Abstract

Innovation projects involving emerging technologies often face uncertainty not only about technical solutions but also about the users and beneficiaries of the innovation. While traditional project management approaches often assume that the target audience is clearly defined from the outset, in practice this assumption is frequently challenged as new insights emerge during the project lifecycle. This study examines the dynamics and implications of redefining the target audience during the development of an artificial intelligence (AI)-based solution in a public innovation context, highlighting how such changes shape project strategy, scope, and outcomes. The goal of this study is to explore how shifts in the intended user base affect project governance, stakeholder engagement, and value realization. Specifically, it asks: How does a change in the target audience during a project’s development reshape its direction, management approach, and impact? To analyze this phenomenon, the study draws on Engeström’s Expansive Learning framework, which interprets stakeholder reframing as collective learning triggered by contradictions emerging during project development.

The project, conducted in collaboration with a national public organization supporting micro and small businesses, initially aimed to develop a digital tool that would directly support entrepreneurs in their decision-making. With no predetermined solution, the project team adopted a Design Thinking approach, using iterative cycles of user research, ideation, prototyping, and testing to shape the artifact. Early workshops and interviews suggested that entrepreneurs would be the primary users. However, iterative evaluation and engagement revealed a critical insight: the greatest value of the AI solution lay in empowering consultants, who interpret and contextualize information and serve as the interface between the organization and its clients. This shift represented a fundamental design pivot, reframing the project’s objectives, user requirements, governance mechanisms, and success criteria. Drawing on data from co-creation sessions, interviews, prototype iterations, and feedback analysis, we trace the non-linear evolution of the project and analyze how stakeholder redefinition influenced decision-making, resource allocation, and the solution design. These insights are shaped by public-sector innovation and consultant-mediated service delivery. Learning and value were assessed through changes in problem framing and perceived utility.

Preliminary findings suggest that changes in the target audience are not merely tactical adjustments but potential strategic turning points that shape project direction and value creation. This study aims to offer three main contributions. First, it explores how redefining stakeholders can function as a learning mechanism in project management, supporting teams as they adapt scope and strategies when new insights emerge. Second, it examines how user-driven design pivots may influence stakeholder engagement and success criteria, highlighting the importance of flexible governance and communication. Third, it discusses how iterative, human-centered approaches such as Design Thinking can help project teams navigate uncertainty, question early assumptions, and identify new sources of value. Taken together, these emerging insights seek to expand our understanding of project dynamics in AI-enabled innovation and to provide guidance for managing evolving stakeholder needs.

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