How generative AI will drive enterprise innovation

Objective: This study provides an empirical analysis of the link between Generative AI (GenAI) adoption and business performance in UK firms, with a specific focus on the mediating effects of operational efficiency and product innovation. Methodology: Grounded in the Resource-Based View (RBV) and the Technology-Organization-Environment (TOE) framework, a conceptual model was developed and tested. Data were collected via a cross-sectional survey of 312 senior managers across diverse UK industries. The data were analysed using partial least squares structural equation modeling (PLS-SEM). Findings: The results indicate a significant positive direct relationship between GenAI adoption and business performance. Both operational efficiency and product innovation were found to be significant partial mediators, with product innovation exhibiting a stronger mediating effect. Key drivers of adoption included technological competence, top management support, and competitive pressure, while regulatory uncertainty was a significant barrier. Implications: The findings offer robust evidence for policymakers and business leaders, positioning GenAI not just as a tool for cost reduction but as a strategic lever for growth, primarily through enhanced innovation. The study underscores the need for sustained investment in technological infrastructure and workforce skills. Originality/Value: This research is one of the first large-scale quantitative studies to empirically validate the mediating pathways through which GenAI influences business performance, providing nuanced, context-specific insights for the UK market.

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

Today's commercial sector widely recognises AI and generative AI as catalysts and game-changers. AI and generative AI are empowered to assist organisations in developing new products and can also completely transform commercial processes in an organised way without sacrificing standards and quality. While AI and generative AI are indeed beneficial and efficient, they do come with a few inherent problems. Furthermore, successfully implementing AI and generative AI is a costly endeavor. Therefore, authorities must ensure the cost-effective availability of these technologies, allowing small-scale firms to integrate them into their operations. In general, we are optimistic that this research will provide valuable insights to the whole business community on how to effectively manage their organisations.

Entrepreneurs traditionally use “learning-by-doing” and “learning-by-thinking” as alternative approaches to iteratively build business models for their new ventures. However, both approaches face criticism in how they address novelty and uncertainty, which are crucial to successful entrepreneurship. While generative AI (GenAI) is increasingly used in entrepreneurial tasks, the practices through which it becomes a learning resource for entrepreneurs remain unexplored. Based on a qualitative study, we present a process model that illustrates how entrepreneurs incorporate GenAI into business model design through five resourcing practices. These practices transform GenAI into a valuable resource for facilitating learning during the design process. This approach, which we term “learning-by-conversing,” introduces a generative startup methodology to complement the lean startup model. We distinguish two modes of learning by conversing—reflexive learning and confirmatory learning—based on how novice and experienced entrepreneurs engage with it. By proposing a learning approach that integrates GenAI with entrepreneurial efforts, we bridge the “thinking” versus “doing” debate in business model generation and deepen our understanding of GenAI's role in entrepreneurship.

Generative artificial intelligence has become prevalent in discussions of educational technology, particularly in the context of mathematics education. These AI models can engage in human‐like conversation and generate answers to complex questions in real‐time, with education reports accentuating their potential to make teachers' work more efficient and improve student learning. This paper provides a review of the current literature on generative AI in mathematics education, focusing on four areas: generative AI for mathematics problem‐solving, generative AI for mathematics tutoring and feedback, generative AI to adapt mathematical tasks, and generative AI to assist mathematics teachers in planning. The paper discusses ethical and logistical issues that arise with the application of generative AI in mathematics education, and closes with some observations, recommendations, and future directions.

Generative AI, since 2022 when ChatGPT was released, has been widely used in education. While bringing tremendous benefits to teachers and students, it also poses numerous challenges. Exploring the public's opinions and sentiment towards Generative AI in education will greatly promote their integration. Weibo, as the most important social media in China, offers a wealth of data to analyze public attitudes and viewpoints. In this study, 17,799 weibo posts was crawled for topic modelling and sentiment analysis by using BERTopic algorithm and the Large Language Model DeepSeek respectively. It's suggested that the public mainly focus on seven topics related with Generative AI in education, including AI-driven teaching innovation and digital transformation, applications of AI in academic research and the resulting changes in academic publishing, applications and experiences of AI in language education, risks, challenges of using GenAI in education and responses, feedback and complaints after using AI, the development of artificial intelligence major and related fields as well as the impact of AI on the job market and responses. Nearly half users embrace this new technology for its convenience, while some people demonstrate different attitudes. They worry such risks as the job loss, plagiarism, over-reliance and so on. At the end this study, several suggestions are put forward to tackle those negative sentiments to promote the the deep integration of Generative AI in education. Meanwhile, the limitation of this study was also proposed.

