Agentic AI Boom: Data is Key to Scaling

Worldwide AI spending is anticipated to reach $2.5 trillion in 2026, marking a significant 44% increase year-over-year. The agentic AI market alone is expected to soar to $8.5 billion by the end of 2026 and nearly $40 billion by 2030. Currently, organizations utilize an average of 12 AI agents, a number projected to grow by 67% within two years.

However, scaling these AI agents presents considerable hurdles. Research indicates that nearly two-thirds of enterprises have experimented with agents, but fewer than 10% have successfully scaled them to deliver measurable value. The foremost obstacle identified by eight in ten companies is poor data quality and data limitations.

To effectively scale agentic AI, businesses must prioritize a robust data foundation. This includes modernizing data architecture, enhancing data quality, and advancing operating models. Two emerging usage models, single-agent and multi-agent workflows, both critically depend on a steady flow of high-quality, trusted data to automate complex business processes accurately.

McKinsey outlines four coordinated steps to build strong foundational data capabilities, connecting strategy, technology, and people. Identifying critical workflows, mapping end-to-end processes, and defining clear metrics are vital. Ultimately, access to high-quality data, along with data lineage and standardization, will serve as a strategic differentiator in the agentic AI era, with governance becoming paramount as systems scale.