Snowflake: Agentic AI Redefines Power in Business

Snowflake and Agentic AI: A New Era for Businesses

The rise of agentic AI marks a crucial turning point for businesses, which are no longer content to use artificial intelligence merely as a support tool. They are now beginning to delegate concrete actions to these systems, placing governance, accountability, and control at the heart of their strategies for adopting this technology.

At the opening of the Summit 2026 in San Francisco, Snowflake delivered a message that went far beyond traditional product announcements. Behind the demonstrations and speeches, a fundamental question emerged: what happens when agentic AI no longer just responds to requests but begins to take initiatives within the company?

Snowflake's keynote was particularly revealing, featuring Sridhar Ramaswamy and representatives from major companies like Accenture, Anthropic, and Sanofi. Each of these players, whether data platforms, transformation consulting firms, model providers, or large industrial groups, shared their vision of the transition from spectacular AI to AI integrated into daily operations.

For the past two years, the market has been captivated by the performance of LLMs (large language models), co-pilots, agents, and conversational interfaces. This evolution is undeniable. However, as these tools integrate into business systems, the question of their scope and control becomes central. It is now about determining how far AI can intervene, with what data, under what control, and on whose behalf.

From Assistance to Action: A Paradigm Shift

An agent capable of summarizing a meeting offers undeniable comfort. In contrast, an agent that intervenes directly in tools like Salesforce, Workday, Slack, Jira, or an ERP raises political issues. At this stage, the agent is no longer just assisting: it triggers actions, modifies processes, prioritizes tasks, recommends solutions, and sometimes even makes decisions. What becomes crucial is the governance of these actions, far beyond the mere technical performance of the tool.

This is where the major challenge of agentic AI lies. Often presented as a means to streamline work, it risks highlighting aspects that organizations had managed to keep in a useful blur:

• access rights granted by habit,
• processes never formally documented,
• trade-offs between business units and IT departments that no one wanted to revisit.

Agentic AI will not merely automate tasks; it will force companies to clarify their internal rules. Many will discover that these rules are either nonexistent or based on political compromises rather than clear and acknowledged governance.

Formalizing Internal Rules: A Necessity

For Snowflake, the answer is clear: bring AI back to governed, secured, and contextualized data. A model, no matter how powerful, quickly loses its value if everyone has access to it. What remains specific and valuable are the customer histories, business constraints, internal rules, and weak signals unique to each organization.

This battle for context will reshape internal power dynamics. In the future, those who master data, access, and workflows will no longer be a mere support function. They will hold part of the command chain of the automated enterprise. It will not necessarily be just the IT department, nor just the business units. Power will go to those who can organize delegation: defining what the agent can do, what it cannot do, and when humans need to take control.

Trust: A New Strategic Asset

The presence of Daniela Amodei, president of Anthropic, added an additional dimension to this issue. Just hours after the confidential filing of documents for an IPO, her speech on trust could no longer be perceived merely as a statement of principles. For AI players, security and governance are becoming major selling points. Moreover, trust is beginning to be seen as a financial asset. A public market will not only value the performance of a model but also its ability to convince large organizations that they can delegate part of their operations without losing control.

The market is entering a more complex phase, where clients will want to know what data the AI accesses, who retains control, and what risks it introduces into the organization. These questions will be decisive. Some solutions will continue to impress during demonstrations but will not survive the test of the field. Some companies will still accumulate pilot projects until the moment they must assess their true capacity to delegate without abdicating responsibility.