SAS’ Alyssa Farrell on Data Management, Quantum, and AI Positioning

During SAS’s Innovate 2026 event recently, SAS announced it would refresh its SAS Data Management portfolio, a cloud-native offering built on the SAS Viya data and AI platform.

Among the new and expanded capabilities are AI-ready data management, governance by design, agentic AI and copilots, and cloud-native analytics acceleration.

“A modern data platform is not 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.”

Channel Insider sat down with Farrell during SAS Innovate to discuss this refresh, quantum computing, and more in data and AI.

SAS’s Data Management Refresh

According to Farrell, the refresh was driven primarily by evolving customer needs around scaling AI, supporting open architectures, and improving governance.

“I think that there were a number of factors that we were looking at in the market – what customers needed – to be able to really scale AI, and also prepare for agents,” said Farrell. “A lot of that had to do with being able to take advantage of some open architecture.”

She noted that SAS has placed an emphasis on interoperability, consistency, and preparing for future technologies such as AI agents and quantum computing.

“We see that a lot of organizations are using multiple open file formats and our ability to actually work across those file formats,” Farrell said. “Instead of building different workarounds … we built that into the architecture, [and] looked at increasing the governance and lineage in a more consolidated way.”

Quantum computing: Current state and future outlook

In furthering the company’s data and AI posture, SAS also announced an upcoming offering to equip organizations with quantum AI.

While still in the experimental phase, Farrell noted that organizations are testing hybrid and simulated approaches for quantum AI, particularly for complex problems in finance and life sciences.

“I think that what we’ve seen from the quantum computing side is that a lot of the computing resources might not mature fully until like 2029,” Farrell told us. “But organizations today are starting to experiment with a quantum approach.”

“You can kind of think about it like: is the juice worth the squeeze? Is the cost of running this in quantum going to be that much faster or more efficient for me in this complex problem I’m trying to solve?” she added.

Why SAS is betting on quantum becoming a spectrum of workloads

SAS’s stance is to view classical and quantum computing as a spectrum, with proven classical computing at one end and experimental, exponentially more powerful quantum computing at the other. 

On this spectrum, SAS notes that many industry and business problems fall somewhere in the middle, with a hybrid approach splitting workloads.

In Q4 of 2026, SAS will launch SAS Quantum Lab for SAS Viya customers – a launchpad for their quantum AI journeys and to help those ready to explore, test, and validate their quantum work.

Positioning in the Data & AI ecosystem

Farrell explains that SAS provides a data intelligence layer that can integrate with other platforms while offering prebuilt capabilities that reduce complexity and cost. It emphasizes flexibility in heterogeneous IT environments.

“SAS provides a data intelligence layer, and we can either do that on top platforms like Snowflake and Databricks, or we can be that data platform,” Farrell said. 

“The data intelligence layer that we bring is very much differentiated, because we’re still providing more pre-built data management and governance capabilities … you really kind of have to look at the total cost of ownership.”

Total cost of ownership and governance are foundational rather than restrictive in SAS’s view – enabling faster, safer innovation. Context, through data models, knowledge graphs, and architectures like Retrieval-Augmented Generation (RAG), is key to making effective decisions.

“If someone asks me: how do I start to move with AI at scale? We say: ‘You’ve got to have the governance and guardrails built in first,” Farrell explained. “The data model is what’s also driving the context: I’m looking for this type of data to do this type of thing, to drive this decision.”

Workforce impact and the role of humans in AI

As layoffs increase across sectors and for large IT enterprises, workforce impact and the human role in AI were big topics of discussion at SAS Innovate.

While there are concerns about job displacement, SAS’s focus throughout the conference was on human value (i.e., creativity, problem-solving, and innovation), which remain essential. 

SAS is encouraging organizations to balance technological advancement with people and data.

“The most important thing today is people and data … if you have good people who believe that their work matters and you have good data that you can trust, you can figure out the rest,” Farrell told me. “Human ingenuity fills the gaps of building the agents, making the original workflows, and solving the complex problems. Focus on people, focus on data.”

Channel Insider was also able to chat with SAS’s Senior VP, Global Channels, John Carey, at the conference. Read more about how SAS is strengthening its partnerships while positioning itself around human-centric, responsible AI.