In the midst of the 'AI winter', when artificial intelligence was barely a theoretical concept, SAS Institute began its journey on July 1, 1976. Half a century later, the company has achieved something few tech companies can boast: remaining profitable, independent, and relevant without succumbing to every technological fad. While giants like Microsoft integrate AI assistants into every corner of their ecosystem and Oracle invests billions in AI infrastructure, SAS has chosen a more thoughtful path: applying artificial intelligence where it truly delivers business value, with the same caution that has characterized its history.

From a university project to a global analytics giant

What began as a research project at North Carolina State University (NCSU) to analyze complex agricultural data transformed into a four-person company that today employs 11,000 people in 39 countries and generates over $3 billion in annual revenue. Co-founder and CEO Jim Goodnight recalls those early days with humor: "When we founded the company, our goal was to make it to the end of the year without going bankrupt. In fact, that year we even made some money."

SAS software began to be distributed outside the university in 1971, and by 1974 an NCSU press release described it as "an important contribution of North Carolina State University to data analysis worldwide." After the first user conference in January 1976, Goodnight, along with A.J. Barr, John Sall, and Jane Helwig, decided to incorporate the company. Barr and Helwig sold their stakes a few years later, leaving Goodnight and Sall as co-owners to this day.

From about 150 initial clients, SAS's customer base has grown to approximately 80,000 sites in 150 countries. Although its name may not be known to the general public, its software powers critical functions such as data analysis in clinical trials for major pharmaceutical companies, pricing strategies for global retailers, and anti-money laundering and fraud detection systems for numerous banks. Sectors like aerospace, environmental protection, retail, and manufacturing rely on SAS for data management, risk management, corporate governance, decision intelligence, marketing, and fraud prevention.

AI with a head: SAS's prudent approach

At a time when the tech industry seems obsessed with generative AI, SAS keeps its feet on the ground. Goodnight shows healthy skepticism: "People are spending a lot of money guessing the next best word in a sentence." For SAS, the focus is on using AI to make its software easier to use and its results more self-explanatory, avoiding unexpected costs for customers.

Unlike other vendors that charge per AI call, Goodnight explains: "When you make an AI call, it actually goes back to SAS, and we run it here at no cost to the customer, so we've tried to add all our AI capabilities without charging for them, because we have domain knowledge." This approach is not only honest but also reinforces customer trust, something that in digital transformation processes is fundamental.

Relevance amid media noise

Bryan Harris, CTO of SAS, acknowledges that the acceleration of technological change has been positive for the company, but also more demanding. "We have to be relevant in the media noise of a new technology and, above all, extremely relevant in its reality," he says. This means discarding practices like tokenmaxing, which he considers a "vanity metric," and focusing on business impact and financial responsibility.

Harris is blunt about the risks of AI: "When you start talking about automating business processes with agents and there's an error rate between 10% and 30%, that's not positive or something clients can base their careers on." That's why SAS shows its clients how to overcome that error rate, how to use its technology to do so, and the need to understand the risks of the technology to apply it in the right use cases and areas for the business.

This pragmatic approach recalls other technology adoption strategies we've seen in the sector, such as OpenClaw: the AI that doesn't touch your mobile, where the key is to apply technology where it truly adds value.

Beyond generative AI: R&D in physical AI, digital twins, and quantum computing

In addition to improving the accuracy and governance of AI agents, SAS is investing in other R&D areas such as physical AI, digital twins, and quantum computing. These technologies, though less media-hyped, have enormous transformative potential in sectors like logistics, manufacturing, and energy. For example, digital twins allow simulating and optimizing complex processes before implementing them in the real world, something we are already seeing in digital transformation success stories.

A culture that endures: the human factor as a competitive advantage

Despite technological changes, SAS has maintained its value system and people-centered leadership style intact. The company has repeatedly appeared on lists of the best places to work in the United States and globally. Goodnight, now 83, reflects: "I think we've built a great culture. I hope those who lead us into the future remember how to treat people, how to be good to them, and how to pay them well. I hope this continues in the coming years."

This philosophy of putting people at the center is not only ethical but also smart from a business perspective. In a market where talent turnover is high and cybersecurity is critical, having stable and committed teams is a strategic advantage. As we've seen in technology alliances for security, trust and collaboration are key.

Lessons for the IT ecosystem

SAS's story offers several lessons for companies and professionals in the sector. First, the importance of maintaining focus on real customer value, above trends. Second, that independence and sustainable profitability are possible even in a market dominated by giants. And third, that corporate culture and treatment of people are differentiating factors that outlast any technology.

In a world where AI is advancing by leaps and bounds, SAS's prudence is not synonymous with slowness, but with maturity. As its CTO rightly points out, the key is to be relevant in reality, not just in the noise. For companies looking to strengthen their infrastructures or optimize their dashboards, SAS's example shows that technology must serve the business, not the other way around.

Original source: ComputerWorld. Analysis and adaptation by ForgeNEX.