Jay Upchurch, EVP and CIO at SAS, on why data readiness remains the biggest challenge in AI implementations and why India is central to SAS’s global innovation and growth strategy.
Artificial Intelligence has evolved from a buzzword to a boardroom strategy, yet many enterprises remain stuck between proof of concept and scaled deployment. Jay Upchurch, SAS’s Executive Vice President (EVP) and Chief Information Officer (CIO), is at the intersection of internal transformation and customer enablement.
SAS, a US-based analytics and AI software company founded in 1976, empowers data-driven industry decision-making. Jay leads the company’s global IT organization and heads the SAS Managed Cloud Services business. This dual role positions him uniquely to witness—and shape—how organizations adopt and operationalize AI at scale.
In a recent interaction with Jatinder Singh, Executive Editor of CIO&Leader, at SAS’s Pune, India office, Jay shared insights on why data readiness remains the biggest hurdle, how SAS Viya is enabling scalable and trustworthy AI, and why India is central to SAS’s global innovation and growth agenda. Excerpts from the interview:
CIO&Leader: AI is now at the centre stage. While most enterprises are exploring AI, very few feel confident about moving from pilot to production. Why do you think this is the case?
Jay Upchurch: Let me give you some context. I have two roles at SAS: I lead internal IT and oversee our hosted and SaaS offerings. So, I see both how we use our technology and how customers adopt it.
AI has existed as a concept since the 1950s. What has changed recently is public awareness, mainly due to Generative AI. While SAS has led the AI space for decades, GenAI has made it mainstream.
Many CIOs were drawn to GenAI because of its accessibility—it started at the consumer level; however, in enterprises, where accuracy, predictability, and accountability matter, using GenAI unthinkingly isn’t viable. You need the proper AI technique for the right business problem.
For instance, we use GenAI-based tools like Microsoft Copilot and ServiceNow’s Now Assist to boost productivity. But beyond GenAI, we employ other AI methods across our operations. The key is not to force GenAI into every scenario but to be methodical in choosing what works best.
Take life sciences: if you are building a model for drug development, you can’t have an output that’s “probably” right—human lives are at stake. So precision and trust are non-negotiable.
AI aspirations often fail because the data foundation is weak. I see this with our customers and even among my peers in the CIO community. Before you do anything with AI, you need to modernize your data estate, establish strong governance policies, and ensure data hygiene.
Without this, no AI initiative—GenAI or otherwise—will scale effectively. As CIO, my job is to ensure our internal data architecture is sound and to bring in the right AI tools with appropriate guardrails. But beyond tools, we also need to focus on AI literacy, educating the entire organization on what AI can do and how to use it responsibly and productively.
CIO&Leader: So, data readiness and AI literacy are critical. How are you approaching this from an organizational culture perspective?
Jay Upchurch: It starts with storytelling and education. We are working to demystify AI across the company, especially to counter fears like “AI is going to take my job.” AI isn’t replacing people; it’s augmenting their capabilities.
We show our teams how to use AI to reimagine their work—not just to be more productive but to think differently. Once people start experimenting, curiosity takes over.
I refer to my internal employees as customers, and I want to ignite their curiosity so they actively look for new use cases and ways to apply AI. That’s how AI becomes a part of the organizational DNA.
CIO&Leader: SAS recently committed over $1 billion in AI investment. Could you elaborate on how this is used, especially in responsible AI?
Jay Upchurch: Yes, we’ve made two separate $1 billion investments:
- The first is aimed at advancing our AI and platform capabilities.
- The second is focused on building industry-specific solutions, driven by customer demand.
A significant part of this is our Data Ethics Practice, led by Reggie Townsend [Vice President of the Data Ethics Practice at SAS]. Reggie is globally recognized in AI policy and ethics. He advises governments, including the U.S., UK, and Singapore, on responsible AI usage. His goal is to help shape a global standard for ethical AI.
One innovation from this practice is the “Model Nutrition Label”—a transparent summary of how a model was built, what data was used, and how decisions are made. It’s akin to a food label but for AI. This helps organizations deploy models confidently and accountably, minimizing unintentional bias and ensuring regulatory compliance.
CIO&Leader: Besides the ethics practice, what industry-specific innovations are funded by this investment?
