a quick recap.

Experts say that artificial intelligence (AI) is likely to have a bigger impact on health care than anything the field has experienced in our lifetime. During the COVID-19 pandemic, we're seeing how AI is helping clinicians screen for, and diagnose, COVID-19; it's aiding researchers' efforts to develop new drugs and vaccines. Though some health care workers worry that AI tools could replace their jobs, in fact AI is driving innovation, process improvements and better patient outcomes. It's enhancing providers' ability to care for more patients safely. The American Hospital Association and Microsoft now offer a free, one-hour course, for continuing education credits, to guide health care teams through key considerations and specific actions for AI's responsible and strategic implementation.