Improving Project Goal Setting Through AI-Driven Knowledge Management Tools

Abstract

This paper investigates how artificial intelligence (AI)-driven knowledge management (KM) tools might better shape project goal setting. The paper focuses on their capacity to surface relevant organisational knowledge at the earliest and most strategic phase of project planning. While knowledge management (KM) has long been considered as a lever for improving project outcomes, its integration into the goal-setting process remains underdeveloped, particularly in connection with AI-enabled technologies such as large language models (LLMs), natural language processing (NLP), and robotic process automation (RPA). We argue that project goals are not set in isolation. We consider that these are shaped by what an organisation knows, remembers, or forgets. In this context, our author’s team strongly believes that AI-enhanced KM tools might have the potential to highly influence what goals are proposed, prioritised, and formalised. The paper adopts a case study approach, analysing five organisations across manufacturing, insurance, IT, and infrastructure sectors. The selected cases vary in their use of KM systems and AI capabilities, allowing us to compare both traditional and advanced configurations. Using the SECI model (Socialisation, Externalisation, Combination, and Internalisation) just as an useful interpretive framework, we trace how knowledge is captured, transformed, and embedded into planning processes. Our comparative analysis shows that AI enhances the speed, scale, and contextual relevance of knowledge flows, particularly during the Combination and Internalisation phases. The paper also highlights that successful implementation also highly depends on cultural readiness and correspondingly on the capability to properly integrate into the existing planning routines. Findings indicate that AI-KM tools may contribute to improving the planning quality by surfacing overlooked insights, reducing scope drift, and correspondingly by aligning goals with past performance data. However, we consider that these tools cannot replace human judgement or communication and soft skills, mostly concerning negotiation. The core competences and mostly soft skills have an important impact that lies in enabling better-informed conversations between partners involved within negotiations. We conclude that AI-driven KM, when effectively embedded, might turn organisational memory into a usable asset that might be helpful within the strategic decision-making process. We consider that our research might contribute to both knowledge management and project management literature by reasserting the importance of goal setting as a knowledge-intensive process, one that AI might support, but not in an automatic way.