Why Strong Data Foundations Are Crucial for Scaling Agentic AI

McKinsey’s latest report "Building the Foundations for Agentic AI at Scale" shows that although about 66% of companies have experimented with agentic AI, fewer than 10% have turned it into scalable business value; the primary obstacle cited by 80% of firms is inadequate data infrastructure.

Agentic AI vs. Generative AI

Unlike traditional generative AI that merely produces content, agentic AI acts as an autonomous "intelligent agent" that can invoke tools, coordinate multiple models, and make real‑time decisions. The report distinguishes two typical architectures:

Single‑agent workflow : one agent sequentially uses multiple tools and data sources.

Multi‑agent workflow : several specialized agents collaborate via a shared knowledge graph and fine‑grained data access.

Both architectures demand continuous, high‑quality data; fragmented or poorly governed data leads to contradictory or erroneous agent behavior.

Seven Data‑Architecture Principles for Agentic AI

Treat data ingestion as a product—one ingestion, enterprise‑wide availability.

Share not only data but also its meaning so AI models and agents interpret it consistently.

Use a unified data foundation for both analysis and AI to avoid duplicate pipelines.

Embed trust by default—security, privacy, and governance are automatic.

Expose capabilities through stable interfaces, enabling teams to build applications easily.

Make behavior observable and measurable—track data quality, model performance, and cost in real time.

Provide a controlled agent runtime—central execution layer enforces corporate rules and safeguards.

These principles shift data architecture from fragmented silos to modular, interoperable foundations that support safe, real‑time, context‑rich agentic AI.

Four‑Step Action Plan to Harden the Foundation

Identify high‑value processes for "agentification" —pilot end‑to‑end workflows (e.g., knowledge management, marketing, sales) that can benefit most from autonomous agents, define required data, and validate with clear metrics before scaling.

Layered modernization of data architecture —upgrade existing platforms rather than replace them. Connect data source, platform, semantic (ontology/knowledge‑graph), data‑product, and consumption layers to enable agents to access unified, real‑time information. Standards such as MCP and A2A are key.

Ensure data quality end‑to‑end —move from periodic clean‑ups to continuous, real‑time management. Both structured and unstructured data must meet unified standards; AI can automate validation, anomaly detection, and enrichment, while newly generated data must be tracked for lineage and reconciliation.

Build an operational and governance model for agentic AI —shift human roles from execution to supervision and orchestration. Define what agents can do, which data they may access, and when human approval is required. Implement guardrails, lifecycle management, and a federated governance model where domain teams handle day‑to‑day rules and a central team maintains shared platforms and global safeguards.

In the era of agentic AI, a solid data foundation becomes the core competitive advantage. Enterprises that successfully agentify high‑value processes, modernize their data architecture, guarantee data quality, and upgrade governance will be the first to convert experimental AI results into scalable business outcomes.

Now is the window to act—strengthen the data foundation, and let agentic AI truly "take off".