From AI hype to workflow reality: a strategic framework for integrating generative AI across organizational functions

This article explores the transformative potential of generative artificial intelligence in enhancing enterprise security and controls. As organizations confront increasingly sophisticated cyber threats, traditional reactive security measures prove insufficient against adaptive adversaries. Generative AI offers a paradigm shift by leveraging advanced machine learning algorithms to understand normal system behaviors, predict potential attack vectors, and respond autonomously to emerging threats. The article examines how generative AI enhances security through proactive threat detection, behavioral analysis, anomaly detection, and real-time threat intelligence. It delves into the transformation of core security processes, including automated vulnerability assessment and adaptive authentication. The article highlights generative AI's capability to simulate attacks through graph-based modeling and adversarial training, enabling organizations to identify and remediate vulnerabilities before exploitation. While acknowledging significant implementation challenges related to data privacy, model security, algorithmic transparency, and regulatory compliance, the article provides a strategic adoption framework with case studies demonstrating successful implementations in financial services and healthcare sectors, offering a roadmap for organizations seeking to leverage generative AI for enhanced security postures.

The rapid evolution of Generative AI has opened up numerous possibilities to enhance organizational decision-making through improved operational efficiency, innovation, and data analysis accuracy. However, companies in developing countries face challenges such as underdeveloped digital infrastructure, labor shortages, data security risks, and regulatory uncertainty. These factors create a gap between theoretical benefits and practical implementation. This study explores opportunities, key risks, and organizational readiness for Generative AI adoption in business decision-making within developing countries. Using a mixed-method sequential design, phase I involved a quantitative survey of 120 companies across five nations, while phase II included interviews with 15 key informants. Data were analyzed using Structural Equation Modeling and NVivo, with interactive visualization aiding in framework development. Findings indicate that Generative AI could improve decision-making efficiency by 89% and reduce operational costs by 15%. Top risks include data security (score: 4.41) and regulatory ambiguity (score: 4.27). Organizational readiness was high in managerial support (score: 4.01) but low in internal regulation (score: 3.67). The proposed Strategic Generative AI Adoption Framework integrates innovation diffusion, risk management, and readiness, offering practical insights for adaptive AI adoption in developing countries.

Generative-AI (GenAI) rose from boutique research to mass hype in less than three years, but most firms still fail to monetize proofs-of-concept into production worth. Recent surveys show that 69 % of enterprise GenAI initiatives stall before being operationalized while 46 % of PoC initiatives are abandoned outright and Gartner forecasts a 30% abandonment rate by the end of 2025. We contend that a primary cause of this attrition is continued application of classical product-management heuristics—tuned to deterministic feature work—when GenAI problems are probabilistic, socio-technical, and risk-weighted. Drawing on observations from past twelve enterprise GenAI launches (2023-2025), and 25 semi-structured interviews with product managers at multiple firms, we (i) clarify where classical discovery, sizing, and prioritization practices go wrong; and (ii) distill a three-part decision playbook comprising a Three-Gate Decision Funnel, Six-Point Opportunity Scorecard, and Value-Feasibility Prioritization Matrix. Early adopters report cycle-time compression by up to 40%, with an average of 33% and doubled conversion rates from PoC to production after the playbook's use. The paper positions these artefacts as a practitioner-oriented handbook and research-based contribution to the nascent field of GenAI product strategy.

This study evaluates the role of Generative AI in optimizing digital supply chain performance, focusing on IoT integration, predictive analytics, and blockchain security. The primary objective is to determine which AI-driven initiatives offer the greatest benefits in enhancing resilience and operational efficiency. A structured multi-criteria decision-making approach is applied using the ELECTRE III method, leveraging quantitative data from DHL’s operational records (2022–2025). The evaluation is conducted with a panel of 18 industry experts, including logistics professionals and AI specialists, who participated in structured interviews and expert assessments to establish weighting criteria and performance metrics. Findings indicate that IoT-driven real-time tracking and predictive analytics for maintenance rank highest in enhancing supply chain resilience, improving operational responsiveness, and reducing downtime. Additionally, blockchain-supported security mechanisms reinforce data integrity and transparency, strengthening logistics security. Conversely, OCR-based automation and NLP-powered logistics systems demonstrate comparatively lower impact, emphasizing the need for targeted AI adoption strategies. This study contributes to structured AI evaluation methodologies by establishing a repeatable decision-making framework, ensuring scalability beyond DHL’s logistics operations. Limitations include the reliance on industry-specific datasets, which require further validation across diverse supply chain environments.

