Jul 2026· Development and Learning in Organizations: an international journal· pp. 1-4· 0 citations· 4 references
TL;DR
A strategic approach to use of GenAI, including the support of top management, incorporation of the principles of a “learning organization,” consultation of key stakeholders and appropriate choice of initial content development, will enhance the likelihood of success.
Abstract
Adoption of generative artificial intelligence (GenAI) technologies has spread rapidly, including in many HR departments. This paper discusses a strategic approach toward implementing Gen AI into learning and development (L&D) programs and courses.
This is a “viewpoint” paper from the perspective of two authors, one an academic who researches the impact of AI on HR, the other an executive coach and corporate learning and development consultant with years of experience as a chief learning officer.
L&D programs are ultimately about capturing and enhancing organizational knowledge toward gaining competitive advantage. A strategic approach to use of GenAI, including the support of top management, incorporation of the principles of a “learning organization,” consultation of key stakeholders and appropriate choice of initial content development, will enhance the likelihood of success.
Appropriate GenAI implementation will address employee engagement and improve the firm’s learning culture.
Not all GenAI adoptions have been successful. Organizations that do not take a strategic approach may end up with a failed or suboptimal outcome in their L&D GenAI projects in comparison to those who do.
Artificial intelligence (AI) is changing how educational institutions interact with donors, alumni and stakeholders. While early applications focused on automating routine tasks, the emergence of generative AI (GenAI) and large language models (LLMs) has introduced a new paradigm: AI as a strategic thought partner. This paper explores how GenAI can enhance leadership, creativity and decision making within university advancement, drawing on personal experience during a large-scale university merger and the applied use of multiple LLMs — including Microsoft Copilot, Claude and ChatGPT — as strategic tools. It presents principles of effective prompt design and use of ‘personas’ that enable advancement professionals to collaborate productively with AI and proposes a values-led advancement AI governance framework to support ethical and responsible use. The discussion highlights both opportunities and risks — emphasising the importance of human review, data privacy and the need for advancement-specific guidelines. The paper concludes that GenAI, when implemented with clear guardrails and institutional integrity, has the potential to elevate advancement from an operational function to a strategic, mission-aligned partner in shaping the future of higher education. This paper is also included in The Business & Management Collection, which can be accessed at https://hstalks.com/business/.
Liz Hawkins· Journal of Education Advance...· 0 citations
This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.
Giedrius Čyras, Vita Marytė Janušauskienė· International Scientific Con...· 1 citation
Artificial intelligence (AI) is reshaping work and human resource management, yet existing reviews largely treat competencies as secondary outcomes of AI adoption and offer limited theory-driven integration of how competency management itself is transforming. This study addresses that gap by systematically examining how competency management has evolved in AI-enabled contexts, how dominant theories explain AI-driven competency change, and where those theories require extension. Using a PRISMA 2020-guided systematic review of 187 Scopus-indexed journal articles, this study combines bibliometric mapping (keyword co-occurrence, temporal overlay, and bibliographic coupling) with directed qualitative content analysis to link research fronts with underlying theoretical mechanisms. The findings show that AI-related competency change extends beyond technical skills toward hybrid and portfolio-based configurations that integrate technical understanding, managerial judgment, learning agility, governance capabilities, and psychological readiness. The analysis demonstrates that no single framework sufficiently explains these shifts. Human Capital Theory, the Resource-Based View, and Dynamic Capabilities each illuminate partial mechanisms, while complementary perspectives from HRD, socio-technical systems, organizational economics, and psychology are needed to account for task contingency, human-AI complementarity, structural redesign, and employee readiness. The study contributes a theory synthesis that re-conceptualizes competency management as a dynamic, multi-level, and socio-technical system. It offers implications for designing adaptive competency architectures, aligning HRD interventions with AI-enabled work systems, and embedding governance capabilities within workforce development strategies.
The use of Generative Artificial Intelligence (GenAI) is deeply impacting all Knowledge Management (KM) processes. In particular, due to its ability to generate new content in different forms, the new technology is deemed capable of deeply transforming the knowledge creation process, which is considered the highest and most impactful stage of KM processes. Despite this, a comprehensive understanding of how companies can leverage GenAI to create organizational knowledge is lacking, both empirically and theoretically. Regarding the latter, scholars have recently underlined that prior research has yet to focus on the transformation of the SECI model and Ba theory, the most widely used conceptual frameworks for interpreting the organizational knowledge creation process, in the era of human-intelligence interaction. However, in the last two years, some studies have examined whether the SECI model needs to be revised in light of GenAI. Based on a review of the 19 systematically identified articles on Scopus, the present paper identifies, discusses, and compares the three different conceptual approaches adopted by scholars in dealing with the topic in question: a) applying the SECI model in its original version; b) adapting the original SECI model with small adjustments; c) developing a new SECI-based model. The paper compares the three approaches, highlighting how they assume different notions of the role of GenAI in the knowledge creation process and the types of knowledge involved. The academic and practical implications that arise from the study are discussed in the conclusions.
E. Scarso, K. Kirchner· European Conference on Knowl...· 0 citations
Generative artificial intelligence (GenAI) has moved from experimental novelty to a central input in organizational decision-making, with McKinsey's Q1 2026 Global AI Survey finding that 65 percent of organizations now use generative AI in at least one business function, roughly double the adoption rate reported ten months earlier, and 72 percent report at least one AI workload in production. Despite this scale of adoption, the empirical record on business value remains sharply divided. This paper synthesizes recent academic and industry evidence, drawing on 24 sources published primarily between 2023 and 2026, to examine three interlocking questions: what theoretical frameworks currently explain GenAI's role in managerial and strategic decision-making, how enterprises are applying GenAI in practice across functional areas, and what structural challenges limit the translation of GenAI adoption into measurable business value. The paper synthesizes evidence from strategic management research on AI-assisted evaluation of business alternatives, organizational theory on GenAI's emerging roles in decision processes, and empirical field studies on ambiguity handling and sycophantic behavior in AI-generated business advice, alongside a widely cited 2025 MIT study finding that 95 percent of enterprise generative AI pilots fail to deliver measurable profit-and-loss impact. Findings indicate that GenAI functions most reliably as an augmentation tool that aggregates and structures diverse inputs for human judgment, rather than as an autonomous decision-maker, that single-model evaluations of strategic alternatives are frequently inconsistent and biased while aggregated multi-model evaluations approximate expert human judgment, and that the primary barrier to enterprise value is organizational and workflow integration rather than model capability. The paper concludes with a proposed decision-integration framework and implications for executives, AI governance functions, and researchers.
M.SANGEETHA, Una Suman Kumar Patro, K.ARPITHA et al.· International journal of com...· 0 citations
This study examines agentic artificial intelligence (AI) as “digital employees” and extends learning & development (L&D) frameworks to include their training and ethical alignment.
A conceptual, theory-integrative approach is adopted, drawing on socio-technical systems theory and the resource-based view (RBV), supported by evidence such as bias on Airbnb.
Agentic AI systems act as organizational participants whose learning shapes decisions. Reframing their learning as an L&D responsibility requires core L&D activities to be rethought for non-human learners. Without governance, these systems may amplify bias.
The study is conceptual and lacks empirical validation. Future research should test AI-focused L&D models and examine human-AI collaboration.
The study can be used to implement structured AI training, align systems with ethical norms, and strengthen governance for responsible AI use in organizations.
The paper reframes agentic AI as part of the workforce and extends L&D to include artificial agents, clarifying what the L&D function specifically gains from this perspective.
Jyoti Arya, Anand Hindolia· Development and Learning in...· 0 citations
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