The Importance of Data Foundations for AI Agents

The rise of AI is transforming enterprise value, with global spending on AI projected to rise dramatically in the coming years. This investment is fundamentally driven by businesses demanding innovation and the pressure to transform the ever-increasing volume of generated data into actionable, AI-driven value. The ultimate goal is clear: to enable more intelligent, data-driven decisions across all business functions.

Companies are generating more data than ever, with pressure to transform this data into actionable insights and AI-driven value driving tech investment. Yet, 80% of enterprise knowledge is hidden in unstructured data, such as documents, PDFs, and images. Cloud platforms therefore need to rapidly evolve into unified, AI-first ecosystems to unlock more intelligent, data-driven decisions across all business functions.

As business objectives surrounding AI and Agentic AI systems rocket, an urgent problem has emerged: many current data stacks are not ready for agents.

The Need for Context

For AI to drive value and impact in an enterprise setting, it has a paramount requirement: broad context. Without comprehensive, trustworthy data, an AI agent cannot function reliably and will inevitably struggle with hallucination, slow data retrieval times and inconsistent outputs. The current fragmentation in data platforms is rapidly becoming a significant blocker to realizing enterprise value from AI investments.

Specifically, the weaknesses in legacy data systems directly translate into five main challenges in data readiness and therefore, AI agent performance:

• Data is Not Served Effectively to Consumers: Slow to deliver business & AI-driven insights and unable to effectively support diverse users, from business analysts to data scientists, in operationalizing AI at scale.
• Failure to Identify a Single Source of Truth: Disparate or siloed data with lack of trust in data leading to inconsistent insight & hallucinated AI outputs.
• Solution is Not Scalable: High cost of legacy  infrastructure maintenance and deployment, coupled with the demands of AI workloads and real-time analytics, make it difficult to scale effectively.
• Difficulty Migrating from Legacy: Businesses struggle to migrate from legacy systems to cloud-native, AI-ready architectures, hindering the adoption of open standards and unified data platforms. For example, the traditional migration approach takes too long due to complex planning involved as well as manual code migration. Instead, enterprises should consider AI-assisted migration to speed up code conversion.
• Securing Data and Infrastructure: Ensuring unified governance, robust security, and compliance across complex, multimodal data environments and AI agent interactions remains a complex challenge.

 

To move beyond the limitations of current systems, the data foundation must be purpose-built to provide the broad, governed, and accurate context that AI agents require.

Examples of AI Agents Dependent on High-Quality Data

AI agents can be deployed across a wide range of business functions, and the quality of their performance is directly tied to the underlying data they consume. Poor data quality in any of these areas will degrade the agent’s ability to act autonomously and accurately.

Impactful examples of AI agents where high-quality data is essential include:

• Customer Service Agents: Need a complete, 360-degree view of customer history, product knowledge, and current policies to resolve issues accurately and autonomously.
• Personalised Marketing Agents: Rely on fresh, granular customer behavioral and transactional data to create and target campaigns effectively in real-time.
• Automated Prospecting Agents: Require broad, unified access to internal sales data, external market trends, and unstructured competitor research documents to generate high-value, non-hallucinated outputs.
• Personalised, Dynamic Pricing Agents: Must have real-time, trustworthy data on inventory, demand, competitor pricing, and historical sales to set optimal prices without error.
• Internal Productivity Agents: Depend on organized, governed access to internal documents and structured data to perform tasks like summarizing reports or managing workflows effectively.

 

Conclusion

The journey from simple AI tools to sophisticated, autonomous agents is exciting, but it hinges entirely on the quality of the data foundation. The shift to agentic AI is not just an upgrade to the model; it is a fundamental stress test for a company’s entire data architecture. To successfully capture the potential value, businesses must prioritize unifying and governing their data into a reliable Single Source of Truth. Only by providing broad, trustworthy context can businesses move past the limitations of hallucination and inconsistency, and truly empower AI agents to be the intelligent, reliable engines of future enterprise growth.