Objectives: Professionals and practitioners in food science & technology navigate a minefield of challenges stemming from the convergence of scientific inquiry and research, and mass and social media. Escalating skepticism and erosion of trust in science is exacerbated by poorly conducted science, plagiarism, inadequate peer review, predatory publishing, misrepresentation of science in the media, and public perceptions of science that are shaped by politicization and mis- and disinformation. Methods: Key search terms (science, food science & technology, nutrition) were crosslinked with search terms that describe challenges undermining trust in science (media, mis/disinformation, skepticism, hype, generative AI, credibility, politicization, etc.). Over 200 articles covering social media impacts on scientific credibility, the evolution of science & peer review, the rapidly changing rules governing scientific output in academia and industry and codes of ethics meant to govern how professionals work, particularly in the context of food science, food technology, and nutritional science. Results: Contradictory 'facts' presented in mass and social media generate distrust in scientific discoveries. Leveraging the comprehensive literature review, a strategic framework was defined that 1) identifies and manages factors that challenge integrity and credibility of food research, and 2) prescribes strategies that allow professionals to mitigate and manage challenges in this complex space in order to provide credible research results in food & nutritional science and food technologies. Evidence supports a compelling need for strict adherence to common codes of ethics when conducting, reporting and communicating research results in academia and public forums. Conclusions: Tools and a framework for professionals identifies the intersection of factors that contribute to erosion of trust and highlights challenges as they relate to perceived loss of integrity or credibility in food science and technology. Funding Sources: N/A.

Generative AI technologies, such as GANs and Transformer-based models, are transforming healthcare and cybersecurity. In healthcare, they improve medical imaging, diagnostics, and personalized treatments, enhancing patient outcomes and operational efficiency. In cybersecurity, generative AI strengthens defenses through real-time threat detection, anomaly identification, and synthetic data generation for secure testing, tackling modern cyber threats. Both fields, however, face challenges in data quality, ethics, transparency, and regulation. Addressing these requires domain-specific frameworks like the Technology Acceptance Model (TAM) in healthcare and Zero Trust Architecture (ZTA) in cybersecurity. This chapter explores generative AI's impact, highlighting challenges, tailored solutions, and strategic frameworks to ensure ethical and operational effectiveness. As AI evolves, it stands as a cornerstone for progress in both fields, balancing innovation with responsibility.

This article examines the evolving dynamics between artificial intelligence and human creativity in organizational settings. Drawing on recent empirical research, particularly meta-analyses of generative AI models like GPT-3.5 and GPT-4, the evidence reveals a nuanced relationship where AI demonstrates moderate advantages over humans in certain creative domains while also introducing potential constraints. Organizations face both opportunities and challenges: AI can enhance ideation quantity, but may reduce diversity of ideas without proper intervention. The research highlights promising pathways for effective human-AI creative collaboration, including optimized prompting techniques, complementary team structures, and strategic implementation frameworks. As generative AI becomes increasingly integrated into creative workflows, organizations that understand these dynamics and implement evidence-based practices for human-AI collaboration will gain significant competitive advantages in innovation processes.

Generative AI can fabricate advanced scientific visualizations: ethical implications and strategic mitigation framework

The integration of generative AI into financial advisory services marks a significant advancement in portfolio optimization, risk assessment, and decision support and recent developments in large language models (LLMs), such as ChatGPT, have demonstrated the ability to process both structured financial data and unstructured market sentiment, enhancing the accuracy and adaptability of investment recommendations. However, the application of generative AI in robo-advisory systems presents ethical, regulatory, and psychological challenges and this study conducts a systematic literature review to examine the technological benefits of AI-driven financial advisory, while also addressing concerns related to algorithmic bias, explainability, and user trust. The review applies a TOWS-based strategic framework to analyze strengths, weaknesses, opportunities, and threats (SWOT) in the adoption of AI-enhanced robo-advisors. Findings consequentially indicate that explainable AI (XAI) and hybrid AI-human oversight models are critical for mitigating transparency concerns and algorithm aversion. While real-time data processing improves investment insights, the black-box nature of generative AI remains a key barrier to regulatory compliance and consumer adoption. Additionally, regulatory fragmentation across jurisdictions complicates AI governance, necessitating adaptive compliance strategies and cross-border cooperation. The research further highlights that financial literacy and trust-building mechanisms, including user-centric onboarding and transparent risk assessments, are essential for overcoming psychological resistance to algorithmic decision-making. In conclusion, the paper proposes an approach for integrating generative AI into robo-advisory systems, combining advanced financial analytics, XAI, human oversight, and ethical AI governance. Future research should focus on empirical evaluations of hybrid advisory models, regulatory harmonization, and AI-driven financial education tools to ensure responsible adoption. These findings contribute to the growing discourse on sustainable and user-centric AI deployment in financial services, providing strategic recommendations for industry practitioners and policymakers.

