Adoption of agentic AI slowed by data readiness and governance gaps

Agentic AI is emerging as a new stage of enterprise automation, enabling systems to reason, plan, and act across workflows. Adoption, however, remains uneven, with far fewer organisations scaling deployments beyond pilots.

Unlike traditional analytics or generative tools, agentic systems make decisions rather than simply producing insights. Without sufficient context, they struggle to align actions with real business conditions, revealing a persistent context gap.

Recent survey data highlights this disconnect. Although executives express confidence in AI ambitions, significant shares cite data readiness, infrastructure, and skills as barriers. Many identify AI as central to strategy, yet only a limited proportion tie deployments to measurable business outcomes.

Effective agentic AI depends on layered data foundations. Public data provides baseline capability, organisational data enables operational competence, and third-party context supports differentiation. Weak governance or integration can undermine autonomy at scale.

Enterprises that align data governance, enrichment, and AI oversight are more likely to scale beyond pilots. Progress depends less on model sophistication than on trusted data foundations that support transparency and measurable outcomes.

Would you like to learn more about AI, tech, and digital diplomacy? If so, ask our Diplo chatbot!