Sas Inovati: The true advantage of him? Is not what you think
Ask most people what he gives her advantage and they are likely to tell about the speed, automation or aura of him.
But according to experts at SAS INOVATE 2025, the true competitive advantage of it is not the algorithm – is the ability to use it responsibly to make reliable, faster, better decisions.
As Sas approaches its 50th anniversary, the leading technology official Bryan Harris used the historical point not to look back, but to set up a bold way forward: building smartest, reliable systems.
Here are five main receipts from the SAS Innovate opening session with Harris-filled with real-world examples and quotes that challenged conventional thinking about the value of it.
1. The superpower of that is the intelligence of the decisions
Over the years, we have seen waves of technological advances: first came PC and the Internet, then cloud, machine learning and deep learning. Now, everything has to do with him – in every imaginable aroma: the generating, the agent and the quantum he. The rapid pace of innovation can feel great, even for companies that build it.
“This rhythm of innovation can feel intense and overwhelming, even for companies that create it,” Harris said. “But every subset and he is stirring one thing: the intelligence of the decisions.”
The true value of it does not stand in what it creates, but in the decisions it enables.
You are not just looking for any decision. You need a decision that gives out the outgoing results. You need a decision that helps you compete to win in the market. You need a decision advantage. Bryan Harris, Sas Cto
2. The generator will not fix the broken business models
The generative is boiling in areas like customer service and text summary, but it is not a magical arrangement for all business problems. Harris called the myth that simply adding that generator to a process will solve all your issues.
“It feels like all you have to do is sprinkle a small generation in the enterprise and all your problems will disappear magic,” he said during his marking.
While the generating has impressive applications, it also has serious restrictions, especially when it is not properly implemented.
“You can have a good, impartial pattern of it – but feed that wrong data and have a one -sided result,” Harris warned.
In fact, a recent study found that large language models tend to recommend higher interest rates and loan denials for black applicants compared to white applicants, even when credit results are the same. On average, black applicants need credit scores 120 higher points to be approved at the same rate, according to the study.
Harris emphasized that such discrepancies are systemic in large LLM. A simple solution, like adding more emphasis on rapidly, does not resolve the issue.
“That is why LLMs and fast engineering are just not enough for most cases of enterprise use,” he said. “You need an orchestration of LLM, machine learning, API and more to ensure accuracy, justice and governance.”
This indicates the displacement of the concentration of the industry from the generating to the agent-moving beyond the creation of content in making decisions directed by that which is fair, accurate and effective.
3 by the Hype generating and he in the agent reality of it
While the world of technology still loves the generation, Sas is already deciding what is another: he Aicic – systems that reason, act and cooperate with or without human contribution.
And it’s walking fast. The global agent market it is expected to exceed $ 70 billion by 2030, with 52% of organizations adding agents to work this year.
So what does Sas with that agent mean?
“Sas thinks it as a spectrum between” man from loop “and” man in loop “,” Harris said.
“After all … we don’t give you just one agent – we give you transparency and explanation.”
At one end, agents act autonomously, such as denial of real -time fraudulent transactions. On the other hand, they flag irregular cases, provide risk results, and provide context for human review.
This is the common intelligence in action – already at work in SASABOUT Intelligent decision -making. In a demonstration, Harris showed how SasABOUT ViyaABOUT It can flag mortgage cases, explain its reasoning, and give reviewers full transparency from the model cards in the decision line.
These agents are not limited to fraud or credit. SAS customers use them in industry. They are built in low -code environments and combine LLM, rules, models and api in scalable, reusable work flow.
The result? He smarter, safer and more responsible with governance and justice built.
4 What if … digital twins can think as operators?
What if you can ask your factory floor, “What if?” And get responses supported by real data, simulation and he?
This is what Sas, Epic Games and Georgia-Saccharor are building together.
In the Mill of the Savannah River of Georgia-Pacific, which is over 400 meters tall (larger than four football fields), they built a digital twin to prove how autonomous directed vehicles (AgV) move through the facility. These cars are critical, but finding the right size of the fleet and course is difficult.
“Today’s manufacturing plants are complex environments with AGV, robotics and people working together,” explained Roshan Shah, VP I in Georgia-Pacific. “When things go wrong, it gets expensive and people can get hurt.”
Using Sas Viya, the team led simulations within Unreal Engine to try AGV strategies. They assumed that more AGV would improve productivity, but the digital twin showed different.
“The addition of AGV seems intuitive, aren’t it? More workers have to mean more production,” Shah said. “But in Unreal, the cost of the extra AGV actually slowed down things.”
The optimal number turned out to be 47, increasing the performance by 8%.
“Instead of building and testing these solutions in the real world,” said Bill Clifford, VP of the UNREAL engine ecosystem in epic games, “a click allows us to find the best configuration among all possible worlds.”
Sas even added a “factory editor” to simulation, letting managers adjust the submission, reassess the AGV, or differ between real and synthetic data on a gamelia interface.
As Shah said, “Imagine being a manager of plants and be able to adjust the operations in this way. It is a full game switch. Target work!”
This is not just visualization. This is the decision -making made by him and the comprehensive technology.
Understanding twin digital technology
5. Transforming optimism with that quantum
Harris led to Quantum he as a game shift for dealing with large -scale optimism problems.
“Imagine having a digital twin that answers your most difficult questions” what if ‘right away, “he said, mentioning how the quantum accelerates data processing from hours or days in minutes.
He referred long collaborations with global organizations like Procter & Gamble. “We’ve worked together for decades to improve performance and reduce the cost,” Harris said. “Now we are exploring how quantum he can lead him to the next level.”
Whereas Krista Comstock, director of digital innovation in P&G, to say, “one of the most significant obstacles is to improve productivity and performance. Quantum, being a problem -problem solver, does not always give the best answer, despite being faster. the good of both worlds: with speed and quality of solution. “
From reducing the time of calculation to increased efficiency of digital twins, hybrid-traditional quantum approaches are already showing true promises in transforming the direction on the scale.
Ultimately
He may be evolving quickly, but SAS is helping clients evolve faster, with means that make any more intelligent, explanable and effective decision.
Whether it is done through digital twins, the Aitic or Innovation Quantum he, one thing is for sure: the future belongs to those who build confidence in every layer of their technology.
