The vast majority of AI proof-of-concept projects fail to reach production due to digital boundaries, digital employees, and bad data, according to Capgemini research. Addressing these challenges requires a fundamental rethinking of how businesses approach AI adoption.
Data quality is a major obstacle: Many organizations have become accustomed to working with subpar data, which poses significant risks as AI increasingly drives business decisions:
Developing digital operating models is crucial: To successfully adopt AI, businesses must establish clear digital boundaries and define the problems AI should and should not address:
Organizational change is key to scaling AI: The main challenge in moving from proof-of-concept to full-scale AI adoption lies in business adoption and management, rather than technology:
Broader Implications: The low success rate of AI proof-of-concept projects transitioning to production highlights the need for businesses to radically rethink their approach to AI adoption. Rather than focusing solely on the technology, organizations must prioritize data quality, establish clear digital boundaries, and drive the necessary organizational changes to scale AI effectively. Failure to address these challenges will hinder businesses from realizing the full potential of AI and risk falling behind in an increasingly AI-driven world.