AI Models as a Commodity and Why Data Foundations Decide Who Wins - with Guillermo B. Vazquez of SAP

2026-06-09 · Source: The AI in Business Podcast · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Consulting & Professional Services · Depth: Intermediate, long

Summary

Guillermo Vazquez, Chief Architect in Business Transformation Services for SAP America, addresses the challenge enterprises face in operationalizing rapid AI advancements amidst fragmented data and processes. He emphasizes that AI models should be viewed as a commodity, with the foundational elements of harmonized data and standardized processes being critical for effective AI-enabled ERP. Vazquez highlights the necessity of investing in data harmonization, considering global privacy laws and architectural definitions, and leveraging AI tools for data cleansing. For "build vs. buy" decisions, he proposes categorizing business processes into standard (95%), specialized, and unique (5%), advocating for a focus on long-term maintenance costs over initial development. He envisions a future where AI agents adapt systems, making them "alive" and responsive to market changes within three to five years. Industries like aerospace and defense are leading by prioritizing strong foundations, while AI tools offer new opportunities for midsize companies.

Key takeaway

For AI Architects or Directors planning AI-enabled ERP deployments, prioritize investing in robust data harmonization and process standardization. Your focus should shift from upfront build costs to long-term maintenance, especially for unique, differentiating workflows. By establishing strong foundations now, you ensure your systems are adaptable for future AI agent-driven transformations, avoiding competitive disadvantage within the next three to five years.

Key insights

AI models are a commodity; robust data and process foundations are paramount for successful enterprise AI adoption.

Principles

• Treat AI engines as a commodity.
• Data harmonization is foundational for AI.
• Prioritize long-term maintenance costs.

Method

Categorize business processes by value (standard, specialized, unique) using a heat map to identify strategic differentiators, focusing on maintainability.

In practice

• Implement a global data template.
• Utilize AI tools for data cleansing.
• Conduct end-to-end business process audits.

Topics

• AI-Enabled ERP
• Data Harmonization
• Process Standardization
• Build vs. Buy Strategy
• AI Models as Commodity
• Enterprise AI Adoption

Best for: Executive, Director of AI/ML, AI Architect, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.