Recent advancements in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have enhanced organizations' ability to reengineer business processes by automating knowledge-intensive tasks. This automation drives digital transformation, often through gradual transitions that improve process efficiency and effectiveness. To fully assess the impact of such automation, a data-driven analysis approach is needed -- one that examines how traditional and AI-enhanced process variants coexist during this transition. Object-Centric Process Mining (OCPM) has emerged as a valuable method that enables such analysis, yet real-world case studies are still needed to demonstrate its applicability. This paper presents a case study from the insurance sector, where an LLM was deployed in production to automate the identification of claim parts, a task previously performed manually and identified as a bottleneck for scalability. To evaluate this transformation, we apply OCPM to assess the impact of AI-driven automation on process scalability. Our findings indicate that while LLMs significantly enhance operational capacity, they also introduce new process dynamics that require further refinement. This study also demonstrates the practical application of OCPM in a real-world setting, highlighting its advantages and limitations.

Affiliations: 1. Cedars Sinai Medical Center, Los Angeles, California 2. University of California Los Angeles, California Keywords: vision transformer, 3D convolutional neural network, assessment tool, suturing skill, video analysis Key information: 1. Research question: Can we automate the end-to-end assessment of suturing expertise, and what benefits would it offer? MANUSCRIPT Introduction We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills. While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes.

The automation of work, understood as the process by which human labour is replaced by machines, is also a cause for scholarly concern across different disciplines. For some scholars, the large-scale deployment of AI in the workplace amounts to a'Fourth Industrial Revolution' or a'Second Machine Age', threatening to render human work--nay, humankind in its entirety--obsolete [3],[6]. Even despite the potential introduction of a Universal Basic Income (UBI), which could in principle guarantee citizens' livelihood, it is argued that policymakers would still need to safeguard work, since it bears intrinsic value that transcends the instrumental value of a paycheck [8]. AI-driven automation is, hence, largely framed as a threat to be counteracted by law. Nonetheless, the axiological superiority of work as an intrinsically valuable activity and the insistence on its preservation, even if humans' sustenance could be otherwise secured, should not be taken for granted.

Artificial intelligence (AI) signals the beginning of a revolutionary period where technological advancement and social change interact to completely reshape economies, work paradigms, and industries worldwide. This essay addresses the opportunities and problems brought about by the AI-driven economy as it examines the effects of AI disruption on the IT sector and information technology education. By comparing the current AI revolution to previous industrial revolutions, we investigate the significant effects of AI technologies on workforce dynamics, employment, and organizational procedures. Human-centered design principles and ethical considerations become crucial requirements for the responsible development and implementation of AI systems in the face of the field's rapid advancements. IT education programs must change to meet the changing demands of the AI era and give students the skills and competencies they need to succeed in a digital world that is changing quickly. In light of AI-driven automation, we also examine the possible advantages and difficulties of moving to a shorter workweek, emphasizing chances to improve worker productivity, well-being, and work-life balance. We can build a more incslusive and sustainable future for the IT industry and beyond, enhancing human capabilities, advancing collective well-being, and fostering a society where AI serves as a force for good by embracing the opportunities presented by AI while proactively addressing its challenges.

In any roundup of 2022, Elon Musk's Optimus robot waving its mechanical arms in the air is likely to be featured prominently. Musk boldly claimed that we could see a "fundamental transformation of civilization" with such advances in robotics. While his vision may take years to unfold, we are now at a juncture where we will see rapid deployment and advancement of artificial intelligence. The future is waving hello, and 2023 promises to be exciting in AI. Despite record numbers of low unemployment, companies continue to find it challenging to find employees -- especially people with the right skill sets.

DevSecOps tools such as GitLab's One DevOps Platform plan to inject AI into developer workflows to shore up secure coding, a shift IT pros and analysts say is timely as security AI becomes more popular. In IT and security operations, AIOps tools can reduce the number of alerts IT pros must respond to or narrow down the root cause of incidents as distributed cloud-native infrastructure grows more and more complex. The same kind of overload that's led IT ops teams to embrace artificial intelligence and machine learning has creeped into the developer side of the DevSecOps model as well, according to IT analysts. "Cloud services and modern software development processes, such as microservices application architectures, create a much greater scale of software releases and attack exposures," said Melinda Marks, an analyst at Enterprise Strategy Group, a division of TechTarget. "That, coupled with the cybersecurity skills gap, means that they are looking for ways to reduce tedious, manual tasks to work more efficiently and reduce staff burnout." The movement to shift security left into DevOps workflows is bringing along applications for AI assistance as well, from vendors such as Palo Alto Networks' Prisma Cloud and GitLab.

Sayantan Dasgupta is a Sr. Director of Demand Generation at Gramener Ex VC Partner 2x VP Marketing 15 years of leadership experience. The global AI market in 2021 reached a staggering value of $327.5 million. Clearly, AI is a topic of interest everywhere. It can maximize engagement with stakeholders while also helping companies run optimally. What are the applications for AI in business?

Artificial intelligence has become invaluable to many different industries all over the world. A 2019 study by Gartner found that 37% of businesses use AI technology in some way. That number has almost risen over the last couple of years. One of the biggest reasons AI technology is becoming more valuable is that it helps companies automate many essential processes. AI has helped automate many processes that previously took days, weeks, or even months to complete. Automation is playing an ever-increasing role in the functioning of our society, and perhaps the area that is affected the most by this phenomenon is business management.

Automation in artificial intelligence has an extensive effect on the economy. Industrialists and giant companies all over the world are further adapting to the idea of automation in artificial intelligence. In India, technological progress, is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. An increment in labor productivity generally translates into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services.

Industrialists and giant companies all over the world are further adapting to the idea of automation in artificial intelligence. In India, technological progress, is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. An increment in labor productivity generally translates into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services. AI should be welcomed for its potential economic benefits.