SAS, the 50-year-old privately held analytics giant, has positioned Artificial Intelligence not as a standalone revolution, but as an integrated tool within its comprehensive suite of solutions. At the recent SAS Innovate 2026 conference in Grapevine, Texas, the company underscored this pragmatic approach, showcasing advancements in agentic workflows, digital twins powered by Unreal Engine, and ongoing quantum computing initiatives. The overarching message to its vast customer base, spanning Fortune 500 companies and beyond, is clear: SAS is focused on making AI demonstrably useful and trustworthy for their specific business challenges.
The company’s CTO, Bryan Harris, articulated this philosophy during his keynote address, drawing a parallel between past technological paradigms and the current AI surge. "Every breakthrough technology follows the same arc," Harris stated. "It solves a problem, it reshapes society, and eventually it fades into the background of everyday life. Yesterday it was the internet, and today it’s AI. And tomorrow, I can assure you it will be something else. The only thing that outlasts every innovation is people." This sentiment served as a guiding principle throughout the event, emphasizing that while technology evolves, the human element and the fundamental business problems remain constant.
SAS’s enduring success, now spanning half a century, is rooted in its origins. Founded in the 1970s by a team analyzing agricultural data at North Carolina State University, the company has consistently prioritized domain-specific problem-solving over the pursuit of nascent technologies for their own sake. Udo Sglavo, SAS’s VP of Applied AI and Modeling, elaborated on this historical perspective. "SAS has made really good progress for 50 years by focusing on domain questions," Sglavo told The New Stack. "It was not about creating the technology. It was really about, ‘Can we solve a specific business question, industry question?’"
This foundational ethos has guided SAS’s response to the current AI landscape, particularly the rise of large language models (LLMs). While some competitors opted to develop their own proprietary LLMs, SAS has adopted what Sglavo describes as an "agnostic technology" approach. "AI will change again. We will see different waves of different AI techniques coming in," Sglavo observed. "And we will always say, ‘Look, it doesn’t matter to us.’ To us, it’s just a tool that we are using to solve the problem."
This commitment to neutrality extends across SAS’s technological evolution. Harris highlighted this continuity, referencing the company’s past innovations such as the SAS programming language, its multi-vendor architecture in the 1980s that allowed its software to run on diverse hardware, and its later multi-cloud strategy supporting AWS, GCP, and on-premise deployments. The current iteration of this strategy involves supporting a "multi-large language model type of environment." Sglavo illustrated this with a practical example: "When we go to a German insurance company, and they are a Microsoft shop, we can’t come in and say, ‘We want you to use this large language model.’ They’ve made this decision already." This client-centric, adaptable approach ensures SAS can integrate with existing technology stacks, rather than forcing customers to adopt new ones.
Bridging the Gap: From Agentic Hype to Enterprise Reality
A significant focus at SAS Innovate 2026 was the practical implementation of agentic AI within enterprise settings. Brett Wujek, SAS’s Head of Product Strategy for Next-Generation AI, identified a common misconception: that agents inherently possess knowledge. "The biggest misconception he encounters is ‘that the agents actually know anything,’" Wujek explained. He emphasized that while LLMs excel at natural language processing, the true power and reliability of agents stem from the "tools and resources" they can access.
Harris elaborated on this point, framing it as a critical issue of trust. Traditional enterprise systems are built on deterministic logic, offering predictable outputs. In contrast, LLM-based agents are inherently non-deterministic, meaning "three different runs can produce three different outputs." For widespread enterprise adoption, this unpredictability necessitates robust guardrails, including rigorous verification and validation processes.
SAS’s solution to this challenge is its new agentic workflow capabilities, built upon its Viya cloud platform. The company is developing MCP (Model, Control, and Process) servers that expose SAS’s established analytics, governance, and decisioning capabilities as tools that can be orchestrated by agents, even those managed by third-party vendors. "I never hear from customers, ‘Well, we don’t trust the results coming from SAS,’" Wujek remarked. The goal is to extend this inherent trust to any agentic framework a customer chooses to employ. "We’re now at that tipping point of coming back around on, all right, how can we actually use this technology in a really trusted and structured way?" Wujek told The New Stack.
