SAS

Leaders are left scrambling to feel confident about whether AI is being used responsibly, creating a widening gap between what leaders want from AI – innovation, efficiency and competitive advantage – and what they can confidently manage in practice. Only 24% of AI projects have adequate security controls in place, while 82% of executives say trustworthy AI is essential. This emphasizes the disconnect between intent and execution.

When people talk about gut health, they often jump straight to probiotics, skipping over the foundation — feeding existing gut microbes a wide selection of plant foods. A variety of plants offer different fibers, resistant starches, and polyphenols that fuel beneficial microorganisms and support a diverse, resilient microbiome. In recent years, scientists have revealed a simple yet powerful guideline for boosting your gut microbiome — aim for at least 30 unique plant foods each week.

But AI may be creating a different kind of competition. While large banks continue to invest billions in AI initiatives, community banks, credit unions and regional lenders have an advantage of their own: They can often move faster. They can test, learn and adapt without the complexity that comes with steering a massive organization. The question is whether smaller banks are taking advantage of that opportunity.

It’s an uncomfortable question. But it sits at the center of nearly every conversation about productivity, automation and the future of work. Across industries, work is increasingly framed in terms machines can understand: outputs, efficiency gains, throughput. Are these necessary metrics? Sure. But they’re not neutral. They shape how we define value – and, by extension, how we define what people are for. And that’s where the risk emerges.

Pareto charts are used by quality engineers to visually display a set of causes that produce defects in a manufacturing process. The chart is based on the famous Pareto Principle (the 80/20 rule), which states that 20% of the causes often produce 80% of the defects. If you focus on the most common causes, you can quickly reduce your defect count. This article describes an alternative to the standard Pareto chart and shows how to create the alternative version in SAS.

At SAS Innovate 2026, SAS partner Notilyze took a different approach. Rather than leading with a traditional demonstration, Notilyze showcased an interactive soccer game that encouraged attendees to play first, then discover the analytics behind the experience. Built on SAS® Viya®, the experience combined gamification, real-time analytics and data visualization to show how raw data can quickly become actionable insights.

In an interview, Merrill reflected on discovering SAS through a small notice in Datamation magazine. At the time, the software was new, largely unknown and available for $100. "North Carolina State University's statistics department announces the availability of the Statistical Analysis System," Merrill recalled reading. The software package contained roughly 100,000 lines of code and represented a new way to analyze and understand data. What happened next left a lasting impression.

A recent article discussed Pareto charts and how to create them in SAS. A Pareto chart is used in quality control to identify the most likely reasons that a manufacturing process produces defective items. Suppose there are k different reasons why a process can fail, and you intend to randomly select N defective items to examine. The number of defective items for each reason can be modeled by using a multinomial distribution.

Through keynote presentations, hands-on learning and conversations with peers, attendees left with fresh ideas, stronger relationships and renewed excitement about what’s possible with data and AI. But don’t take my word for it. Hear from some of our attendees on why they loved Dallas. It’s not over yet! SAS Innovate on Tour kicked off with events already hosted in Warsaw (May 19), Frankfurt (May 21), and Madrid (May 28) and will continue around the world for the rest of 2026.

When good insights lead to confusing experiences Banks know a lot about their customers. They know how often someone logs in, what products they use, how they tend to spend or save, and even what they might need next. That level of insight has never been the problem. The problem is what happens next. A customer might receive an email encouraging them to open a savings account. At the same time, they might see a message in their banking app about a credit product.