As enterprises move from testing AI to deploying it at scale, new challenges around control, trust, and visibility are emerging. Addressing this shift, SAS has introduced a new set of tools focused on governance as businesses adopt agentic AI systems that can act, decide, and operate across complex environments.
At its annual event, the company unveiled a portfolio that includes copilots, agent frameworks, Model Context Protocol (MCP) plugins, and management tools. These are designed to help organisations scale AI while maintaining oversight.
“What we’re seeing here is really a shift from AI that forms to AI that acts,” said Marinela Profi. “This is a significant leap, because it introduces new requirements around trust, around governance, around accountability.”
A key highlight is SAS Viya Copilot, a conversational AI assistant integrated into the Viya platform. It allows users to analyse data, build models, and make decisions using natural language. The tool supports features such as Q&A, explainable code generation, model guidance, dashboarding, and AI-assisted search. Initial versions focus on Asset and Liability Management and Health Clinical Data Discovery, with expansion planned for banking and manufacturing.
SAS also introduced an MCP server to standardise how internal and external AI agents connect to tools and data securely. Alongside this, an Agentic AI Accelerator provides frameworks and components to design, deploy, and manage AI agents across skill levels.
To strengthen governance, SAS announced SAS AI Navigator, a SaaS platform launching in Q3 2026. It will give enterprises a full view of their AI systems and allow them to apply policies and regulations across models and tools.
“It’s giving visibility into your AI inventory,” said Reggie Townsend. “But it also answers the really basic question: How are we doing?”
The company emphasised human oversight as critical, promoting a “human-in-the-loop” approach to maintain control over AI decisions.
On the data side, SAS updated its Data Management platform to improve data quality, transparency, and control. Enhancements include better lineage tracking, AI-ready data systems, and a cloud-native analytics platform designed to process data without moving it.
Overall, SAS is positioning governance and trust as key differentiators as enterprises scale AI adoption.
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