Challenges in AI Implementation: Perspectives from Practice and Research

Artificial Intelligence (AI) has become an inevitable topic for organizations across various sectors and sizes, offering promising applications as technological accessibility continues to expand. Despite its potential, practical implementation of AI-based Systems remains difficult with particular challenges tied to specific organizational contexts. Often companies invest heavily in AI development but encounter problems such as failure to achieve market readiness of the prototype or the systems struggling to deliver expected benefits. These setbacks often stem from flawed implementation strategies, excessive reliance on technology, or inadequate integration into existing organizational frameworks. Therefore, this paper addresses these challenges encountered at different phases of AI implementation projects. To this end, we initially conduct a Rapid Structured Literature Review (Armitage and Keeble-Allen, 2008), examining the literature on AI implementation cases and associated scholarly reviews. Extending the initial analysis, we experiment with AI driven document analysis as a means to integrate findings of a greater amount of publications into the review. The literature review is subsequently complemented by insights from our own consultancy experiences from the field of AI consultancy. The paper gives an overview of the most salient challenges in AI implementation projects and points out some approaches to mitigate those challenges. From a methodological standpoint it shows that AI driven reviews can yield similar results as conventional reviews, but may lack some explanatory depth. We find that a combination of manual and automated approaches tends to be the most effective strategy.