As SAS celebrates its 50th anniversary, one question naturally follows: What comes next? Few groups have a better vantage point than the partners who work alongside SAS every day. They help organizations apply analytics and AI across industries, giving them a front row seat to the challenges customers are trying
Most conversations about agentic AI focus on how autonomous these systems can become. Financial services leaders are asking a different question: Where should autonomy stop? For banks and insurers, the challenge is not simply deploying AI agents. It is determining how those systems, governance requirements and human expertise can work
AI is reshaping how work gets done across industries. In marketing, the real challenge is turning a multitude of ideas into a final campaign. A typical campaign often starts with a strategy meeting, followed by sessions with data teams and creative reviews. Stakeholders weigh in at different stages, sometimes with
Is the roof over your head about to cave in? Did you just change jobs, get married or start planning for retirement? In situations like these, most people think about their insurance. Life changes are a great time to evaluate insurance coverage (for customers and insurance companies alike), but waiting
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
Many AI initiatives don’t stall because of the models – they stall because of the data behind them. Teams can build promising prototypes, but moving those models into production is where progress slows down. Data is spread across systems, pipelines are difficult to maintain and governance often lags behind how
For 50 years, SAS has worked alongside organizations tackling some of the world’s most complex challenges. While the technology has changed dramatically, one thing remains constant: our customers’ commitment to solving problems, improving outcomes and making a meaningful difference. In the first half of 2026, we shared 26 new customer
Organizations know AI is important. They’re investing in it, encouraging teams to use it and looking for ways to scale its value. But many organizations are still struggling to turn that priority into governed, scalable action. 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
For years, the assumption has been that AI would primarily benefit the largest banks – those with the biggest technology budgets, the deepest talent pools and the resources to experiment at scale. But AI may be creating a different kind of competition. While large banks continue to invest billions in
What if the real disruption in AI isn’t that machines are becoming more capable but that we’re quietly redefining work in ways that make humans less so? 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,
