Why so many businesses are still on the wrong side of the AI divide (www.techradar.com)

Despite significant investments in AI, many businesses are facing an "AI paradox" where expected benefits remain elusive. Research from MIT and McKinsey revealed that 95% of enterprise AI projects have failed, largely due to companies not properly aligning their data management strategies with AI capabilities. Many organizations prioritize AI-driven innovation without first establishing a robust data and technology foundation, leading to fragmented, siloed datasets that hinder performance. Successful integration of AI requires a critical focus on data-readiness and workflow orchestration. For instance, the property and casualty insurance sector has struggled with slow claims processing due to disparate data management. However, insurers that embedded AI across their operations—coordinating data quality and workflow efficiency—saw drastic improvements, reducing claims cycles from weeks to days. This highlights the necessity of a unified data backbone and collaborative AI agents across business functions, illustrating that achieving transformative AI outcomes demands a strategic approach to data management and integration rather than merely deploying advanced technologies.