Throughout history, the ambition of technological advancement has always been driven by the pursuit of human prosperity. But with AI, we are approaching a pivotal gut check to live up to this promise for the next generation. In my role as CTO at SAS, I talk with a lot of
Responsible innovation is more than a framework. It’s the discipline of building technology that earns trust. As AI becomes more capable and deeply embedded in business workflows, organizations must ensure that every decision, design choice, and workflow supports accountability, transparency and human-centered outcomes. The Responsible Innovator Spotlight is a new
AI는 금융을 비롯한 다양한 산업에서 혁신을 가속화하고 있습니다. 하지만 그 이면에는 데이터 편향, 환각(Hallucination), 개인정보 유출 등 새로운 리스크가 빠르게 증가하고 있죠. SAS는 지난 9월 17일 ‘금융기관을 위한 AI 거버넌스 및 미래 혁신 전략 세미나’를 개최하고 AI 기본법 시행을 앞둔 현 시점에서 금융기관이 어떤 준비를 갖춰야할 지에 대한 핵심 내용을
Generative AI (GenAI) has seemingly permanently shifted the trajectory of enterprise strategy, offering a way to transform massive amounts of unstructured data into actionable, contextual intelligence. However, as organizations move past pilot projects and scale this technology, they run headfirst into a widening gap between the potential of AI and
For years, “responsible AI” has lived comfortably as a corporate promise, a slide in a presentation, a talking point at a conference. But as the EU AI Act phases into force, that comfort is rapidly eroding. The regulation officially entered into force on August 1, 2024, but its obligations will
Discover how to establish a foundation for success with AI in insurance.
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
A new report from MIT estimates 95% of AI projects will fail. That’s staggering. Corporate GenAI pilots are failing to deliver significant revenue growth or measurable impact. The primary issues are not the quality of AI models, but a “learning gap” and flawed enterprise integration. Recent findings from the SAS
Every organization collects data, but collecting it isn’t enough. For companies that want to make personalized offers, detect fraud and optimize supply chains, decision-making is the ultimate measure of analytics success. But despite massive investments in data infrastructure and AI, many companies still struggle to bridge the gap between insight and action. That’s
Every AI success story starts with a single decision: to move beyond experimentation and commit to real-world impact. But moving from idea to enterprise-scale deployment isn’t just about algorithms – it’s about laying the right groundwork. In the first part of this series, we explored three ways to lay the
What if the data behind your next prescription could help save lives – not just yours, but millions more? Behind the scenes, researchers are turning everyday health records into powerful insights that shape the safety of drugs and medical devices. At the heart of this work is Cheryl Walraven, a
Agentic AI is more than just the next step in automation. It’s a shift toward systems that don’t just respond to commands – they reason, decide and act with a blend of analytics and embedded governance. This is the second blog post in a series exploring how to design AI
The financial services industry is undergoing a period of profound change, driven by a dynamic economic landscape, increased regulatory scrutiny, changing consumer behavior and rapid technological advances. Banks operating in this environment are under increasing pressure to transform their risk modeling and decision-making ecosystems in order to remain competitive. This
AI agents are no longer confined to labs and prototypes. They’re shaping how we live, work and make decisions. From customer service bots and self-driving cars to robotic surgical assistants and virtual companions, these systems now influence real outcomes in society. This is the first blog post in a series
Navigating the interpretability paradox of autonomous AI: Can we maintain trust and transparency without sacrificing performance? AI has rapidly evolved from simple, rule-based systems into sophisticated autonomous agents capable of making decisions without direct human oversight. These advanced systems, known as "agentic AI," go beyond basic automation to independently sense
As AI agents gain autonomy, who governs their actions? How do we ensure they align with human values, ethical standards, and legal frameworks? The urgency of governance for AI agents AI is no longer just a tool – it is becoming an actor in decision-making processes. From AI research assistants
Social benefits are crucial government services, especially during downturns, helping families navigate uncertainty and survive. Today, citizens interact with government services online, from applying for benefits to making appointments. This was particularly beneficial during the COVID-19 pandemic, allowing services to remain accessible while maintaining social distance. However, going digital also
AI governance is an all-encompassing strategy that establishes oversight, ensures compliance and develops consistent operations and infrastructure within an organization. It also fosters a human-centric culture. This strategy includes specific governance domains such as data governance and model governance, necessary for a unified AI approach. Why AI governance matters The
Whether working as a business analyst, data scientist or machine learning engineer, one thing remains the same – making an impact with data and AI is what really matters. Pre-processing and exploring data, building and deploying models and turning those scoring values into an actionable insight can be overwhelming. A
Across all industries and sectors, the volume and scope of data continues to increase exponentially. It shows no sign of slowing down or reducing. UK Defence, and the wider public sector, collect vast amounts of data. They have a desire to innovate and exploit that data through analytics, artificial intelligence,
Artificial intelligence (AI) is causing a digital transformation that is changing businesses’ operations. It is likely to bring a sea change compared to the Industrial Revolution. Many challenges with AI are technical, but most failures occur because of poor strategy and execution. Fortunately, there are some steps you can take
In this Q&A with MIT/SMR Connections, Iain Brown, SAS’s head of data science for the United Kingdom and Ireland, discusses technical readiness for AI, customer adoption trends, IT’s changing role, and mission-critical considerations for technology and talent. Q: What does it mean, from both a technology and a cultural standpoint,
Artificial intelligence (AI) is one of the most popular topics in the tech industry, along with IoT, cloud and blockchain, to mention just a few. Although it is a very promising technology, it is also connected with very high expectations, quite often beyond the capabilities that AI provides today. As
