The Five Data-Centric Steps Enterprises Should Take to Succeed with AI

2026-06-26 · Source: The AI Journal · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

Semarchy's research reveals a significant shift in enterprise AI investment, with C-suite executives allocating over 20% of their technology budgets to AI more than tripling from 16% in 2025 to 50% in 2026. Currently, 97% of leaders invest in AI, and 98% describe their organizations as "AI-ready." However, a divide is emerging: organizations using integrated data platforms, systematic quality controls, and Master Data Management (MDM) are succeeding, while others face project delays (22%), operational inefficiencies (21%), increased costs (20%), compliance challenges (19%), and decreased trust (19%) due to poor data foundations. Five characteristics are consistently linked to success: early governance integration (77%), systematic quality controls (66%), agentic data management (65%), MDM as a foundation (50%), and operationalized data delivery (48%).

Key takeaway

For Directors of AI/ML or VPs of Engineering scaling AI initiatives, prioritize robust data foundations to avoid project delays and cost overruns. Your success hinges on integrating Master Data Management, implementing systematic data quality controls, and operationalizing data delivery. Embed AI considerations into data governance early and treat data platforms as core AI engines to ensure reliable, scalable AI deployments.

Key insights

Data-centric approaches, integrating MDM and quality controls, are critical for successful enterprise AI adoption and avoiding project failures.

Principles

• Treat data platforms as AI engines.
• DataOps ensures continuous data readiness.
• Data as a product builds reusable assets.

Method

Successful data-centric AI involves five characteristics: early governance integration, systematic quality controls, agentic data management, MDM as a foundation, and operationalized data delivery.

In practice

• Embed AI into data governance early.
• Actively measure and enforce data quality.
• Prioritize DataOps for streamlined delivery.

Topics

• Enterprise AI
• Data Governance
• Master Data Management
• Data Quality
• DataOps
• Agentic AI

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI Journal.