SAS' Mary Osborne, Ali Dixon Ricke, and Franklin Manchester break down what insurers still need to learn about generative AI.
The time is now for insurance companies to lead the way in addressing climate risk
With a new year around the corner, it’s time to start thinking about what tech trends are coming in the months ahead. 2025 will be interesting, because now that AI has been around long enough to go from novel to normal, we should stop marveling at what it can do
In 2012, Harvard Business Review declared the data scientist the sexiest job of the 21st century. Here’s what we knew at the time: big data was (and still is to this day) an enormous opportunity to make new discoveries. We were in the boom of user-generated content from social platforms,
At the very heart of the financial world lies a commodity so vast it’s almost immeasurable ... and it’s growing exponentially with potential yet unrealized. Big data – complex structured and unstructured datasets arriving from innumerable sources – is reshaping the global banking industry. Used effectively, big data can support the delivery
Many of us have fond memories of a Family Feud host saying, “And the survey says…!” as the game show’s answer board lit up, bringing joy or disappointment to the contestants. For some, the host who comes to mind is Richard Dawson or Ray Combs, while today’s fans likely think
As data decays, it becomes less useful. See how synthetic data for insurance can help.
Climate risks threaten insurers' profitability and financial stability. See how insurers can adapt.
En la actualidad, los modelos analíticos son herramientas esenciales para tomar decisiones basadas en datos. Desde prever tendencias hasta optimizar operaciones, los modelos analíticos dependen en gran medida de la calidad de los datos de entrada. La precisión, integridad y relevancia de estos datos son cruciales para obtener resultados confiables
The ongoing impact of inflation on the economy is a persistent news headline. Organizations around the world are exploring how data and AI can help lower costs and improve efficiency. Georgia-Pacific, one of the world’s largest manufacturers of pulp and paper products, is ahead of the curve. They are poised
The search to maximize our own productivity is never-ending. We all want to be more efficient in our work and carve out more time with loved ones. As you assess data and AI technology to automate processes and maximize efficiency, you may wonder if it’s truly possible for you or
Fairness, transparency, integrity and competition are essential for managing public funds. We rely on departments to choose the best value from the private sector. Efficient public procurement improves services, infrastructure, and the economy. It must also be accountable to the public by protecting financial loss from fraud, waste, abuse, and
It is no surprise that generative AI (GenAI) is quickly becoming a potent tool in the health and life sciences industry. There are numerous ways that AI and GenAI can help beat the rising tide of fraud. We can count on these new technologies to enable the ability to commit
Headlines about government spending often highlight eye-catching, big-ticket items like transportation infrastructure projects or military equipment. However, smaller expenses also accumulate – significantly and often undetected. In fiscal year 2024, the US federal government spent $6.75 trillion on various goods, services and programs, and the UK central government spent £35.2
Fearmongers would have you believe that AI will replace us, but I truly believe there has never been a better time in history to be a developer. With the advance of artificial intelligence into generative AI (GenAI) and enhanced computing power, we stand on the brink of a new era
Data scarcity, privacy and bias are just a few reasons why synthetic data is becoming increasingly important. In this Q&A, Brett Wujek, Senior Manager of Product Strategy at SAS, explains why synthetic data will redefine data management and speed up the production of AI and machine learning models while cutting
수많은 산업에서 중추적인 역할을 맡게 된 클라우드 컴퓨팅은 조직이 분석과 머신 러닝 및 AI의 힘을 활용하여 인사이트를 얻고 혁신을 추구할 수 있도록 도와줍니다. *이 글은 Spiros Potamitis 가 작성한 내용을 SAS코리아에서 번역한 것입니다. 그러나 클라우드 컴퓨팅의 급속한 확대로 인해 클라우드의 탄소 발자국 역시 크게 증가했습니다. 알기 쉽게 비교하자면, 클라우드 컴퓨팅의
Synthetic data generation, as its name suggests, is one component of generative AI. With this technology, marketers can generate artificial data sets that share the attributes and characteristics of real customer data. As marketers continue to expand their use of both traditional AI and GenAI, synthetic data generation reduces the
It’s no surprise that MarTech in 2024 seemed to be dominated by GenAI, GenAI and … more GenAI. Perhaps this is a bit of an exaggeration, but you can’t deny this topic was a headliner in the MarTech space, along with AI and customer data platforms. Here are my top
Generative AI (GenAI) is here to stay – there’s no question about it. A recent SAS survey of 1,600 organizations found that 54% have begun implementing It, and 86% plan to invest in it within the next financial year. As organizations integrate AI into their workflows, a critical question arises:
"Generative AI (GenAI) initiatives should support broader public goals and needs," says SAS' Ensley Tan. While governments recognize GenAI's potential to improve operational efficiency and citizen experience, there is more to it than setting up projects and expecting them to work. Tan, SAS Asia-Pacific Lead for Public Sector Consulting, said public
Dans le paysage technologique actuel, les données synthétiques, nouveau sous ensemble de l’IA générative, apportent de nouvelles pistes de réflexion pour la création des modèles d'intelligence artificielle. Contrairement aux données traditionnelles, pouvant être limitées par des contraintes de biais, de quantité, ou encore des contraintes de confidentialité et de conformité,
Remember the first time you held a smartphone in your hand? It wasn’t just a fancy phone – it was the beginning of a revolution. Before smartphones, we used separate calls, cameras, emails, and navigation devices. But almost overnight, this gadget transformed everything: how we communicate, consume media, work and
As businesses in the UK and Ireland rapidly adopt generative AI, strategic insights from the latest SAS study reveal the roadmap to successful integration and the hurdles to overcome. GenAI is rapidly transforming how businesses operate, innovate, and interact with customers and employees alike. However, as the technology proliferates, so
Data science continues to be a pivotal force driving innovation across industries. From enhancing customer experiences to optimizing operational efficiencies, the role of data science is expanding, bringing with it new challenges and opportunities. This article explores the emerging trends and technologies that are shaping the future of data science
A woman arrives at the emergency room with chest pain. She immediately receives an x-ray. While the radiologist looks at the image, her AI assistant flags anomalies in the patient’s lungs – invisible without the technology. The chest pain turns out to be benign, but sophisticated imaging reveals early-stage lung
Everyone is talking about artificial intelligence (AI) for a good reason. It’s already revolutionizing the world as we know it. And governments are well-poised to use AI to improve services and operations. But we can expect the AI conversation to shift into 2025. Attend any conference of government leaders, and
Another promise of innovation and advancement? Not in 2025. The hype of market disruption and transformation might be the content that catches attention, but it’s not always the most accurate description of the industry. For far too long, industry experts have praised the ideas of future-forward growth and promised year-over-year
We know that building trust in technology is a big deal. It’s no longer enough for AI to just work – we need to understand how it works, what it is doing and whether it’s performing as expected. That’s where model cards come in. If you remember from our previous
Want to deploy digital twins as part of your predictive maintenance strategy? You should, for countless reasons that have been identified elsewhere. But like anything else worth doing in business, it’s a process – you need to know where your organization lands on the maturity curve, and where it’s going.
