Aug 2026· Knowledge and Process Management· 0 citations· 14 references
TL;DR
The findings indicate that GenAI use is widespread in the sample, but integration into business processes is incremental and localized rather than deeply embedded and governance practices are evolving alongside its use.
Abstract
This study examines how organizations integrate generative artificial intelligence (GenAI) into everyday business processes and workflows, and how governance practices are emerging alongside such integration. While prior research has focused on task‐level performance effects or adoption intentions, less evidence examines how GenAI is embedded in organizational workflows and governed in practice. The study adopts an exploratory, descriptive research design based on survey data collected from 120 practitioners across organizations of varying sizes and sectors. The survey captures reported patterns of GenAI use, workflow integration, governance practices, safeguards, and outcomes, and the analysis relies on descriptive statistics and exploratory comparisons across organizational contexts. The findings indicate that GenAI use is widespread in the sample, but integration into business processes is incremental and localized rather than deeply embedded. Many organizations rely on third‐party tools and user‐driven experimentation, while deeper enterprise‐level integration and formal governance arrangements are less frequently reported. The results show uneven development between workflow‐level use, technical embedding, operational transformation, and governance formalization. Operational safeguards appear more common than enterprise‐wide governance structures. Respondents report improvements in efficiency, productivity, and decision‐making alongside concerns about privacy, security, bias, and explainability, indicating experimentation. This study provides cross‐organizational, practitioner‐reported descriptive evidence on how organizations are integrating GenAI into business processes and how governance practices are evolving alongside its use. The findings extend current empirical understanding of how organizations are reporting early GenAI use and governance in practice by moving beyond isolated tasks and adoption intentions to examine workflow‐level integration across organizations.
The study conceptualises governance tensions in GenAI adoption as inherent paradoxes arising from the same mechanisms that create value, and proposes the Assure-Account-Align routine as a candidate bridge between contextual conditions (TOE) and dynamic action (DC), offering a sensitising device for future research rather than a validated framework.
Ginevra Degregori, Davide Calandra, P. Biancone· Management Decision· 0 citations
Generative artificial intelligence (GenAI) is increasingly used in knowledge-intensive work, yet empirical evidence remains limited on how it influences organizational knowledge processes and innovation. Although the rapid diffusion of GenAI technologies has attracted significant attention from researchers and practitioners, there is still a limited understanding of how these tools are integrated into everyday knowledge work and how they contribute to the generation and implementation of innovative ideas. This study examines how knowledge workers in Norwegian organizations use GenAI, the factors that encourage or constrain adoption, and the influence of leadership and organizational culture on its effective use. An exploratory qualitative study was conducted through ten semi-structured interviews with knowledge workers from public- and private-sector organizations. Participants represented a variety of professional roles and organizational contexts, providing insights into both individual and organizational experiences with GenAI. The interview data were analyzed thematically to identify recurring patterns related to knowledge activities, innovation processes, and organizational conditions. The findings show that GenAI supports innovation by facilitating knowledge discovery, synthesis, translation, recombination, idea generation, and concept development. Participants associated its use with faster access to information, accelerated learning, improved problem-solving, and broader exploration of alternatives. Many described GenAI as a valuable cognitive support tool that enhanced creativity and enabled more efficient knowledge sharing across teams. However, they also raised concerns about inaccurate outputs, weak source traceability, privacy, cybersecurity, and excessive reliance on AI-generated content. Additional concerns related to the potential erosion of critical thinking skills and professional expertise if human judgment is not actively maintained. Trust, employee autonomy, and supportive leadership encouraged experimentation, while GDPR compliance, transparency, and professional accountability shaped responsible use. The study conceptualizes GenAI as a conditional knowledge-integration mechanism whose contribution to innovation depends on human validation, organizational learning, and responsible governance. The findings contribute to knowledge management and innovation research by highlighting the organizational capabilities required to harness GenAI while mitigating associated risks
Aurilla Aurelie Bechina Arntzen, Tone Aud Vold, Neno Ruseno· European Conference on Knowl...· 0 citations
Business Process Analytics (BPA) has become increasingly important for improving organizational processes and supporting data-driven decision-making. However, existing Business Process Management and Data Analytics methodologies provide limited support for stakeholder participation, User-Centered Design (UCD), and collaborative implementation, creating barriers for organizations with limited analytical maturity. This study presents MIDA5, an initial methodological framework that integrates BPA, Data Analytics, Business Process Management, UCD, and gamification into a participative implementation methodology. Developed following a Design Science Research approach, MIDA5 comprises five phases, 14 stages, 33 activities, and 61 methodological artifacts. The framework was evaluated through a 3-month-and-12-day organizational case study conducted at a public university involving four core organizational participants, seven organizational stakeholders, and complementary organizational applications. The implementation formalized an undocumented process, integrated heterogeneous data sources, developed an analytical database and ETL workflow, and produced three interactive Power BI dashboards. The analytical solution achieved a System Usability Scale score of 84.2 and a SERVQUAL score of 4.35/5, providing initial evidence of usability, stakeholder acceptance, and organizational applicability. MIDA5 contributes an initial, collaborative, user-centered methodological framework that operationalizes BPA through structured stakeholder participation, standardized artifacts, and iterative organizational validation, while reducing methodological and technological barriers.
