Snowflake publishes May 12–29 industry guidance on AI, compliance and partnerships

Snowflake released a cluster of Industry Solutions posts between May 12 and May 29, 2026, aimed at sector practitioners and decision‑makers. The updates aggregate sector‑specific guidance on AI, compliance and partner — driven implementations, signaling practical next steps for regulated and commercial buyers who rely on data foundations for emerging AI applications.

The recent entries are dated and titled as follows: May 29’s "Insights from Slalom: Federal Agencies Should (re)Consider Snowflake" (Public Sector); May 28’s retail and consumer goods analysis on like‑for‑like growth amid macroeconomic volatility; May 27’s "Snowflake Completes IRAP Protected Assessment on Google Cloud Platform"; May 21’s "Agentic AI in Financial Services: Nasdaq’s eVestment™ is Leading the Pack" and a May 21 post on a new agreement with OneGov;

plus May 14 posts on Cortex Code for FP&A and common AI pitfalls in finance, a May 13 retail technology piece, and May 12’s article on trustworthy AI in healthcare. Each item pairs technical detail with partner or compliance context. The Industry Solutions index groups content into categories such as Advertising, Media & Entertainment; Financial Services; Healthcare & Life Sciences; Manufacturing; Public Sector; Retail and Consumer Goods; Technology; and Telecom. Several recent pieces emphasize AI use cases built on a data foundation, while others highlight compliance steps and public‑sector engagement, reflecting a mix of technical guidance, partner perspectives and regulatory updates.

Aggregated, the posts point to a few tangible implications for buyers: government‑facing certifications like the IRAP Protected Assessment on Google Cloud Platform; partner recommendations for agencies (for example, Slalom); and focused discussions on AI and FP&A tooling such as Cortex Code and Nasdaq’s eVestment™, which target both regulated financial workflows and commercial analytics. These updates clarify adoption pathways and compliance considerations for organizations evaluating data‑driven AI projects.