The conversation around AI agents has moved remarkably fast. Just two years ago, organizations were experimenting with generative AI through chat interfaces and copilots. Today, many are exploring autonomous and semi-autonomous agents capable of reasoning, making decisions, interacting with systems and executing business processes. As organizations look beyond that, they’re
Some of the best ideas don't start with a finished solution. They start with a problem worth solving and the opportunity to experiment. The SAS Hackathon is a monthlong global innovation experience that brings together partners, customers and SAS experts to tackle real business challenges using data and AI. Along
“It’s not a technology problem. It’s a process problem.” “It’s not an accuracy issue. It’s an adoption issue.” “It’s not the model. It’s the human.” Sound familiar? I hear these arguments all the time. But here’s the reality: generative AI has an accuracy problem. In fact, it is well documented
A model can finish running in seconds. Understanding what it is telling you can take much longer. A fit statistic signals how well a model performs. A residual plot reveals whether assumptions hold. A decision tree shows how observations are split into groups. But before any of that can support
Customer engagement is entering a new era that’s evolving very quickly. And it’s not defined by more messages or channels – it centers on intelligence, autonomy and trust. As organizations rethink how they connect with customers, a new model is emerging: engagement that predicts, learns and acts with purpose. In
A bank deploys a new AI-powered credit decisioning model. The technology works. The model performs well. The business team is ready to move forward. Then the questions begin. Can we explain the recommendation? What data was used? Who approved the model? How do we monitor performance over time? What happens
Editor’s note: This post was co-authored by Diana Rothfuss. For the past few years, banks have focused on proving that AI can deliver business value. Across financial services, organizations have launched pilots, explored generative AI use cases and tested how AI can improve everything from customer engagement and fraud detection
AI adoption continues to accelerate, but not everyone approaches AI the same way. Some people experiment with every new tool they encounter. Others prioritize governance, trust and oversight. Some focus on business outcomes, while others are driven by curiosity and exploration. These differences matter. How you engage with AI influences
AI agents are moving beyond individual productivity and into government operations. According to Gartner, at least 80% of governments will deploy AI agents to automate routine decision-making, enhancing efficiency and service delivery by 2028. Because of this, government leaders are challenged to determine where they can deliver value with AI agents while maintaining public trust. Their
Today, your life is being affected by decisions made by AI and machine learning systems. These technologies influence everything from hiring and lending decisions to the content you see online. When those decisions produce harmful outcomes, many people worry about the technology itself. But the greater risk beyond AI becoming
