Sep 2026· VINE Journal of Information and Knowledge Management Systems· 0 citations· 79 references
TL;DR
The findings of this study show AI supports KT by reducing cognitive load, validating with science, encouraging peer learning and creating feedback loops for improvement, and shows how AI can change professional roles and bridge knowledge gaps among actors.
Abstract
This paper aims to examine how artificial intelligence (AI) can facilitate knowledge translation (KT) within the accountant–small and medium-sized enterprises (SMEs) business ecosystem, addressing the widening gap resulting from the decline in compliance-based accounting services and SMEs’ growing need for accessible strategic advisory support.
This study uses an interventionist research approach to examine the development and implementation of the Strategy Revolution platform as an AI-enabled KT system. The platform incorporates the Strategic Footprint methodology, mapping firms across 71 strategic variables, supported by AI-driven analytics and a professional community. Empirical evidence is gathered from pilot implementations involving accountants and SMEs, combining qualitative data (interviews, observations and questionnaires) with quantitative platform-generated data.
The findings of this study show AI supports KT by reducing cognitive load, validating with science, encouraging peer learning and creating feedback loops for improvement. The platform fosters ongoing knowledge sharing, shifts accountants from compliance to strategic advisors and boosts collective competitiveness. As a qualitative and exploratory study, these results are interpretive and do not confirm economic or performance benefits.
This paper enhances knowledge management by framing AI-enabled platforms as ecosystem-level infrastructures for knowledge sharing. It introduces algorithmic mediation in KT processes and shows how AI can change professional roles and bridge knowledge gaps among actors. While much literature on generative AI in knowledge work views AI as a productivity aid, this study uniquely theorizes AI as a KT infrastructure at the inter-organizational ecosystem level. Algorithmic mediation is seen as a fourth translation mode, complementing Carlile’s (2004) syntactic, semantic and pragmatic types.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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