Public Application Programming Interfaces (APIs) have become essential com- ponents of digital ecosystems, enabling organizations to access external data and transform it into meaningful information for strategic decision making. While previous studies have primarily emphasized the technical and operational roles of Public APIs, limited attention has been given to their contribution to human centered strategic decision making within digital organizations. This study aims to explore how Public APIs support strategic decision making by enhancing managerial interpretation, organizational responsiveness, and responsible digital innovation while promoting human centered organizational practices. A qualitative multiple case study approach was employed involving organizations operating in data intensive digital environments. Data were collected through semi structured interviews with managers and analysts, complemented by document analysis and limited non participant observation. The collected data were analyzed using thematic analysis to identify recurring patterns related to strategic decision support, organizational adaptability, and human centered technology adoption. The findings demonstrate that Public APIs function not only as technical connectors but also as strategic information resources that strengthen managerial judgment, improve organizational agility, and support evidence based decision making. The study further reveals that integrating external data through Public APIs encourages more transparent, inclusive, and responsible decision making, thereby contributing to organizational resilience and stakeholder value. However, challenges related to data quality, governance, external dependency, and trust remain important considerations for sustainable implementation. This study contributes to the growing body of knowledge on human centered digital transformation by demonstrating how Public APIs can support responsible strategic decision making beyond purely technical applications. The findings offer practical insights for organizations seeking to align data driven innovation with organizational objectives while fostering transparency, inclusiveness, and broader humanistic impact within digital ecosystems.
Fransisca Rahardja· Journal of Orange Technology· 0 citations
Generative artificial intelligence (GenAI) is rapidly transforming organisational knowledge management (KM) processes. Yet limited research has examined how cultural and institutional contexts shape GenAI-enabled KM adoption across developed and developing economies. This study investigates the organisational, cultural, and institutional factors influencing GenAI-enabled KM practices in Jordan and Europe through a mixed-methods approach. Quantitative data were collected from 456 respondents across higher education institutions, SMEs, public-sector organisations and large enterprises. Qualitative data was collected through 36 semi-structured interviews. The findings reveal notable variations in the extent of GenAI adoption, readiness of organisational structures, maturity governance and trust in AI systems across cultures. Structural model analysis showed that technological infrastructure, AI literacy, leadership support, financial resources, and data governance positively affected GenAI adoption. Power distance and uncertainty avoidance negatively moderated adoption effectiveness. Qualitative results revealed that GenAI has high potential for supporting explicit knowledge processes, including externalisation and combination. Tacit knowledge processes such as socialisation remain dependent on people, trust and collaborative organisational culture. The study advances knowledge management theory by extending the SECI model through a GenAI-enhanced perspective that differentiates AI effects across tacit and explicit knowledge conversion processes. The study advances knowledge management theory by: (1) extending the SECI model through a GenAI-enhanced perspective that differentiates AI effects across tacit and explicit knowledge conversion processes—demonstrating process-contingency not previously specified; (2) reconceptualising GenAI capabilities as context-sensitive dynamic capabilities within the Knowledge-Based View, requiring complementary organisational resources; and (3) integrating cultural moderators into AI adoption theory, showing that cultural dimensions function as effectiveness moderators rather than just direct predictors. The study makes a novel contribution to cross-cultural AI adoption literature through a context-based model combining organisational readiness, cultural dimensions, and institutional maturity.
M. Taqatqa, Rami Aljbour· European Conference on Knowl...· 0 citations
As generative AI becomes embedded in knowledge-intensive work, organizations face a central paradox: AI systems produce influential outputs while their inner workings remain opaque. Although explainability research has largely focused on technical methods, less attention has been given to how organizational actors practically make sense of and govern AI-generated knowledge. This study adopts a knowledge-practice perspective to examine how explainability is enacted in everyday work within a mid-sized consulting firm. The study is based on a qualitative case design involving nine semi-structured interviews with AI practitioners, clients, and an AI expert. Rather than treating explainability as a technical property, the analysis explores how consultants interpret, validate, and operationalize AI outputs in the absence of formal governance structures. The findings show that explainability is enacted as a situated and role-dependent knowledge practice. First, employees assess outputs pragmatically, focusing on whether they “make sense” in context rather than seeking insight into model internals. Second, prompting emerges as a form of tacit expertise developed through trial-and-error learning, functioning as an informal mechanism for influencing and interpreting outputs. Third, actors rely on local validation routines such as re-running prompts and cross-checking information to manage uncertainty and maintain trust. Finally, the absence of internal guidelines results in individualized and uneven practices, indicating that knowledge about AI use is created but not institutionalized. From a Knowledge Management (KM) perspective, the study contributes by conceptualizing explainability as an emergent knowledge practice shaped by situated sensemaking, tacit skill development, and informal governance. It extends knowing-in-practice research by illustrating how organizations cope with opaque digital systems when formal knowledge infrastructures lag behind technological adoption. For KM practice, the findings highlight the need for lightweight governance mechanisms, shared prompting norms and role-adapted guidelines that support collective knowledge development around AI use.
Magnus Erga Skreden, Per Olav Eide Svendsen, Eli Hustad· European Conference on Knowl...· 0 citations
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