This article examines the transformative impact of Generative AI (GenAI) on organizational structures and operational paradigms across industries. As AI capabilities advance at an unprecedented rate, forward-thinking leaders must develop strategic frameworks to integrate these technologies effectively. The article explores the accelerated evolution of AI from basic pattern recognition to sophisticated systems capable of solving complex problems, and its omnipresence across diverse sectors including transportation, consumer technology, energy, manufacturing, and logistics. It identifies prime opportunities for AI integration within technical and non-technical domains, revealing how highly skilled professionals often spend substantial time on repetitive tasks that could be automated. The research outlines a new operational paradigm where human expertise is channeled into strategic activities while AI handles routine cognitive tasks, creating powerful synergies when properly structured. The article concludes with a comprehensive seven-step implementation framework for organizations seeking to become AI-powered entities, emphasizing the importance of workflow audits, strategic opportunity identification, robust infrastructure, governance frameworks, organizational AI literacy, impact measurement, and continuous iteration. This approach creates a sustainable foundation for leveraging both human and artificial intelligence in complementary ways.

The Fourth Industrial Revolution (4IR), marked by automation and robotization, significantly influences sustainable economic development and policies. Key aspects include job displacement and job creation, and the evolving nature of work, necessitating reskilling, upskilling and cross-skilling. Addressing these challenges requires strategic frameworks and empowering young adults, the future workforce, through education and awareness. This paper explores the impact of the 4IR on the labour market, particularly its effect on the nature of work, skills, lifelong learning, and labour law. It examines how young adults, especially the graduate and final year students at the University of Osijek, perceive these changes and their readiness to encounter them. The study aims to understand their perceptions, attitudes, and beliefs about the 4IR’s transformative effects on the labour market. This is crucial as they represent the emerging workforce, which will be impacted by automation, robotization, and other technological shifts. The purpose of the study is to gain deeper insight into how young adults perceive these changes in general, but also in terms of their future jobs and necessary skill pool. The research involved a survey (Lime Survey, SPSS) based on a critical review of literature in the 4IR, automation, robotization, and labour market changes. It assessed the perceptions, attitudes, and preparedness of young adults regarding the 4IR’s impact on the labour market. Findings indicate that young adults generally anticipate job losses due to automation and robotization but also expect new jobs creation. They are largely willing to engage in lifelong learning and to adopt new skills in order to adapt to a dynamic labour market. The study gains relevance in light of the unexpected rise of generative AI (GAI), which introduces more profound changes and challenges, and creates a context that allows for a comparison between the current and recent economic and labour disruptions.

This article examines the transformative impact of Generative Artificial Intelligence (GenAI) on the financial services industry, with a particular focus on strategic implementation and operational transformation in the banking sector. Through a comprehensive analysis of current implementations and strategic initiatives, particularly in North American banks, the article explores how GenAI is revolutionizing traditional banking operations, from automated knowledge management to personalized customer services. The article investigates the multifaceted approach institutions are taking toward AI integration, including investments in infrastructure, talent development, and risk management protocols. The article reveals that successful AI implementation requires a balanced approach between innovation and risk mitigation, with institutions strategically deploying AI solutions across various operational domains. This article contributes to the growing body of literature on digital transformation in financial services by providing a structured framework for understanding the strategic implications of GenAI adoption and its role in shaping the future of banking operations. The article concludes by identifying critical success factors for AI implementation and suggesting future research directions in this rapidly evolving field.

