SAS enhances its Viya platform with advanced AI-powered tools
SAS is cutting through the noise in AI. They’ve doubled down on making real-world AI applications more accessible, scalable, and useful. At its core, the new upgrades to the Viya platform are about one thing: reducing friction for businesses that need data-driven decisions at speed. Instead of simply throwing more machine learning features at the problem, SAS is focused on delivering tools that solve real problems, quickly and without adding complexity where you don’t need it.
Let’s talk about the core components. The first is SAS Data Maker. This tool generates synthetic data, basically artificial datasets that look and behave like the real thing. Businesses often don’t have access to good data, or worse, can’t use sensitive data due to privacy regulations. Data Maker uses tech from Hazy, which has been leading in synthetic data, to fill that gap. It reduces the risk of exposing personal data while still allowing companies to train their AI systems with high-quality inputs. This helps move development faster without breaching trust or compliance rules. It’s scheduled for general release in Q3 2025. Smart move.
Another key rollout is Viya Intelligent Decisioning. It lets companies deploy AI agents that automate decisions based on rules and logic you define. It’s not “black box” AI. You control the degree of autonomy these agents have, depending on the risk level of the task. If the task is basic and repetitive, automate it. If it’s sensitive, keep a human in the loop. That’s the kind of flexibility enterprises need. And it’s available now.
For smaller and midsize companies, the big ask has always been affordability and simplicity. SAS Viya Essentials is aimed squarely at that segment. It’s a hosted, managed service. You don’t need to build complex infrastructure or hire a full team of specialists. You get a curated set of Viya tools delivered out-of-the-box. That’s straightforward value.
Then there’s SAS Viya Copilot. Think of it as your AI assistant built into the platform, except it understands both code and business tasks. If you’re a developer or data scientist, it helps accelerate model building and clean up code. If you’re a business user, it can answer questions and give insights without needing to write scripts. This goes live in Q3 2025, and it’s a move toward making AI interaction more natural and useful across all roles.
These upgrades are not about hype, they’re about speed, control, and practical adoption. SAS isn’t trying to reinvent AI. It’s making AI simple where it matters and powerful where it counts. That matters when you’re moving at scale.
Embedding robust governance and ethical frameworks into AI deployment
AI is everywhere now, used in decision-making, automation, and everyday business operations. But most organizations haven’t built in the necessary controls, and it’s starting to show. SAS is taking a decisive step with governance built into its AI strategy from the ground up.
With AI spreading fast across business units, some teams have started deploying generative AI without oversight, exposing sensitive data and creating compliance risks. Reggie Townsend, Vice President at SAS’s Data Ethics Practice, puts it clearly: large organizations are already deep into AI adoption, but too many are doing it without policy, structure, or clear lines of responsibility. That gap creates real risk, operationally and legally.
SAS’s answer is the AI Governance Map, a tool to help organizations assess where they stand in terms of responsible AI maturity. It’s structured across four key areas, giving executives and tech leaders a clear path forward. The objective is to move from experimentation to accountable deployment by design, meaning policy, ethics, and compliance are embedded early, not patched in later. When AI fails, it’s rarely a tech problem, it’s usually an oversight or a misstep in setting expectations or controls. The Governance Map is engineered to close that loop.
Kathy Lange, Research Director for AI Software at IDC, points out that SAS is shifting its entire strategy to embrace a broader set of users in the enterprise. Developers, analysts, business leads, they all interact with AI in different ways. SAS isn’t building a one-size-fits-all solution. It’s optimizing the entire AI lifecycle, development, testing, deployment, under a single, governed structure that delivers business results with speed and clarity.
For business leaders, the value is direct. You get more control, fewer surprises, and a defined process for when AI systems need to be paused, corrected, or scaled. In heavily regulated industries, that can be the difference between progress and costly setbacks. And as AI faces growing scrutiny from regulators, having governance in place is no longer optional, it’s a competitive edge.
Expanding industry applications through targeted modeling and immersive simulations
SAS is sharpening its focus on industry-specific applications of AI, and doing it with purpose. Instead of offering general tools that leave most of the work to your teams, they’re delivering AI models and simulation capabilities that are pre-built for high-impact use cases. Faster deployment, lower implementation risk, and clear outcomes are the direct upsides for executives who need real returns from AI, not just experimentation.
The company is strengthening its use of AI-driven simulation through digital twins, virtual models of real systems enhanced with generative AI. SAS has integrated these with Unreal Engine from Epic Games to enable advanced 3D simulations. Using Epic’s RealityScan tool, users can create detailed, photorealistic environments just by capturing imagery through a mobile device. These visuals are then brought into simulations to create accurate, immersive views of operations. This matters when you’re running large-scale systems where precision in modeling leads directly to better planning and output.