This study investigates Indonesian EFL learners’ perceptions of generative AI (GenAI) applications compared to their perceptions of human tutors in an online test preparation course. Semi-structured interviews with 11 participants who had participated in practice with both a GenAI tool and a human revealed that the GenAI tools were valued for their efficiency, objectivity, and detailed feedback, particularly in pause detection and word count analysis. However, learners highlighted the importance of human tutors for personalized, adaptive, and in-depth feedback. They saw GenAI as providing consistent and rapid scoring, but human tutors as fostering a more supportive learning environment through individualized guidance. Many participants favored a hybrid approach, integrating GenAI’s analytical precision with human interaction to enhance test preparation and real-world speaking skills. Notably, AI was generally trusted more for assessment consistency, whereas human tutors were preferred for instructional support. These findings suggest that for EFL learners in a test preparation course a combination of GenAI and human tutors may be the most satisfying.

Purpose This paper presents a comprehensive overview of existing academic research on the application of Generative AI (Gen-AI) and ChatGPT within agricultural supply chains, highlighting current trends, leading publishers, thematic areas and proposing a future research agenda. Design/methodology/approach A bibliometric analysis was conducted using VOSviewer software on a sample of 79 articles retrieved from the Web of Science (WoS) and Scopus databases to identify emerging research topics. Findings The number of publications in this area has increased, and since 2023, academic interest in the application of Gen-AI and ChatGPT to agricultural supply chains has shown a consistent upward trend. Key areas of focus include enhancing prediction accuracy, coordination, automation, employee performance, optimizing supply chain management and improving overall business outcomes. However, concerns also exist, like hallucination, data privacy, security risks, lack of accuracy, copyright issues and ethical implications. Consequently, the risk of ungoverned Gen-AI outputs without human oversight potentially disrupting operations underscores the need for robust governance frameworks to ensure its reliable integration in agricultural contexts. Practical implications Gen-AI and ChatGPT provide several advantages for agricultural supply chains. They enhance forecasting capabilities and coordination among partners, automate stakeholder interactions to increase efficiency, reduce manual tasks to improve employee performance and support data-driven decision-making through advanced analytics, ultimately strengthening supply chain management. Originality/value This study offers a structured and in-depth examination of the Gen-AI and ChatGPT phenomenon in agricultural supply chains. Moreover, it contributes to the literature by identifying existing research gaps and suggesting potential directions for future investigation.

The purpose of this article is to demonstrate the pedagogical application of Generative AI (GenAI) to a particularly fruitful area of Homer, the speeches, drawing on narratology as a theoretical framework to contextualise the use of the technology. The teaching methodology, students interrogating a chatbot to explore a speech from Homer, comprises dialogic learning as students craft questions, reflect and respond to the chatbot’s responses. This reiterative process is demonstrated through dialogue with Microsoft Copilot on one speech from the Iliad , book 16, where Achilles chides a tearful Patroclus ( Il . 16.7–19), and one from the Odyssey , book 19, where Odysseus rebukes the treacherous maid Melantho ( Od . 19.71–88). Two different strategies were deployed to highlight the response patterns of GenAI. With the Iliad , the strategy was to ask Copilot questions directly about the speech; with the Odyssey , Copilot was asked to assume the role of a character from the exchange. It was found that Copilot supported a narratological interpretation of the text by offering students an informed, and largely accurate, window on the speech for them to explore key considerations such as focalisation, the viewpoint on the unfolding narrative. Furthermore, while Copilot provided a rich layered response, there was still space for students to negotiate the meaning of the text further, retaining their own responsibility as active learners. The conclusion is that GenAI is in line with an inquiry-based approach to the study of Homer that promises to engage students and keep the discipline fresh.

Teachers' and students' perceptions of AI-generated concept explanations: Implications for integrating generative AI in computer science education

Cyberattack sophistication is rapidly increasing, leading to a decline in the effectiveness of traditional signature-based security solutions. Consequently, intelligent and adaptive security solutions are now being employed that are powered by generative artificial intelligence. Numerous review papers have been conducted on these solutions, but they focus on particular aspects of cybersecurity, providing not a holistic view of all areas. This paper attempts to investigate the applications of generative AI in cyberdefense by reviewing 113 academic papers clustered according to cyberdefense domains indirectly referring to such generative AI models. The review illustrated generative AI’s effectiveness in transferring security from the reactive security model to a proactive one by greatly increasing the detection of new threats and inducing synthetic data which enhances the quality of training, with numerous papers achieving performance accuracy exceeding 95%. There are uniterable issues to overcome however, including the computing costs, model instability and reliance on data. A new multi-dimensional taxonomical methodology and comparative analysis was undertaken on the various generative AI architectures with a strategic roadmap given to guide and orientate further research towards the adoption of autonomous, effective, resilient and reliable cyberdefenses.

