How SAS is putting AI governance at the heart of its agent strategy

SAS positions AI governance as the cornerstone of its agentic AI strategy

AI is crossing a boundary. It’s moving from being a tool that observes and predicts to one that takes action. That shift creates both opportunity and risk. When machines start making real decisions, invoking tools, integrating data, and automating tasks, organizations risk losing control unless governance keeps pace. SAS recognizes this and is not waiting for problems to emerge. At its SAS Innovate conference, the company unveiled a full stack of AI governance tools: copilots, agent frameworks, Model Context Protocol (MCP) plugins, and management systems that keep human control at the center.

Marinela Profi, Global AI and Generative AI Market Strategy Lead at SAS, captured the essence of this change. She said the company is witnessing a move from “AI that forms” to “AI that acts.” This transition elevates the importance of accountability, transparency, and trust. SAS is building its agentic AI ecosystem around these principles, ensuring organizations can both automate and audit at the same time.

For executives, this signals a turning point. Governance is now about strategic control. Systems must be designed so leaders can see, direct, and trust what AI does across every workflow. Those who can balance speed with oversight will turn governance into a growth driver. In other words, the companies that master responsible automation will move faster and safer than those relying on manual intervention or blind trust in the machine.

SAS introduces Viya Copilot, a human-supervised, conversational AI tool

AI works best when it speaks the same language as its users. SAS’s new Viya Copilot does exactly that. It’s an intuitive, human-supervised conversational system that helps developers, data scientists, and analysts work within the Viya platform using natural language. Integrated with Microsoft Foundry, it lets users ask questions, analyze data, build models, and generate explainable code, all without stepping outside their workflows.

The Copilot’s early use cases show SAS’s practical approach. The Asset and Liability Management (ALM) Copilot helps organizations manage financial risk scenarios and translate natural language into analytic models. The Health Clinical Data Discovery Copilot supports data analysis and research interpretation in healthcare. Both examples demonstrate the benefit of coupling AI automation with human oversight, efficiency gains without sacrificing transparency or accuracy. Marinela Profi described it as an “expert assistant” designed to support full data lifecycles.

For business leaders, this means AI operations can now be democratized across teams. But the oversight remains intentional, people still drive the analysis while the AI accelerates execution. This balance ensures that as SAS expands the Copilot to sectors such as banking and manufacturing later this year, companies can scale faster without losing accountability or control.

Executives should view Viya Copilot as a model for future enterprise AI: systems that are human-centered, governed, and explainable from the first query to the last decision.

SAS enhances agent interoperability and governance

SAS is addressing one of the biggest challenges in enterprise AI: connecting multiple intelligent systems while maintaining control and security. The company’s new Viya MCP server creates a standardized method for integrating external large language models (LLMs) such as GPT, Claude, or Gemini into SAS’s core analytics environment. Instead of building custom integrations or duplicating logic, enterprises can now connect AI agents safely and uniformly. This eliminates many of the blind spots that occur when systems operate in isolation.

To complement this, SAS released the Agentic AI Accelerator, a toolkit that includes ready-to-use code, interfaces, and best practices for designing, deploying, and governing agents within the Viya platform. It’s accessible on GitHub, allowing enterprises to adopt and build upon SAS’s framework without unnecessary complexity. These developments make it possible for teams with different skill levels, from data engineers to low-code users, to create AI agents that follow the same governance and compliance rules.

Marinela Profi, Global AI and Generative AI Market Strategy Lead at SAS, explained that “the Copilot is not only answering questions for you, it can invoke capabilities across Viya in a more structured way.” Her point emphasizes that automation can be directed and monitored simultaneously.

For C-suite leaders, this evolution matters. Integrating AI systems from multiple vendors has always carried operational and security risks. With SAS’s MCP framework, those risks are minimized, creating a consistent governance layer that allows AI to expand safely across departments and platforms. It’s not about restricting the technology, it’s about enabling scale while maintaining structured oversight.