This trust is further underpinned by SAS’s long-standing commitment to robust engineering. Luis Flynn, who leads product marketing for SAS’s packaged AI solutions and agent-based analytics, stressed that SAS’s offerings are not mere wrappers around open-source libraries. "What we still do best is compiled C code," Flynn stated. "We’re not spitting out just stitched-together Python code." While Python users can interact with SAS capabilities in a familiar manner, the underlying engine relies on highly performant, compiled algorithms, ensuring efficiency and reliability, particularly when an MCP server calls a SAS forecasting model, for instance.
The Value of "Boring" in the Age of AI
SAS’s pragmatic approach, often characterized as "boring," is precisely what underpins its value proposition for its Fortune 500 clientele. These organizations rely on SAS for critical, billion-dollar decisions, demanding dependable data and robust analytics. Simultaneously, they are eager to leverage new AI tools to enhance their capabilities. SAS’s history demonstrates a pattern of making sound, albeit sometimes unglamorous, decisions about new technologies.
Sglavo articulated this dichotomy: "When technology is early in its life cycle, you pick the use cases which are impressive. And eventually you go back to the use cases which are extremely boring, because that’s where the money is." This philosophy was evident in the demonstrations at SAS Innovate 2026. While photorealistic digital twins, created in collaboration with Epic Games and Unreal Engine, showcased advanced simulation capabilities for manufacturing and sterilization facilities, and a four-phase framework for AI-assisted software development within SAS itself highlighted shifts in developer roles, the underlying emphasis remained on delivering tangible business value.
Looking Ahead: Quantum Computing and Trustworthy AI
Beyond immediate AI applications, SAS is actively investing in quantum computing. A dedicated team is developing a toolbox designed to abstract hardware differences across various quantum vendors. However, even in this cutting-edge field, SAS remains anchored to its core strengths. Bill Wisotsky, SAS’s Principal Quantum Systems Architect, recounted an instance where an insurance company approached SAS with a complex optimization problem they believed necessitated quantum computing. Before delving into a quantum solution, SAS’s classical computing experts successfully reformulated and solved the problem in a mere 90 seconds. "SAS is not a quantum computing company," Wisotsky asserted. "As a company, we want to provide value."
The increasing complexity of AI, particularly generative AI, brings governance to the forefront. Kristi Boyd, SAS’s Senior Trustworthy AI Specialist, emphasized that governance is intrinsically linked to the use case. "With generative AI, the technology is what it is, but how you apply it is where you’re actually introducing the risks," Boyd explained. She cited two SAS customers with contrasting approaches to risk management. PZU, a long-standing Polish insurer, embraced innovation by building a governance framework that allowed for more risk-taking and pilot projects. Conversely, an anonymous UK financial institution prioritized reliability over novelty, positioning itself as a more cautious adopter of new technologies. Both entities required robust governance infrastructure, albeit for divergent strategic objectives.
SAS’s new governance product, Navigator, set to be generally available in summer 2026, is designed to be vendor-agnostic and standalone. It can govern SAS models as well as models developed in Snowflake or sourced from third-party providers. Sglavo believes that governance will evolve into a significant competitive differentiator. "I actually believe this will be a competitive advantage eventually," he stated. "You can say, if you’re using our software, we can guarantee that we are not training on dodgy data."
Navigating Anxiety: The Human Element in the AI Era
Harris opened his keynote by addressing a palpable anxiety concerning the role of human ingenuity in the face of rapid AI advancements. "We are in a crisis, a crisis of confidence of human ingenuity," he declared. "It’s not a collapse in the belief that AI will matter. It’s a collapse in the belief that people will matter."
Sglavo observes this anxiety manifesting within SAS’s customer base. "Most of the time it’s the C-level which says we got to do AI, so people get nervous," he noted. This pressure often results in extensive prototyping but limited production deployment. Flynn expressed concern that organizations relying too heavily on AI as an "easy button" risk atrophying their own core competencies. "If organizations use it as the easy button, they atrophy," he warned. "They never build the fundamentals, and they never think pragmatically."
SAS’s status as a privately held company provides it with the flexibility to resist these pressures. "We can move a little bit slower and take more time with these kinds of decisions," Sglavo commented, referencing the company’s decision not to rush into building its own LLMs, unlike some competitors. Harris concluded his address by acknowledging the current moment as pivotal, but ultimately transient. "There’s no question that this is a pivotal moment, but it’s just a moment," he said. SAS’s strategic bet on foundational strengths, a strategy that has served it well for five decades, appears poised to guide it through the evolving technological landscape.
