Procurement has long been burdened by complexity – slow contract cycles, compliance risks and manual handoffs that eat away at time and efficiency. As AI matures beyond basic chat interfaces and static outputs, procurement leaders are beginning to witness the transition from manual inefficiency to automated agility. What makes agentic
Before we get started, I’d like to acknowledge that we’re inundated with buzzwords – all day, every day. I work in marketing, so I totally understand I’m part of the group to blame (sorry). But hear me out: “AI engineering” not only makes sense, but it’s critical to the future
At SAS Innovate in Orlando, our partners shared how they’re helping public sector organizations modernize operations and serve communities more effectively with SAS®. From using AI and automation to streamline government workflows, to enabling data-driven decision-making that improves citizen services, they’re showing what’s possible when innovation meets collaboration. In
Generative AI (GenAI) adoption is skyrocketing, but are leaders putting too much trust in it? The new Data and AI Impact Report found decision-makers trust GenAI three times as much as traditional machine learning, even though machine learning is more mathematically explainable. Just because GenAI seems trustworthy doesn’t mean it
Agentic AI is generating excitement and being cheered as the next frontier in automation and decision-making. However, beneath the buzz, lies a sobering forecast: Gartner warns that “over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate
Financial institutions operate in a highly regulated, data-sensitive environment. At the same time, these institutions are under pressure to modernize credit scoring models that balance fairness, accuracy and compliance. The challenge? Most financial institutions lack access to the volume and diversity of high-quality, privacy-safe data needed to fuel these models.
Food assistance programs like SNAP are lifelines for millions of households. Yet, ensuring their accuracy is an ongoing challenge for state agencies managing them. Even small errors in eligibility decisions can quickly add up, costing states billions, straining resources and undermining trust in the program. The question isn’t whether these
AI systems have problems. They don’t always work right. They hallucinate, lie, or simply forget the all-important closing bracket in a JSON payload. AI systems need accountability, safeguards and oversight. But what should that look like? Human-in-the-loop (HITL) and its companion human-on-the-loop are in a nascent stage. These strategies, designed
Generative AI has stormed into the enterprise toolkit. According to the IDC Data and AI Impact Report: The Trust Imperative, commissioned by SAS, eight in ten organizations are already using it. It’s become a social phenomenon, too. Many have used GenAI tools like Copilot and ChatGPT, talked about their prompts
I've seen firsthand how companies often treat AI governance as a necessary evil – something bolted on after innovation, like installing smoke detectors in a finished building. As both a data scientist building models and an advocate pushing for responsible AI practices, I experienced this dynamic daily. The companies winning