Jay Upchurch: Absolutely. Beyond responsible innovation, we’re building domain-specific AI solutions. Some examples:
- In banking, we help assess lending pressures and liquidity exposure.
- In government, we offer fraud detection and anti-money laundering platforms. For example, His Majesty’s Revenue and Customs in the UK processes 250,000+ tax filings daily using SAS to detect fraud, triggering human-in-the-loop investigations when anomalies arise.
- In life sciences, we support precision-driven drug development pipelines.
These aren’t generic AI platforms but tailored solutions built on domain expertise and critical business outcomes.
CIO&Leader: Is SAS’s AI investment part of a global strategy, or is there a dedicated focus and allocation for India?
Jay Upchurch: Great question. Over 50% of our revenue comes from outside the U.S., and India is one of the fastest-growing markets in our Asia-Pacific region. We operate in over 120 countries, and India is a crown jewel in our growth strategy.
We have three offices in India, with Pune being our significant R&D and delivery hub. We are seeing strong growth in banking, insurance, and government here. And India’s retail and manufacturing sectors are ripe for advanced AI adoption, especially in IoT and computer vision.
Globally, we’re bringing AI innovations from one sector or geography and adapting them to others. India will benefit significantly from this cross-pollination.
CIO&Leader: How do you see SAS supporting startups and digital-native businesses in India?
Jay Upchurch: These companies are built with data at their core, and the demand for scalable, ethical, and high-performing analytics is only growing. For them, the foundation must be robust—arguably more so than traditional enterprises. SAS has the platform capability, ethical frameworks, and industry knowledge to support them across their journey, from experimentation to enterprise-grade deployment.
CIO&Leader: As a technology leader, what strategic priorities are currently guiding your agenda?
Jay Upchurch: I have five major priorities, but let me share the top three.
First, I am focused on enabling the company’s future through technology. That includes transformation initiatives—like building a strong AI foundation—and preparing for our eventual IPO aspirations. We’re executing a significant back-office modernization effort, especially around lead-to-cash processes.
Second, I’m committed to delivering an exceptional customer experience. I also lead our managed cloud services business, and we’re constantly exploring how to provide more transparency and self-service capabilities for our customers, so they have better visibility and control over their environments.
Third, I am working to grow our cloud business. A lot of our customers are still on legacy systems. We help them modernize and migrate to the cloud, allowing them to leverage cloud-native capabilities that reduce cost and drive productivity. We’ve built Viya specifically with this modernization journey in mind.
CIO&Leader: How does Viya help organizations scale AI effectively, especially in the Indian context?
Jay Upchurch: Viya is indeed our flagship AI and analytics platform. It is fully cloud-native and built for portability. It supports hybrid environments—on-prem, cloud, or Kubernetes-based.
We look at the AI lifecycle in three parts:
- Data preparation: Governance, access, and transformation of data. One of the unique strengths of Viya is its ability to leave data where it is—be it Snowflake, Databricks, or Azure Data Lake—and still manage and analyze it efficiently.
- Modeling and analytics: This is where data scientists can leverage built-in AI capabilities, conduct modeling experiments, and select the best models through champion-challenger approaches.
- Deployment & governance: Once models are ready, Viya allows them to be deployed as containerized runtimes. These are traceable, monitorable, and explainable, critical for ensuring trust.
A noteworthy example is Georgia-Pacific, which runs over 15,000 SAS models in manufacturing environments, all of which are tracked for efficiency and accuracy.
CIO&Leader: You’ve spoken before about India being a critical hub for SAS. Given the demographics here, how do you view India’s role in shaping the future of work and AI?
Jay Upchurch: India has a huge demographic advantage—the average age is around 28 years. That’s remarkable when you compare it to many other parts of the world. And with that youth comes both energy and expectations.
What I love most about being in India is how the younger workforce is already AI-native. They use AI tools in their personal lives, bringing that openness and comfort into the workplace. That kind of early-career digital readiness becomes a powerful catalyst for adoption and innovation.
In many ways, India becomes a microcosm of the future of work. We are seeing extensive internal adoption of AI, not because we are pushing it, but because the workforce is pulling it in. That’s a compelling dynamic.