Introduction:

Opportunity identification (OI) is a fundamental process in innovation and strategic decision-making across various industries and fields. It enables businesses, policymakers, and other stakeholders to recognize potential areas for development, innovation, and value creation. Traditionally, OI has relied on expert-driven assessments, structured brainstorming sessions, and industry best practices. While these methods have proven valuable, they are often constrained by cognitive biases, siloed thinking, and a lack of real-time data integration, which can lead to missed opportunities and inefficiencies.

Recent advancements in artificial intelligence (AI) offer new possibilities for enhancing the OI process by providing intelligent, data-driven insights, facilitating stakeholder collaboration, and fostering creativity. AI-driven tools, such as machine learning algorithms, natural language processing, and generative AI models, can analyze vast amounts of data, identify patterns, and generate novel ideas that human teams might overlook. Additionally, AI can support real-time data integration, enabling stakeholders to make informed decisions based on the latest trends, technological advancements, and societal needs.

This master thesis aims to explore and understand AI-assisted OI processes by analyzing creativity workflows and stakeholder workshop scenarios. The research will focus on developing generalizable frameworks and methodologies for AI-assisted ideation and decision-making. Toward the end of the thesis, the findings can be contextualized specifically for the circular economy, providing insights into how AI-driven OI approaches can accelerate sustainable innovation.

Research Objectives:

This thesis focuses on the exploration OI processes and methods in order to develop AI-assisted approaches for OI in the field of circular economy. The following research objectives guide the study:

• Development and Analysis of OI Workshop Scenarios: This research will design and evaluate different workshop scenarios involving diverse stakeholder groups, including industry representatives, policymakers, local businesses, researchers, and community members. The goal is to analyze how different stakeholder compositions influence the opportunity identification process and determine the most effective configurations for fostering innovation (within the circular economy context). In addition, the extent to which AI can support with the workshop scenarios, idea generation, and decision-making should be addressed and analyzed.
• Comparative Study of Creativity Workflows: A structured analysis of various creativity workflows used in OI will be conducted. Based on a literature review, OI standards, best practices and methodologies such as design thinking, lateral thinking and open innovation will be elaborated and (separately and or in combination) assessed for their effectiveness. The study will also examine how AI can support and enhance these workflows. The research will compare these workflows and propose a framework for integrating AI-driven ideation techniques into the OI process.

The general findings from the study can then by applied and contextualized to the circular economy domain in order to identify how AI-assisted opportunity identification approaches can address key challenges in transitioning to a circular economy. (Based on the analysis, practical recommendations will be developed for stakeholders in the circular economy to implement AI-supported opportunity identification processes effectively.)

Methodology:

Various methods (individually and in combination) can be used in the Master’s thesis to achieve its objectives. For example, the following methodological approaches are conceivable:

• Literature Review: A comprehensive review of creativity workflows, stakeholder engagement strategies, and AI applications in opportunity identification and circular economy innovation. This will provide the theoretical foundation for developing workshop scenarios and AI-assisted creativity workflows.
• Design Thinking: This user-centered methodology will be used to design and refine workshop scenarios and AI-assisted creativity workflows. It will involve iterative feedback loops with stakeholders to ensure the tools and processes align with their needs and foster innovation.
• Design Science Research (DSR): A structured approach to developing and evaluating the AI-based assistant. This will involve problem identification, iterative design, prototype development, and evaluation to ensure the AI assistant effectively supports opportunity identification in circular economy workshops.

Expected Contributions:

This thesis will provide valuable insights into the role of AI in facilitating creativity and opportunity identification. The expected contributions include:

• A systematic comparison of creativity workflows for OI.
• A set of OI workshop scenarios with varying stakeholder compositions.
• An evaluation framework to assess the effectiveness of AI-assisted creativity workflows and stakeholder workshop scenarios in fostering opportunity identification.