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:
- By 2030, AI is expected to make 50% of business decisions, particularly in autonomous supply chain applications, making poor data quality unacceptable from a risk perspective.
- Digital employees cannot wait for cleaned-up data to make operational decisions, as this is not feasible in real-world scenarios such as autonomous vehicles or warehouses.
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:
- This involves specifying which data should and should not drive AI decisions, as well as what AI should and should not influence within the business context.
- AI solutions will be constrained by their functions within the organization, with different departments having distinct rules, regulations, and motivations for their AI applications.
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:
- Businesses must approach AI adoption from the perspective of automating and examining their business models, not just incrementally integrating AI into existing processes.
- Data architecture for AI needs to be fundamentally different, with data being front and center where digital employees can use it in real-time to complete tasks accurately and effectively.
- Organizations must enable their employees to successfully rely on AI in their careers, which requires a significant shift in mindset and organizational structure.
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.
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