Much of the conversation around agentic AI focuses on autonomy. How much work can an agent complete on its own? How many decisions can it make? How far can organizations remove people from the process? Those questions make sense, but they can also obscure where businesses are finding value today.
Organizations rarely struggle to build AI models. They struggle to scale them. Across industries, organizations continue to invest heavily in data and AI, yet many initiatives never move beyond pilot projects. The challenge isn't creating better models. It's maintaining the infrastructure that surrounds them. Data pipelines, governance, business rules and
AI is evolving quickly, but where is it headed next? At SAS Innovate 2026, we asked customers, partners and industry leaders a simple question: What do you think the future of AI looks like? Their answers touched on everything from agentic AI and trustworthy AI to the role of human
The consumerization of health care is no longer a future-state concept. It is here, and it is reshaping expectations faster than many organizations are prepared to meet. Patients today are not just recipients of care. They are consumers comparing experiences across industries. They expect the same level of personalization, convenience
Every missed appointment affects more than one patient. It delays care, leaves valuable clinical time unused, disrupts schedules and creates additional work for care teams already operating under significant pressure. For health care organizations facing growing demand and limited resources, reducing patient no-shows can improve both patient access and operational
There is no question that AI in marketing is here to stay. According to DemandSage, “56% of brands now actively use AI to tailor every customer interaction (content, recommendations and support), while 96% of companies report AI has significantly improved their personalization ROI.” And it does pay off: DemandSage research
AI gets the spotlight. Data does the work. Across industries, organizations are racing to adopt large language models (LLMs), build AI assistants and explore autonomous agents. This ambition is understandable. AI promises faster decisions, new efficiencies and entirely new ways of working. But amid the excitement, many leaders are overlooking
Small and midsized businesses are entering a defining period for AI adoption. Data, analytics and AI platforms are becoming essential for organizations that want to compete, adapt and grow. These technologies can help smaller businesses improve decisions, automate processes and respond more quickly to changing customer needs. But access to
Organizations have spent the past few years experimenting with AI. The question now isn't whether AI can generate insights. It's whether organizations can consistently turn those insights into business decisions that create measurable value. That's where many AI initiatives stall. Models perform well in development but never make it to
For many applicants, the permitting process feels like a black box – slow, complex and often frustrating. That perception isn’t unfounded. Across agencies, permitting has long relied on manual reviews, paper-heavy workflows and disconnected systems. The result is predictable: delays, inconsistencies, errors, undetected fraud, and limited visibility for both regulators
