Volume 5 (2026-27)

As artificial intelligence (AI) agents assume the information-routing functions that have defined organisational hierarchy since the Roman Army, the traditional organisational chart is giving way to a new form: an hourglass. In this emerging structure, a broad base of agents handles execution, a concentrated top layer of humans exercises judgment over strategy, ethics, accountability, and relationships, and a thinner, transformed middle focuses less on coordination and more on governance, culture, and capability building. This paper argues that most organisations are currently running redundant human and agent coordination layers in parallel, rather than redesigning around the new reality. It shows why automating entry-level analytical work threatens the traditional expertise pipeline through which junior employees developed judgment via repetition, feedback, and tacit learning. The paper proposes two remedies: reverse apprenticeship, in which juniors review agent output under senior guidance; and simulated experience, in which AI generates high-volume, realistic practice scenarios. Drawing on three emerging organisational models, the paper explains how companies can redesign management layers while deliberately preserving the cultural transmission and talent development functions that middle management historically performed. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: agentic AI; orchestrating agents; organisational chart; AI leadership; human AI orchestrator. future-of-work

Human-in-the-loop (HITL) mechanisms are commonly presented as a primary safeguard for managing risk in artificial intelligence (AI) and automated decision systems deployed in organisational and workplace settings. The presence of human review is often assumed to ensure accountability, explainability, and safety. This paper argues that HITL is not a control by itself, but a design choice whose effectiveness depends on governance, authority, incentives, and cognitive conditions. In practice, human oversight frequently fails to meaningfully reduce risk, particularly in high-volume, high-impact, or time-sensitive environments. The paper examines the structural and organisational factors that undermine effective human review, including automation bias, information asymmetry, scale mismatch, and accountability diffusion. It concludes by outlining the governance conditions required for human involvement to function as an effective risk control in AI-enabled systems. The paper reframes the role of humans from downstream approvers of automated outputs to upstream decision makers responsible for governing where, how, and under what conditions automation should be applied. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: AI governance; human-in-the-loop; automation bias; AI risk management; operational controls; responsible AI; model risk

While artificial intelligence (AI) holds transformative potential for developing economies, significant structural barriers impede its successful adoption across Africa, Latin America, and Asia. As of 2023, only 30 per cent of developing nations have established AI regulations, a regulatory gap compounded by deficient infrastructure and severe talent brain drain. Consequently, the regions that could benefit most from AI remain the least equipped to deploy it safely. This paper examines both the systemic challenges and successful frameworks of AI implementation in the Global South. Prominent case studies demonstrate that combining local adaptation with strategic policy and public–private partnerships yields tangible advancements in critical sectors. Examples include Egypt’s National AI Strategy 2030, Honduras’s blockchain and AI-driven coffee traceability via Harvverse, and agricultural optimisation through projects such as Agrobots and Biorom. Furthermore, AquaIntelligence facilitates community water management, supported by international tech transfers and digital capacity building from organisations such as the United Nations Climate Technology Centre and Network and the Deutsche Gesellschaft für Internationale Zusammenarbeit. Conversely, the study highlights the risks of unregulated AI deployment, which threatens to exacerbate socio-economic inequalities, displace labour, and institutionalise algorithmic bias, thereby favouring resourceful elites while marginalising vulnerable populations. To mitigate these risks, this paper argues that governments must approach AI adoption primarily as a governance challenge rather than a technological one. Synthesising these insights, the core framework proposes three urgent pillars for sustainable digital transformation: expanding equitable access to high-quality data, scaling robust digital education, and designing inclusive policies that distribute technological dividends across all societal strata. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: agrifood Innovation; AI governance; artificial intelligence; developing countries; digital inclusion; Global South; sustainable development

The rapid evolution of generative artificial intelligence (AI) presents significant evaluation challenges. As large language models are increasingly exposed to misinformation, synthetic content, and imperfect web-scale training data, grounding model outputs in trusted, verifiable contexts has become essential. Conventional benchmarking based on static datasets does not adequately capture factual grounding and contextual relevance in operational settings. To address these limitations, the European Broadcasting Union adopted a use-case-driven framework focused on retrieval-augmented generation (RAG) for public service media (PSM). The paper examines how curated, high-quality PSM archives can anchor model behaviour and improve factual reliability. Four contributions are presented. First, the paper describes a practical RAG architecture covering query formulation, document chunking, vector retrieval, reranking, and contextual generation, with attention to reranking strategies that constrain model improvisation. Second, it proposes a multilayered evaluation framework combining information retrieval metrics, factual consistency checks, answer coverage assessment, and expert human judgement. Third, it introduces a smart writing assistant that uses trusted context to support journalistic ideation and drafting while preserving full editorial control. Finally, it explores bounded agentic workflows in which an additional layer intervenes only when retrieval quality is insufficient. Together, these components show how trusted corpora, transparent evaluation, and controlled automation can improve reliability while keeping journalists in command of the editorial process. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: generative AI; LLM evaluation; retrieval-augmented generation; factual grounding; agentic AI; journalism; public service media