But this isn’t just visual enhancement. It’s paired with AI models targeted at high-value, time-intensive business problems. These packaged models are ready to deploy or fine-tune. They cover healthcare (like monitoring risk in medication adherence), public sector (such as ensuring payment integrity in food assistance programs), supply chain optimization for manufacturing, and tax compliance. There’s even a cross-industry tool for entity resolution, which is critical when unifying data from multiple sources. These are built for real-world friction and expected to operate in production environments.
For executives, the message is simple: if AI is going to move the needle, it needs to do more than analyze spreadsheets. It has to target cost-heavy, manual processes and replace them with models that actually scale. With these sector-specific solutions, the entry barrier drops significantly. You don’t need to build custom architecture from zero. You can test fast, iterate quickly, and get value in less time.
None of this would matter if trust and usability weren’t in place. SAS seems to understand that. They’ve framed these tools to work seamlessly on their Viya platform, governed under the same rulesets and ethical oversight as the rest of their AI lifecycle.
In short, SAS is proving that industry AI doesn’t have to be ground-up to be effective. It has to be targeted, proven, and ready to plug into what decision-makers care most about, performance, trust, and immediate impact.
Building trustworthy agentic AI that balances autonomy and ethical oversight
SAS is betting on agentic AI, AI that can act with a level of autonomy but under strict, configurable ethical and operational parameters. This move isn’t conceptual. It’s built into the Viya platform now, with clear controls, governance, and integration paths that let organizations scale while maintaining oversight. It’s a response to what large enterprises already see happening: AI is becoming a participant in the business, not just a tool.
Viya Intelligent Decisioning is central to this strategy. It combines deterministic analytics, rules-based logic that’s predictable and explainable, with large language models (LLMs), giving enterprises a structured way to deploy AI agents that behave consistently, work across complex workflows, and make decisions based on established business and ethical logic. You can set the exact level of autonomy depending on the risk profile, human-in-the-loop where needed, fully autonomous where it’s safe. That’s high-value functionality for companies navigating regulatory scrutiny or operating across volatile markets.
This design keeps control in the enterprise’s hands, where it should be. Whether these agents are processing transactions, verifying documents, or analyzing operational scenarios, they do so within a governed system. That includes integrated compliance, user access control, and data privacy protections, essential features in enterprise-grade AI. The decisions these agents make aren’t just fast or cost-efficient, they are traceable and defensible. That’s a long-term advantage CEOs and board members will care about.
Tiffany McCormick, Research Director at IDC focused on Digital Business Models and Monetization, emphasized this approach. She pointed out that as enterprises shift into open, multi-cloud AI ecosystems, trust and explainability aren’t optional, they’re differentiation. SAS is leaning into that, not just with talk, but with working products that enforce ethical rigor in everyday business logic.
For the C-suite, this flags a wider shift. It’s no longer about whether your business uses AI, but whether your AI systems are defensible, scalable, and aligned with your brand and compliance obligations. Agentic AI, done right, extends decision-making without compromising accountability. SAS understands that trade-off and is delivering tools that remove the operational guesswork.
Bottom line, this is AI that acts, but under rules you can manage and explain. It’s a practical, deployable path toward scaled intelligence, with no loss of control. That’s how serious AI integration moves forward.
Main highlights
- Scale AI with practical tools, not complexity: SAS’s Viya upgrades deliver synthetic data, AI copilots, and decisioning agents designed to reduce barriers and speed up deployment, especially for teams needing secure, scalable solutions without building from scratch. Leaders should prioritize platforms that enable fast adoption without sacrificing control.
- Build AI governance in early to reduce risk: With AI adoption outpacing oversight, SAS’s governance tools, like the AI Governance Map, give enterprises the structure to enforce ethical, compliant AI from the start. Executives should embed governance frameworks now to prevent future exposure and regulatory friction.
- Use tailored AI models to solve real industry problems: SAS is rolling out ready-to-deploy models and immersive simulations for sectors like healthcare, manufacturing, and government. Leaders should focus on use-case-specific AI to streamline high-cost workflows and deliver measurable operational gains.
- Deploy agentic AI with traceable, controlled autonomy: SAS enables an adaptive level of autonomy for AI agents, combining deterministic logic with LLMs inside a governed framework. Businesses should adopt agent-based AI where tasks are repetitive or data-driven but maintain human oversight for strategic decisions.