SAS bolsters ethical oversight and transparency

Trust in AI now defines enterprise credibility. SAS’s upcoming SAS AI Navigator takes aim at this issue by providing businesses a panoramic view of every AI model in use, whether developed internally or sourced from vendors. Set to launch in Q3 2026 on Microsoft Azure Marketplace, it helps organizations monitor all models, apply governance policies, and align them with both corporate standards and regulatory frameworks. This system offers more than compliance, it provides a living inventory of how AI operates, evolves, and impacts outcomes.

Reggie Townsend, Vice President of Data Governance and Ethics Practice at SAS, described the Navigator as a tool that “answers the really basic question: How are we doing?” It’s designed not just to track AI but to measure the integrity of its operations. Townsend highlighted that companies are now treating trust as both a differentiator and a form of business currency. The Navigator allows executives to oversee AI use as they would oversee financial performance, ensuring accountability and strategic advantage remain linked.

For business leaders, the message is direct. AI cannot be left to run without structured oversight. With the AI Navigator, executives can gain real visibility across their AI portfolio, making governance measurable and visible in business performance terms. Townsend’s view reinforces this: responsible AI isn’t a limitation, it’s the foundation for scaling judgment and maintaining brand trust as automation accelerates.

Enterprises that deploy tools such as AI Navigator proactively are better positioned to balance innovation with accountability. It enables leaders to move quickly without losing ethical direction, ensuring that AI remains a competitive strength instead of a potential liability.

SAS refines its data management strategy with enhancements that secure trusted, unified enterprise data for reliable AI operations

AI depends on the integrity and availability of the data it uses. SAS is taking direct aim at this foundational issue with a major upgrade to its Data Management portfolio on the Viya platform. The new system embeds governance, transparency, and cloud-native analytics into every step of data handling, ingestion, preparation, and activation. The update resolves a common problem in large enterprises: fragmented data ecosystems spread across legacy systems, cloud providers, and on-prem environments.

Alyssa Farrell, SAS Industry Market Lead, pointed out that many organizations still struggle with low trust in their data, leading to low trust in decisions. She explained that “agents and AI crave data more than ever before” and emphasized that sound data practices must come first when organizations scale automation. With this update, SAS makes governance non-negotiable. The new SpeedyStore engine adds a key efficiency layer by processing analytics directly where the data resides. This design maintains digital sovereignty while removing operational friction. It ensures enterprises no longer need to transfer large datasets for analysis, reducing latency and cost while maintaining compliance.

For executives, the message is clear. AI cannot deliver reliable insight if the data beneath it is disorganized or unverified. SAS’s re-architected data management approach gives companies a practical framework for strengthening information quality and transparency at source. It helps organizations sustain control over their most valuable digital asset, their data, while keeping compliance aligned with real-time operations.

Enterprises that adopt these practices position themselves to move faster with confidence. They can focus on scaling intelligent systems built on accurate, governed, and transparent data. For C-suite leaders aiming to operationalize AI responsibly, this level of control and visibility is what determines whether transformation efforts deliver measurable business value or stall under complexity.

Main highlights

• AI governance as a competitive anchor: SAS is making governance the core of AI strategy to ensure transparency, accountability, and trust as automation scales. Leaders should prioritize governance frameworks early to maintain control and earn customer confidence.
• Human-in-the-loop copilots for smarter decisions: The Viya Copilot integrates conversational AI and human oversight to streamline analysis and improve decision quality. Executives should adopt similar AI assistants that enhance productivity without compromising oversight or accuracy.
• Unified agent infrastructure for secure scaling: The Viya MCP server and Agentic AI Accelerator standardize connections between internal and external AI systems while ensuring compliance and control. Decision-makers should leverage standardized integration to avoid operational risk as AI ecosystems expand.
• Governance visibility through AI navigator: The SAS AI Navigator introduces full oversight of models, aligning them with regulatory and internal standards. Leaders should use metrics-based governance tools to maintain ethical accountability and turn trust into a measurable business advantage.
• Trusted data as the foundation for reliable AI: SAS’s updated Data Management platform embeds governance and efficiency into data workflows, reinforcing integrity from the source. Executives should invest in unified data systems to ensure scalability, compliance, and trustworthy AI-driven outcomes.