This paper offers a comprehensive analysis of the evolving importance of sustainability data in the real estate sector, highlighting the challenges posed by fragmented systems, complex processes, and inconsistent data management. It argues that, historically viewed as a burden, sustainability data is now recognised as a strategic asset capable of enhancing investment performance, fostering innovation, and creating long-term value. The study examines the deficiencies of legacy systems, siloed datasets, and lack of standardisation, alongside regulatory pressures such as the EU Sustainable Finance Disclosure Regulation, the UK Sustainability Disclosure Requirements, and the Task Force on Climate-related Financial Disclosures, which demand greater transparency. Effective governance of sustainability data is shown to be a key differentiator for better decision making and risk mitigation. The paper identifies eight transformative trends, including centralised data systems, advanced metrics, and the integration of financial and non-financial key performance indicators. It highlights real-world initiatives such as automated data collection and digital logbooks that narrow the gap between sustainability ambitions and operational realities. The intersection of sustainability data and artificial intelligence (AI) is explored, emphasising the necessity of trustworthy governance for effective AI-driven decisions. The authors call for industry leaders to develop robust data governance models, advocating a shift from mere compliance towards strategic value creation, positioning sustainability data as a catalyst for future growth and sector transformation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: sustainability; real estate; investment decisions; data governance; artificial intelligence; data assets; agentic AI

As supply chains become increasingly complex and consumer-centric, the demand for intelligent, adaptable, and scalable technology has significantly increased. This paper examines the impact of artificial intelligence (AI) technologies such as predictive analytics, dynamic routing, automated sorting, and real-time tracking on material handling and logistics operations. The study identifies significant themes and trends in innovation across academic and industrial sectors through a comprehensive literature evaluation and bibliometric analysis. The main results show that using AI in service-based supply networks improves their ability to grow, work more efficiently, and make decisions quickly. Case studies from leading logistics platforms demonstrate how AI can enhance process responsiveness, reduce human labour, and optimise resource use. The research has tangible implications for supply chain managers and governments seeking to implement AI-driven solutions to optimise future logistics. The findings serve as a foundation for further research on enhancing the intelligence of supply networks. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: artificial intelligence; material handling; supply chain optimisation; logistics technology; predictive analytics; warehouse automation; scalable systems

Artificial intelligence (AI) is reshaping creative work in advertising agencies, yet most research focuses on performance metrics. This paper examines how AI integration alters creative development processes and value dynamics for agencies and clients. It draws on 22 semi-structured interviews with senior agency and client-side professionals in Germany. Using a hybrid inductive–deductive content analysis and a phase model derived from it, the study explores how AI tools are embedded across the major phases of briefing, research, ideation, internal review, client presentation, and feedback. Findings show that AI significantly accelerates research, idea generation, and content drafting, increasing efficiency and expanding creative exploration; however, the fundamental structure of the creative workflow remains stable. Rather than replacing established processes, AI intensifies them. While agencies benefit from faster iteration and data-driven insights, clients increasingly expect quicker turnaround times and lower costs. At the same time, concerns about generic outputs, brand misalignment, and legal risks require human oversight. The study demonstrates that AI simultaneously enables value co-creation and co-destruction through enhanced knowledge integration and risks value erosion through rising expectations and quality tensions. Transformation therefore occurs less through structural disruption than through accelerated processes and shifting client–agency dynamics. These findings provide practical insights for managing hybrid human–AI creative workflows in service-based industries. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: artificial intelligence; AI; advertising agencies; creative development workflow; value co-creation; VCC; value co-destruction; VCD

This paper critically reviews Kotter’s classical 8-Step Change Model in the context of artificial intelligence (AI) adoption within business schools. It introduces the AI Adoption Change Model as a more suitable framework, emphasising the emergent, decentralised, and iterative nature of AI integration. Using survey data from the Association to Advance Collegiate Schools of Business, research from the Wharton Human–AI Research Center, and case studies from several leading institutions — including the University of Colorado, Northeastern University, and Frankfurt School — the study compares the traditional top-down approach with success patterns observed in practice. Findings reveal that AI adoption in business education often defies Kotter’s assumptions of pre-selected change agents and hierarchical implementation. Instead, successful cases involve self-motivated faculty experimentation, community-driven knowledge sharing, and iterative scaling. Data indicate a gap between enthusiasm at the dean level and actual implementation, with only a minority of institutions requiring AI training. The paper concludes that fostering grassroots innovation and community practices, rather than top-down mandates, is crucial for sustainable AI integration. The proposed AI Adoption Change Model offers academics a more effective framework, emphasising exploration, organic champions, and institutional capacity building over compliance, with implications applicable to broader knowledge-intensive organisations seeking ethical and sustainable AI integration. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: AI adoption; change management; business education; Kotter model; organisational change; AI implementation strategy