Scaling agentic AI demands a strong data foundation - 4 steps to take first (www.zdnet.com)

A recent report highlights the critical role of high-quality data as organizations strive to scale agentic AI, which refers to AI systems capable of autonomously executing complex tasks. With global spending on AI projected to hit $2.5 trillion by 2026 and the agentic AI market expected to reach $8.5 billion, the momentum for adopting AI agents is accelerating. However, the report points out that most enterprises have struggled to transition from experimentation to measurable value, primarily due to poor data quality, with 80% of companies citing data limitations as a major barrier. To successfully implement agentic AI, organizations must modernize their data architecture and ensure consistent data quality before assigning workflows to AI agents. McKinsey outlines four essential steps: identifying suitable workflows, modernizing data architecture for interoperability, establishing high data quality standards, and building strong governance models. The report emphasizes that establishing a robust data foundation is vital not only for scalability but also for maintaining trust and effectiveness in AI operations. Thus, as businesses embark on this journey towards autonomy through AI, they must prioritize their data strategies to gain a competitive edge in the evolving landscape of agentic AI.