When I joined SAS in 1997, most analytics workflows still revolved around desktops, batch processing and highly technical users. Later that same year, SAS introduced SAS/IntrNet – a technology that helped bring SAS analytics into the growing world of web applications. At the time, it felt like a major shift
Tax return registrations and filings continue to rise and fraud schemes are becoming more automated and sophisticated. At the same time, public expectations for fast, digital service are higher than ever. The intensity is real and the data tells a sobering story. In the United States alone, the Internal Revenue
Fraud targeting public sector programs has entered a new phase. Sophisticated fraudsters, armed with AI, are depleting public sector budgets and damaging trust in government – and that’s a reality governments must face. What was once largely opportunistic and fragmented has become organized, industrialized and increasingly cross-border. Fraudsters no longer
When we published our Five Tips for Choosing a Statistical Computing Environment back in 2023, the conversation was about what to look for. If you’re evaluating how to select a statistical computing environment, start with this overview. The following post focuses on what has changed and what a modern SCE
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
Agentic AI has been heralded as a top tool for efficiently orchestrating customer engagement activities at insurance companies. It can: Automate repeatable, rule-based processes that affect customer experience (think claims processing and customer onboarding). Rapidly retrieve disparate data, analyze risks and recommend decisions. Help lower operational costs while keeping humans
A nagging question remains after asking, “What’s next after zero-click search?” How do frontier models – like OpenAI’s o3 series or Google’s Gemini 3 – learn? Garbage in, garbage out Large language models (LLMs) learn from massive amounts of text data, including billions of words, public information, and, you guessed it,
Trust has always shaped how we adopt new technology, and AI is no exception. While trust remains a real and unresolved challenge, today’s adoption gap also reflects a lack of AI literacy. As AI capabilities advance faster than most people can reasonably keep up, individuals are increasingly interacting with systems
Credit processes in banking are at a turning point. Volatile markets, new competitors, increasing regulatory requirements and an exponentially growing database are putting unprecedented pressure on traditional, largely manual and siloed processes. At the same time, customers expect fast and consistent decisions, while regulators demand transparency, traceability, and control. In
As a public sector leader, the pressure to resolve operational issues in your organization is constant, and you may be considering new or better AI models, AI agents or more broadly applied generative AI (GenAI) applications. At the same time, growing use of AI can raise issues related to accountability,
