AI readiness is often framed around models, technology and talent. In practice, it starts with something more fundamental: trusted data. Without clean, integrated and governed data, even the most sophisticated AI systems can produce unreliable results and erode confidence across the business. I often meet with data industry professionals and
Most leaders do not have an AI problem. They have a readiness problem. In many small and midsized businesses, pilots are underway and use cases are defined. But when leaders step back and ask the harder question, "Is AI actually paying off?", the answer is often less clear. This gap
A quick search through headlines reveals a range of AI-related disappointments. Consider that 95% of GenAI pilots fail, according to MIT. Amazon’s Kiro agent recently sparked a 13-hour outage by deleting a production environment. And we can’t forget that the resource and energy strain from a new wave of AI
Marketers have made enormous strides with generative AI (GenAI) over the last year, moving from experimentation to large-scale deployment. But the next shift in AI maturity – agentic AI – is already underway. This shift will push organizations beyond prompt-based productivity and into a world where AI can act, learn
Georgia-Pacific didn’t treat AI as a science project or a luxury add-on while researching solutions. They made it core to operations, scaling intelligent decisions across an enterprise as massive as their manufacturing footprint. Georgia-Pacific manufactures products used every day, like bath tissue, boxes and lumber, across more than 150 sites
Hear from our experts how insurers can create AI safeguards and ensure safe model use.
For small and midsize businesses (SMBs), AI is no longer optional. But for many leaders, the real challenge is making those investments pay off. That’s where things often break down. Too many AI initiatives start with tools instead of outcomes. They stay stuck in pilot mode. Or they deliver insights
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
Every patent starts as a problem someone couldn't ignore. In 2025, SAS and JMP inventors earned 82 patents – spanning natural language processing, AI, fraud protection, IoT and more – each one rooted in the kind of friction that real organizations actually deal with. At SAS' annual Patent Dinner, the
The regulatory submission process in life sciences is becoming less about assembling documents and more about proving trust. For years, submission readiness was largely treated as an end-stage operational milestone: finalize the analysis, validate the outputs and package everything for regulators. But that model is beginning to break down under
