Scaling agentic AI demands a strong data foundation - 4 steps to take first

2026-04-21 · Source: News and Advice on the World's Latest Innovations | ZDNET · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

Worldwide AI spending is projected to reach $2.5 trillion by 2026, with the global agentic AI market specifically forecast to hit $8.5 billion by the end of 2026 and nearly $40 billion by 2030. Organizations currently utilize an average of 12 AI agents, expected to increase by 67% to 20 agents within two years. However, scaling agentic AI faces significant challenges, primarily poor data quality, cited by eight in ten companies as a roadblock. By 2027, companies failing to prioritize high-quality, AI-ready data risk a 15% loss in productivity. McKinsey research indicates that while nearly two-thirds of enterprises have experimented with agents, fewer than 10% have scaled them to deliver measurable value, emphasizing the critical need for a strong data foundation, modernized data architecture, and improved data quality to support autonomous agent workflows.

Key takeaway

For CTOs and VPs of Engineering aiming to scale agentic AI initiatives, prioritizing a strong data foundation is critical. Your teams must modernize data architecture, ensure data quality, and establish robust governance models to avoid productivity losses and unlock measurable value. Begin by identifying high-impact, repetitive workflows suitable for agentification and invest in connecting fragmented data sources to support autonomous operations.

Key insights

Scaling agentic AI hinges on a robust foundation of high-quality, trusted data and modernized data architecture.

Principles

• Trusted data is the backbone of agentic AI.
• Data silos impede effective agentic decision-making.
• Governance is key for scaled agentic systems.

Method

To scale agentic AI, identify high-impact workflows, modernize data architecture for interoperability, ensure consistent data quality, and build an operating and governance model for agent-led workflows.

In practice

• Focus agentification on deterministic, repetitive tasks.
• Prioritize data interoperability across applications.
• Establish data lineage and accuracy standards.

Topics

• Agentic AI Scaling
• Data Foundation
• Data Quality
• Data Architecture Modernization
• AI Governance

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Architect, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by News and Advice on the World's Latest Innovations | ZDNET.