| Abstract [eng] |
The FMTP is describedin brief: This research explores how resource-constrained DeepTech startups strategically use AI within talent management systems to sustain innovation. Operating in fields such as quantum computing, biotechnology and AI, these firms face ongoing shortages of capital, specialized expertise technological infrastructure and established organizational processes. Against this backdrop, the research investigates how AI is used strategically to attract, develop and coordinate cross-disciplinarity human capital rather than serving as a purely operational support tool. Using a mixed method approach, this research combines an extensive review of academic and industry literature with qualitative interviews involving founders, HR professionals, venture capitalists and accelerator representatives. The analysis was completed in three stages. First, investigate the nature of the resource constraints common to DeepTech startups and show how these constrains encourage phased AI adoption, resilience in the innovation ecosystem and selective use of off-the-shelf AI solutions. Second, it highlights the role of AI in strengthen innovation capacity by accelerating R&D, supporting knowledge integration across disciplines. Third, the findings emphasize the importance of external ecosystems, revealing that coordination gaps among academia, investors, policymakers and incubators often limit effective AI talent integration. The main theoretical contribution of this study is the AI Orchestrated Innovation Capacity Model, which reframes human capital as a dynamic AI-supported capability rather than a strategic resource. Practically, the research offers guidance for founders, HR professionals, investors and policymakers on how deliberate AI use and ecosystem coordination can transform constraints into sustained competitive advantage. Problem, objective and tasks of the FMTP: Problem Although AI is increasingly transforming business functions, there is still little research on how it is used strategically in talent management within DeepTech startups. These startups often have limited resources and operate in specialized, interdisciplinary areas, which makes attracting, developing and retaining key talent especially challenging. Most existing studies focus on traditional tech companies or general AI applications in HR, leaving a gap in understanding how DeepTech ventures apply AI to their specific talent needs. This study aims to fill that gap by exploring how DeepTech startups use AI to support talent acquisition, development and engagement. It also looks at how they manage limited resources, handle ethical and organizational challenges, and adopt AI-driven HR tools to maintain innovation and stay competitive. Objective The object of this research is AI enabled talent management practices in DeepTech startups. Specifically, it focuses on how these ventures operating in fields such as quantum computing, biotechnology and advanced engineering attract, develop and retain interdisciplinary talent through the strategic integration of AI in human resource management. Tasks of the FMTP The primary goal of this research is to examine how DeepTech startups strategically use artificial intelligence (AI) to attract, develop and retain interdisciplinary talent in resource- constrained, innovation-driven environments. The goal will be achieved through the following set of tasks: 1. To analyse how startups address resource constraints when adopting AI-enabled HR strategies. 2. To assess how AI contributes to sustaining innovation capacity in DeepTech ventures. 3. To explore expert insights on how external ecosystem factors influence AI–HR integration and sustain innovation capacity in DeepTech ventures. 4. To develop a conceptual framework linking human capital theory and resource orchestration theory. |
