AI Is Becoming Enterprise Infrastructure: Reading the Industry Direction from OpenAI, Anthropic, and Snowflake's Latest Moves

In May 2026, several signals in the AI industry are converging toward a clear direction: AI is evolving from "single-point model capability" to "enterprise operational infrastructure." This article analyzes the underlying logic of this trend based on the latest moves from OpenAI, Anthropic, and Snowflake, and what it means for AI data analytics products.

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1. OpenAI Deployment Company: Model Companies Entering the "Last Mile" of Delivery

What's Happening

OpenAI announced the formation of the Deployment Company, directly delivering and operating AI systems for enterprise customers. This means OpenAI is no longer just selling model APIs—it's embedding model capabilities into enterprise data, tools, permissions, approvals, and business processes to create production-ready operational systems.

Why It Matters

The biggest challenge for enterprises adopting AI has never been insufficient model capability, but rather the difficulty of integrating models into real workflows:

• •Where is the data, how is it accessed, and who controls it
• •Who is responsible for AI outputs, and how are they audited
• •How do conclusions trigger business actions (approvals, notifications, tasks)

The Deployment Company targets exactly this "last mile"—converting model capabilities into enterprise-grade operational systems. OpenAI entering delivery itself means the boundaries between model companies, consulting firms, and enterprise software vendors are rapidly blurring.

Implications for the Industry

Model capabilities will become increasingly commoditized. The real differentiator will be "who can help enterprises integrate AI into data, processes, and governance systems." Model companies that only provide APIs will face increasing pressure.

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2. Anthropic × Gates Foundation: AI Enters Public Services and Professional Data Systems

What's Happening

Anthropic launched a $200 million partnership with the Gates Foundation, covering global health, life sciences, education, and economic mobility. The partnership includes Claude credit allocations, technical support, dataset connectors, evaluation benchmarks, and knowledge graph development.

Why It Matters

This is not simple "philanthropic donation"—Anthropic is building a set of AI public infrastructure:

• •Connecting Claude to real data and business processes in healthcare, education, and agriculture
• •Using benchmarks, datasets, and connectors to build reusable capability systems

For the AI industry, this is a landmark signal: AI deployment is forming a "model + data assets + toolchain + evaluation system" combination. The model itself is just the entry point; real value lies in the depth of connection with real data and processes.

Implications for the Industry

As AI enters public services and professional data systems, enterprise AI must have:

• •Ability to connect to multiple data sources
• •Ability to integrate with professional domain knowledge bases
• •Ability to produce measurable, traceable outputs

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3. Snowflake: Data Governance Evolves from Static Permissions to Runtime Control

What's Happening

Snowflake released the Sensitive Data Entitlement Report in Public Preview, and extended Cortex AI fine-grained permissions. New features include:

• •Viewing which users access tables containing sensitive data through role permissions
• •AI_CLASSIFY supporting document classification
• •Cortex AI Functions supporting function-level authorization

Why It Matters

This is a critical step in enterprise AI deployment: after integrating AI into the data platform, enterprises must first answer one question—who can allow AI to see what data, invoke what functions, and generate what results?

Traditional data governance is static: assign permissions to roles, creating permission tables. But AI-era data governance must be runtime:

• •Real-time judgment during AI queries whether access is permitted
• •Real-time judgment during AI output whether results involve sensitive information
• •Real-time judgment during AI function invocation whether execution is permitted

Snowflake is upgrading data governance from "static permission tables" to "runtime control layer for the AI era."

Implications for the Industry

When enterprises adopt AI analytics products, data governance is not optional—it's mandatory. Products that cannot solve "whether AI can access, how to access, and how to audit results" will not enter enterprise core systems.

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4. Trend Summary: AI Analytics Products Must Solve Three Things Simultaneously

Synthesizing the three signals above, a clear conclusion emerges:

AI analytics products must simultaneously solve three things: complete tasks, connect to enterprise data, and enable governance and auditing.

Q&A boxes that only generate SQL will become increasingly insufficient. When enterprises choose AI analytics products, they will increasingly value:

• •Depth of integration with existing data platforms
• •Permission control and governance capabilities
• •Audit and compliance support

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5. Implications for AskTable

AskTable, as an enterprise AI analytics product, is in the right position:

1. •

   Task Completion: Natural language-driven SQL generation, multi-dimensional analysis, follow-up questions, and skill accumulation—solving "ability to complete tasks"

2. •

   Data Connectivity: Support for 33+ database types, integration with Feishu, WeCom, and other enterprise platforms—solving "ability to connect to enterprise data"

3. •

   Governance & Audit: Data source permission control, query history records, traceable results—solving "ability to enable governance and auditing"

These three capability dimensions are precisely what OpenAI Deployment Company aims to solve for enterprises, what the Anthropic-Gates Foundation collaboration emphasizes regarding data access, and what Snowflake is working to perfect with runtime control.

Competition in the AI analytics space will increasingly shift from pure model capability to "who can more comprehensively solve the last-mile problem of enterprise AI adoption."

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This article is based on AI industry developments as of May 15, 2026, incorporating the latest releases from OpenAI, Anthropic, and Snowflake.