2,418,089 research outputs found
AI strategy to execution
No full textIn this book, we discuss the “strategy to the execution gap” a leader of an organization encounters while adopting Artificial Intelligence (AI) in that organization. The main focus is on value creation using AI and use of AI as competitive strategy. Although every organization across various industries is interested in integrating Artificial Intelligence into their business, a significant dilemma is the right AI strategy for their organization
Human-AI Collaboration in Academic Writing: towards a Synergy Model and A Case to Include AI as a Co-Author
No full textAs generative AI systems such as ChatGPT and Gemini 2.5 become increasingly integrated into academic workflows, the question of their legitimacy, limitations, and potential in scholarly writing has become urgent. This paper presents a reflexive case study of a sustained collaboration between a domain expert in consciousness studies and Gemini 2.5, culminating in the co-authorship of a peer-reviewed research article. By analyzing exactly 37,440 words of recorded interactions, we identify patterns of synergy, including recursive refinement, conceptual amplification, and accelerated manuscript development. We argue that when guided by a knowledgeable human author, AI can act as a cognitive partner rather than a passive tool—amplifying scholarly creativity and improving efficiency without compromising academic rigor. The case supports a '1+1=3' synergy model for co-authorship, in which human steering and AI fluency converge to produce novel insights and polished output faster and more effectively than either could achieve alone. The findings advocate for a paradigm shift from prohibitive policies to the responsible, expert-guided integration of AI in academic research and writing, grounded in transparency and accountability, and present arguments for why the AI tool should be listed as a co-author despite current injunctions against such practice
How AI can help your company set a budget
No full textAI has been heralded — and put to use — as a groundbreaking new tool that companies can use in the budgeting process. But even companies that have embraced AI are still struggling with aspects of the budgeting process in today’s complex and rapidly changing business environment. Why is that? When does it make sense to rely on AI, and when does it not? In this article, the authors describe experiments they have conducted on the use of AI in the budgeting process — and conclude that AI can and should replace human managers in tactical tasks, where data-driven decision-making leads to faster and more efficient outcomes, but that in the strategic realm, where long-term planning, market adaptability, and business foresight are critical, human involvement and insight remain indispensable
How finance teams can succeed with AI
No full textResearchers at Vlerick Business School’s Centre for Financial Leadership and Digital Transformation have worked with CFOs and finance leaders to understand how finance can lead in the AI era. Their latest study, building on a 2023 digital-maturity diagnostic published in MIT Sloan Management Review, combines survey responses from over 100 senior finance executives with company-level data to explore what enables finance teams to become strategic business partners in an AI-driven world. The findings reveal that although AI adoption is widespread, its impact is often limited by organizational misalignment, digital overload, and the challenge of integrating new technologies without overwhelming human decision-making. The real obstacle turns out to be not the technology itself but whether finance teams are structured to absorb and apply it effectively
Urban AI Guide
No full textThe URBAN AI GUIDE aids city leaders and urban technologists (academic, public, private, and community-focused) in better understanding how artificial intelligence operates in urban contexts.
The idea for this guide arose from conversations with city leaders, who were confronted with new technologies, like artificial intelligence, as a means of solving complex urban problems, but who felt they lacked the background knowledge to properly engage with and evaluate the solutions. In some instances, this knowledge gap produced a barrier to project implementation or led to unintended project outcomes.
The guide begins with a literature review, presenting the state of the art in research on urban artificial intelligence. It then diagrams and describes an "urban AI anatomy," outlining and explaining the components that make up an urban AI system. Insights from experts in the Urban AI community enrich this section, illuminating considerations involved in each component. Finally, the guide concludes with an in-depth examination of three case studies: water meter lifecycle in Winnipeg, Canada, curb digitization and planning in Los Angeles, USA, and air quality monitoring in Vilnius, Lithuania. Collectively, the case studies highlight the diversity of ways in which artificial intelligence can be operationalized in urban contexts, as well as the steps and requirements necessary to implement an urban AI project.
Visit https://urbanai.fr/our-works/urban-ai-guide/ to learn more about the project
Marking intersubjectivity in human-written and AI-generated editorials published in "Il Foglio"
No full textIn March 2025, the Milan-based broadsheet Il Foglio launched Il Foglio AI, a month-long experiment featuring a daily four-page supplement entirely generated by large language models (LLMs). Owing to the success of the experiment, the project has continued as a weekly feature since April 2025. Each edition of Il Foglio AI contains around 25 articles spanning diverse journalistic genres, including editorials, which form the focus of the present analysis. The paper compares human-written and LLM-generated editorials from Il Foglio and Il Foglio AI, examining the use of authorial stance markers to analyze how intersubjective positionings are conveyed. To this end, the study draws on Martin and White’s (2005) taxonomy of four “engagement” meanings typically expressed by markers of intersubjectivity. The analysis is particularly relevant for the description of AI-generated texts as a new textual typology, as LLMs lack experiential grounding and cannot hold attitudes, beliefs, or judgments. The dataset comprises two subcorpora of 25 editorials each, published between April and May 2025 in Il Foglio and Il Foglio AI
Uncanny Semantics. How AI and Human Authors Use Language Differently in Academic Writing
No full textThis study explores the semantic differences between human-written and AI-generated academic texts by applying word embedding techniques to a curated corpus of 325 introductions from linguistic articles. The corpus includes human-authored texts and AI-generated texts produced by six language models (OpenAI, Google, and DeepSeek; base and advanced). Each topic was prompted in two different ways: plain and academic. Using cosine similarity, the most frequently occurring lemmas were grouped into semantic categories. The analysis reveals that AI-generated texts, especially under academic prompts, overuse positive-evaluative and methodological vocabulary (e.g., central, crucial, analysis, methodology) and explicitly refer to text structure more often than the plainly prompted texts (e.g., section, chapter). In contrast, human authors employ more epistemically cautious, critical, evaluative, and connective language (e.g., possibly, inconsistent, by no means). I propose that the relative absence of such epistemic markers in AI texts, combined with their tendency to exaggerate the importance of certain topics or data, reflects a pattern of pseudo-commitment: the models produce syntactically assertive, formally academic prose but only weakly modulate epistemic stance and critical engagement, which may contribute to the reported sense of weirdness in AI-generated academic writing
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
AI-PRISM Project Launch Press Release
No full textThe consortium of AI-PRISM (AI-Powered human-centred Robot Interactions for Smart Manufacturing) is pleased to announce the launch of this joint initiative, granted by the European Commission under the Horizon Europe programme
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