SAS advances its AI-ready data management foundation for industry agents and automation, with governance built-in

As enterprises race to operationalize AI, many are finding progress constrained by fragmented data environments, manual data engineering and governance models not designed to scale with the demands of AI‑driven decision‑making. While AI capabilities have advanced rapidly, the data foundations required to deploy them safely and reliably have not kept pace – leading to delayed projects, increased risk and AI initiatives that struggle to move from experimentation to production.

SAS today announced a targeted refresh of SAS Data Management – a cloud-native portfolio built on the SAS^® Viya^® data and AI platform, designed to help organizations prepare, govern and activate data for analytics, automation and AI. By embedding governance, lineage and performance directly into data workflows, SAS enables organizations to scale AI initiatives with confidence, without sacrificing trust or control.

New and expanded capabilities

• AI‑ready data management
• Governance by design
• Agentic AI and copilots
• Cloud‑native analytics acceleration

“A modern data platform is now a mission-critical requirement as organizations move toward agentic AI workflows with less human oversight,” said Alyssa Farrell, Senior Director of Data and AI Strategy at SAS. “SAS is redefining data management for the AI era by helping organizations optimize modern data estates, reduce complexity and unlock AI value, with governance and trust engineered directly into the foundation.”

Unified data management, with trust built in
In recent global research from IDC and SAS, nearly half (49%) of organizations cited noncentralized or poorly optimized cloud data environments as the top barrier to AI progress, followed closely by insufficient data governance processes (44%). Industry analysts reinforce this gap: Gartner predicts that 60% of AI initiatives will fail due to a lack of AI-ready data.

Data Management in SAS Viya addresses these challenges by treating governance and auditability as core design principles rather than compliance afterthoughts. Instead of layering controls on top of disconnected tools, SAS embeds lineage, transparency and control directly into how data is accessed, prepared and activated for analytics and AI – creating a defensible foundation that organizations can trust.

Bringing analytics to the data – not the other way around
For many organizations, scaling analytics and AI has historically required moving data across platforms – duplicating it across environments and introducing latency, cost and governance risk along the way. SAS Data Management takes a different approach by bringing analytics and AI directly to the data, wherever it resides.

A core example of this approach is SAS SpeedyStore, a high-performance, cloud-native analytical data platform tightly integrated with SAS Viya. By running analytics and AI alongside distributed data, SAS SpeedyStore reduces unnecessary data movement, improves performance and preserves the lineage and auditability organizations need to trust their results.

SAS extends this same principle beyond its own data platform. SAS Data Accelerator enables SAS analytics to run directly inside leading cloud data environments organizations already use – including large‑scale data warehouses and lakehouse architectures – reducing latency, lowering costs and improving security by keeping data in place. SAS Viya also supports modern embedded analytics engines such as DuckDB, enabling fast, local analysis of open formats including Parquet, CSV and JSON within governed workflows.

Agents and copilots that modernize data at the foundation
As organizations adopt agents and copilots to automate decisions and workflows, the quality and governance of the underlying data becomes even more critical. Many AI assistants operate only after data has been prepared, leaving gaps in trust, lineage and oversight. SAS takes a different approach by applying AI-driven assistance directly to the data life cycle itself – where foundation‑level decisions determine whether AI can be trusted at scale.

SAS offers agents and copilots designed to help organizations understand, prepare and safely use data before it is put to work in analytics, automation or AI applications. By operating within governed data workflows, these capabilities help reduce manual effort while preserving transparency and control.

SAS Viya Copilot for Data Discovery enables natural language exploration of governed data and analytics assets, helping users quickly determine what data is available, how it can be used and whether it can be trusted – reducing discovery cycles from days to seconds.

SAS Viya Copilot for Code Assistance brings AI-assisted development directly into SAS Studio, helping developers write, understand and refine SAS and Python code using natural language, without leaving the governed development environment. This accelerates iteration while maintaining human oversight.

SAS Data Maker addresses data access constraints by generating high-fidelity synthetic data that reflects the statistical, relational and temporal characteristics of real data while preserving privacy, auditability and regulatory readiness. This enables teams to develop, test and collaborate without exposing sensitive information.

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