This chapter delves into the transformative impact of AI-powered personalization within the retail sector, emphasizing its critical role in fostering deeper customer loyalty, enhancing engagement, and streamlining operations. It examines how retailers leverage machine learning (ML), natural language processing (NLP), and Generative AI (Gen AI) to analyze vast datasets and deliver loyalty-building personalized experiences across omnichannel platforms. The chapter features case studies of leading brands like Amazon, Starbucks, Sephora, and Walmart, showcasing how AI-driven loyalty initiatives, from tailored product recommendations to personalized customer service and anticipatory inventory management, strengthen customer relationships and drive repeat business. By exploring emerging trends such as hyper-personalization, immersive loyalty experiences, and AI integration with AR/VR technologies, the chapter presents a strategic framework for retailers looking to harness AI's power to build lasting customer loyalty and competitive advantage in today's dynamic retail landscape.

Purpose. The study aims to conduct a comprehensive examination of current trends in digital content marketing and the effectiveness of various communication channels in both B2B (business-to-business) and B2C (business-to-consumer) segments during the period 2023–2025. The research seeks to identify key factors that determine the success of modern content marketing strategies and to evaluate the growing influence of audio and interactive communication formats on audience engagement and brand perception. Given the rapidly evolving digital environment and the impact of post-pandemic and wartime realities, the study also explores how companies adapt their content strategies to maintain competitiveness and audience trust. Design/methodology/approach. The research applies a mixed-methods design combining qualitative and quantitative approaches. Secondary data analysis was conducted based on statistical reports, publications of leading marketing agencies, and industry-specific studies to capture global and regional trends. Comparative analysis of communication channel usage in B2B and B2C contexts was implemented to identify differences in strategic approaches and target audience behavior. Statistical data were processed and visualized using tables reveal dynamics and correlations. A SWOT analysis was further applied to assess strengths, weaknesses, opportunities, and threats associated with digital content marketing implementation, providing a strategic framework for interpreting empirical results. Findings. Social media remains the primary content marketing channel, though its dominance is gradually declining in favor of audio and interactive channels; company websites remain important for basic online brand presence but are decreasing in relative use; audio platforms, such as podcasts and spoken explainers, are rapidly growing in importance, particularly for B2B, where their role increased nearly sixfold; content marketing strategies differ between B2B and B2C: B2B focuses on LinkedIn, Twitter (X), and YouTube with educational content, case studies, webinars, and analytics, while B2C prioritizes Instagram, TikTok, and Facebook with emotional and visual formats, including video, stories, and influencer marketing. Practical implications. The study provides actionable insights for businesses to optimize content marketing strategies, improve audience engagement, increase conversion rates, and strengthen customer loyalty. Emphasis on emerging trends such as generative AI, personalization, and audio content can enhance marketing efficiency and competitiveness. Originality/value. This study offers an original contribution to the understanding of content marketing transformation in the context of global digitalization, post-pandemic recovery, and ongoing geopolitical instability. By emphasizing the expanding role of audio and interactive communication, it underscores the necessity for B2B and B2C organizations to strategically adapt their marketing approaches to maintain relevance, authenticity, and competitiveness in an increasingly fragmented media landscape.

Ethnopharmacology explores bioactive compounds rooted in traditional medical knowledge systems and holds immense promise for drug discovery, cultural preservation, and healthcare innovation. However, fragmented documentation, minimal digitization, and limited integration with biomedical frameworks remain major barriers. The advent of generative artificial intelligence (GenAI), including large language models (LLMs) and molecular generation algorithms, offers transformative solutions to these challenges. This narrative review critically examines the application of GenAI in ethnopharmacology and highlights its role in digitizing traditional knowledge, decoding polyherbal formulations, predicting herb-drug interactions, and accelerating phytopharmaceutical discovery. It synthesizes current literature on GenAI tools and methods relevant to ethnopharmacology, considering natural language processing, knowledge graph construction, molecular modeling, and multimodal data integration. A five-phase strategic framework is proposed for the ethical and effective implementation of GenAI. This review narrates real-world applications from Asian (Ayurvedic, Chinese, Japanese, Thai, Vietnamese), African, and Indigenous American medicines systems demonstrate adaptability across cultures. Stakeholder-specific benefits, spanning academia, healthcare, industry, and indigenous communities, are also discussed, along with methodological innovations and ethical considerations. GenAI offers a significant transition in ethnopharmacology by integrating traditional knowledge systems with advanced computational tools to develop inclusive data-driven innovation across global traditional medicine systems.