Achieving the “AI sweet spot”: Balancing feasibility, viability, and desirability in Artificial Intelligence Implementation - Kölmel, Bernhard; Brugger, Tanja; Pekmezci, Metehan; Pereira, Christian; Bulander, Rebecca; Volz, Raphael (International Journal of Economics, Business and Management Studies, 2025)
This paper presents the “AI Sweet Spot” framework for systematically identifying Artificial Intelligence (AI) initiatives that balance technical feasibility, commercial viability, and user desirability, addressing the challenge of turning ambitious AI concepts into sustainable, high-impact solutions. A structured literature review of innovation management and AI deployment studies was combined with analyses of industry best-practice case studies. Insights were synthesized through the Innovation Sweet Spot lens to form a three-dimensional model, with design-thinking methods embedded to keep user desirability central. Critical success factors include robust data governance, rapid prototyping, cross-functional collaboration, and executive sponsorship. Recurring pitfalls involve siloed decision-making, underinvestment in user research, and misaligned performance metrics. Case studies show design-thinking interventions surface user pain points early, improving adoption and reducing wasted effort. A balanced view of feasibility, viability, and desirability is essential for AI project success. The “AI Sweet Spot” framework provides a roadmap for assessing trade-offs, prioritizing high-value opportunities, and mitigating technical, commercial, and human-centered risks. Practitioners can use the framework’s diagnostic questions and prioritization criteria to evaluate use cases, allocate resources strategically, and establish governance processes that enable responsible, scalable, and user-centric AI deployments.