Digital transformation and artificial intelligence are reshaping how organizations develop capabilities, make decisions, innovate, and pursue sustainable performance. However, existing research largely examines digital transformation, Dynamic Capabilities, AI capability, organizational intelligence, and sustainability through fragmented theoretical perspectives, providing limited explanation of how these domains connect within an integrated organizational pathway. This conceptual paper addresses this gap by developing an integrated theoretical framework linking Strategic Digital Transformation, Dynamic Capabilities, AI-Enabled Organizational Capability, AI-Enabled Organizational Intelligence, and Sustainable Enterprise Performance. Drawing on the Dynamic Capabilities perspective and AI capability literature, the framework positions AI as an organizational capability pathway rather than an isolated technological driver. It conceptualizes Data & Technology, People & Skills, and Organization & Processes as mutually reinforcing dimensions of AI-Enabled Organizational Capability, supporting AI-enabled decision-making and innovation as mechanisms of organizational intelligence. Five theoretical propositions articulate the proposed relationships and explain how capability development connects strategic transformation with economic, environmental, and social performance. The framework contributes an integrated theoretical pathway for explaining how organizations can translate digital and AI capabilities into intelligent and sustainable enterprise outcomes
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
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
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026