Scaling AI Agents with Trustworthy Data Foundations
Data infrastructure is critical to unlocking the full potential of AI agents in enterprises.
1The rise of AI agents
Organizations are rapidly adopting AI agents, recognizing their potential to transform work processes. However, many struggle to achieve the desired ROI from AI investments, primarily due to inadequate data infrastructure and access limitations. Agentic AI places significant new demands on enterprise data systems. The shift from answering questions to taking actions requires agents to access all structured and unstructured data across the enterprise with proper business context.
2The data access gap
Surveyed organizations provide AI systems access to only 45% of company data on average. Among 'data laggards,' this drops to 30% or less. In contrast, 'data leaders' ensure access to over 70% of their data, achieving greater success with AI agents. This disparity directly impacts AI effectiveness, demonstrating how data accessibility determines agent performance. The leaders' approach shows that comprehensive data access is foundational for AI success.
3Decision reliability and data readiness
Only about half of surveyed organizations trust their AI agents' decisions to be accurate and relevant. Data leaders show 100% trust in agent decisions, proving that reliable AI requires a solid data foundation. The trust in AI decisions largely reflects data readiness levels. Data quality and proper context directly influence decision accuracy, making data infrastructure improvements crucial for building confidence in agentic AI systems.
4Scale and speed challenges
Two-thirds of data laggards (66%) report that legacy systems limit AI agent scaling, while 68% say these systems prevent fast decision-making. Data leaders have mostly overcome these constraints, with only 8% reporting similar limitations. This contrast highlights the decisive role of modern data infrastructure in AI performance. Legacy systems clearly hinder organizations from realizing the full potential of agentic AI.
5Time pressure and expectations
All respondents plan to use agentic AI within two years, with 69% aiming for widespread adoption. If Gartner's prediction that AI will augment or automate 50% of business decisions by 2027 proves accurate, organizations must urgently address data bottlenecks. Without removing system constraints, agentic AI will fail to deliver promised speed and efficiency gains. The clock is ticking for enterprises to modernize their data estates.
6Top improvement priorities
For all respondents, the most important scaling initiative is improving AI agents' access to both structured and unstructured data. Enhancing data and AI governance with business context also ranks high. Data leaders additionally prioritize automating data management processes. These focus areas reveal critical requirements for effective agentic AI implementation. Addressing these priorities systematically can significantly boost AI agent performance.
7Lessons from data leaders
Data leaders provide a roadmap for creating optimal data environments where agents can thrive. These organizations implement comprehensive strategies to expand data access, improve quality, and integrate business context. They also minimize human intervention by automating data management processes. These practices substantially enhance AI agent performance, offering valuable insights for organizations embarking on their agentic AI journeys.
8Preparing for the future
As AI agents become more embedded in operations, overcoming legacy system limitations grows increasingly urgent. Enterprises must take immediate steps to make their data estates agent-ready. Improvements in data access, context, and governance are critical to unlocking AI's full potential. Organizations that fail to make this transformation risk falling behind in the digital race, while those that succeed will gain significant competitive advantage through AI-powered decision-making.