Contemporary advanced business services and products increasingly incorporate artificial intelligence components, such as chatbots and generative AI, across various domains. These are delivered through dedicated, complex, innovative software programs and projects initiated by virtually all industry sectors. Complex project management include challenges such as: the complexity of product backlogs with a number of requirements, highly dynamic changes in customer expectations impacting product backlog quality and requirements engineering, labour shortages, advanced tool adoption and automation, predictability of deliveries, insufficient transparency of processes applied to product backlog management, communication barriers between business and project teams. The primary objective of this paper is to address a key research gap related to the insufficient quality of product backlog management in complex agile software project environments. The paper addresses the research question regarding the potential application of chatbots and generative AI as a methodology to conduct reliable agile product backlog evaluation and subsequently enhance refinement processes through dedicated Prompt Engineering Language (PEL). This paper emphasizes the importance of a structured description of product backlog items and its impact on the overall quality of agile product backlog and delivered software products. Following the literature review, the author's empirical research presents a detailed analysis of the research gap and focuses on applying chatbot and generative AI solutions to evaluate agile product backlog items and to improve related agile refinement processes. Research results demonstrate that agile product backlog refinement processes can be supported by chatbots and generative AI utilizing dedicated Prompt Engineering Language (PEL). These tools are not designed to create business value directly but rather enhance the efficiency and automation of product backlog management processes to respond rapidly to stakeholder expectations within agile environments, ultimately achieving superior project outcomes. Nevertheless, numerous challenges must be addressed, particularly related to AI governance and compliance with numerous policies including data privacy, intellectual property, legal, security and internal ones.

This study aims to produce empirical insights into how students in Chinese higher vocational colleges view and use generative AI in EFL learning. This quantitative study uses the technology acceptance model (TAM) to analyze EFL learners' attitudes, intentions, and behaviors about utilizing generative AI in EFL learning with Structural Equation Modelling. 911 English as a foreign language(EFL) learners completed a questionnaire measuring personal innovativeness, perceived ease of use, perceived usefulness, attitude, behavioral intention, and actual use. Structural equation modeling investigations show that Perceived Ease of Use does not directly predict learners' attitudes but can impact them through the full mediator Perceived Usefulness. Positive attitudes towards generative AI and personal innovativeness are associated with increased behavioral intention. Behavior intention predicts the actual application of generative AI in English learning. This study demonstrates the effectiveness of generative AI as a language-learning tool for EFL learners, promoting personalized and sustainable learning.

Generative AI has emerged as a transformative technology capable of reshaping public sector operations by streamlining administrative tasks, enhancing data analytics, and driving more personalized citizen interactions. Despite its potential, a critical gap remains in understanding how generative AI can be effectively integrated into government processes, particularly in developing countries like Indonesia, where bureaucratic inefficiencies and infrastructural constraints persist. This study addresses this gap by conducting a mixed-method investigation that combines a Systematic Literature Review (SLR) of generative AI applications across multiple sectors to inform public service innovation, with expert interviews from key stakeholders Indonesian government, academic, and industry. The findings indicate that generative AI holds substantial promise for reducing manual processes, improving inter-agency coordination, and fostering data-driven decision-making. However, challenges related to ethical considerations, data privacy, and limited digital infrastructure present significant barriers to widespread adoption. By proposing a governance framework and offering strategic recommendations tailored to the Indonesian context, this research provides actionable insights for policymakers, government agencies, and technology providers. In doing so, it contributes to the evolving discourse on AI-driven public service innovation and offers a pathway toward realizing a more efficient and citizen-focused smart government in Indonesia.

The widespread application of digital technology has triggered profound changes in the field of education, promoting the innovation and transformation of the education model, and international organizations have deeply discussed and given creative suggestions on sensitive topics such as when and how artificial intelligence can be used in education. This paper focuses on the latest norms proposed by UNESCO, the OECD and the EU on the application of AI in education, and compares and analyses them in four dimensions, namely, focusing on the perspective, actual influence, degree of innovation, and focusing on preventing risks. Lastly, the international organizations provide useful references and guidance for the specific field of "generative AI", which covering teacher training, educational content standardization, data protection and ethical concerns. The comparison of the education policy recommendations of the three international organizations aims to help governments sort out the similarities and differences between the starting points, concerns and goals of different international organizations, and then clarify the relevance and feasibility of their recommendations.