Editorial: Business transformation through AI-enabled technologies

摘要

The digital era is characterized by rapid technological advancements, with Artificial Intelligence (AI) emerging as a key driver of business transformation. Companies are increasingly integrating AI-enabled technologies into their operations to enhance productivity, streamline processes, and optimize decision-making [2]. For instance, one of the special issue papers examined the integration of artificial intelligence in supply chain management (SCM) [4]. One of the key insights highlighted by this paper is that "integrating AI in SCM not only improves operational efficiency and sustainability but also promotes resilience against disruptions" [4]. From intelligent automation to predictive analytics, AI is reshaping industries by enabling organizations to leverage vast amounts of data for actionable insights. For instance, another special issue paper discussed the use of AI for the analysis of vast amount of data for generating and reporting software defects [5]. This study reported several benefits such as rea-time analysis and operational efficiency which helped identifying and reducing the failure and errors in a timely manner. While AI-enabled automation offers several benefits, however, its inner working needs to be explained for enhancing stakeholders' trust. This topic was covered in this special issue by another accepted paper discussing the "stakeholder-centric explanations for black-box decisions: an XAI process model and its application to automotive goodwill assessments" [6]. Finally, the fourth paper in this special issue provided a methodology for the planning, implementation, and evaluation of skills intelligence management in the context of informed decisionmaking and adaptability [7]. Additionally, this editorial expands on these papers and draws our attention to one of the most significant advancements in AI which is the emergence of generative AI models, such as GPT and discusses its potential to revolutionize business process management (BPM) [1]. By automating repetitive tasks, generating contextual insights, and facilitating seamless human-machine collaboration, AI-driven technologies are setting the foundation for intelligent business ecosystems. The remainder of this editorial further expands the topic of AI-enabled business process management followed by a discussion of key considerations. It concludes with a future outlook on the role of AI in continuous innovation.Business Process Management (BPM) is central to enterprise efficiency, governing how organizations design, analyze, and optimize workflows. Traditional BPM approaches relied on human expertise and structured methodologies. However, the integration of AI has ushered in a new era of smart BPM, where AI models automate process discovery, enhance workflow optimization, and provide intelligent recommendations.For instance, ProcessGPT [1], an AI-driven BPM framework, leverages generative AI to streamline business processes. By analyzing historical data and learning from domainspecific knowledge, such technologies can generate process flows, identify inefficiencies, and recommend optimization strategies. The implications are profound: AI-powered BPM reduces operational costs, enhances agility, and enables organizations to adapt to evolving market conditions.AI's transformative impact extends to data-centric and knowledge-intensive processes, where decision-making is crucial. AI models can analyze vast datasets, detect patterns, and generate actionable insights, thereby augmenting human expertise. In domains such as finance, healthcare, and supply chain management, AI-driven analytics improve risk assessment, optimize resource allocation, and enhance customer experiences.Moreover, knowledge-intensive industries, such as legal and research-driven enterprises, benefit from AI's ability to process complex information. By integrating AI models with knowledge graphs and semantic reasoning, businesses can enhance decision-ma