Why agentic AI pilots stall – and how to fix them (www.techradar.com)

Agentic AI, a form of autonomous artificial intelligence that can reason and make decisions across workflows, is emerging as a potentially transformative tool for businesses. However, many organizations are finding their initial pilot projects to be ineffective, with soaring costs and unmet expectations. The core issue lies in the failure to establish the necessary strategic framework, infrastructure, and data quality prior to implementation. With 80% to 90% of enterprise data being unstructured, accessing comprehensive information is critical for agentic AI to operate effectively. Without this foundation, these systems risk operating on incomplete data, leading to poor decision-making and chaos within organizations. Successful adoption of agentic AI requires a well-defined business objective, robust infrastructure, and strong governance to ensure data quality and ethical oversight. Early adopters are advised to focus on gradual implementation, integrating human oversight to maintain accountability and trust as they scale autonomy. As agentic AI evolves, it promises to connect disparate systems and workflows, enhancing business productivity significantly. The journey towards realizing its full potential will involve aligning strategy, modernizing data management systems, and fostering transparency to build trust among users—a crucial transition that could redefine enterprise technology for years to come.