This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Workforce planning has long been a cornerstone of business strategy, yet many enterprises still approach it with outdated methods. Traditionally, it has been rigid, reactive, subjective and often siloed. But as industries evolveespecially those with high turnover and a large frontline workforce, like retail, healthcare and hospitalitycompanies must rethink how they forecast talent needs.
Managing corporate income taxes is a challenge for chief financial officers and their tax department professionals. Tax codes are often complex, so tax accounting as well as the data required for tax provisions and tax compliance are different enough from statutory accounting to create significant workloads for the tax department. The provision for income tax expense and, for public companies, the assembly of information related to tax-related disclosures, can be a factor holding up the completi
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables , the first cloud object store with built-in Apache Iceberg support. With this integration, SageMaker Lakehouse provides unified access to S3 Tables, general purpose Amazon S3 buckets, Amazon Redshift data warehouses, and data sources such as Amazon DynamoDB or PostgreSQL.
Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables , the first cloud object store with built-in Apache Iceberg support. With this integration, SageMaker Lakehouse provides unified access to S3 Tables, general purpose Amazon S3 buckets, Amazon Redshift data warehouses, and data sources such as Amazon DynamoDB or PostgreSQL.
As far as many C-suite business and IT executives are concerned, their company data is in great shape, capable of fueling data-driven decision-making and delivering AI-powered solutions. But the closer an IT leader is to that data, the less confidence they have in its quality, according to a recent survey from IT consulting firm Softserve, which found that nearly half of C-level execs at large enterprises, including C-level IT leaders, believe their organizations data is fully mature, while just
Retrieval-Augmented Generation (RAG) systems enhance generative AI capabilities by integrating external document retrieval to produce contextually rich responses. With the release of GPT 4.1, characterized by exceptional instruction-following, coding excellence, long-context support (up to 1 million tokens), and notable affordability, building agentic RAG systems becomes more powerful, efficient, and accessible.
Gen AI has entered the enterprise in a big way since OpenAI first launched ChatGPT in 2022. According to Precedence Research, the global gen AI market was over $25 billion in 2024 and is forecast to reach a staggering $803 billion by 2033. And AI at Wharton, part of the Wharton AI and Analytics Initiative at the UPenns Wharton School, together with consultancy GBK Collective, also found in a study of senior decision-makers that enterprises with 1,000 or more employees invested on average more th
Historically, cloud migration usually meant moving on-premises workloads to a public cloud, like Amazon Web Services (AWS) or Microsoft Azure. And because so many businesses were keen to get out of the on-prem infrastructure management business by moving to public cloud, there were plenty of guides and tools to help with an on-prem to public cloud migration.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.
2021 (Exafunction, ) AI , (Cursor) . , LLM LLM . VS , , IDE , . AI , AI . AI , , , , AI . AI , AI . , . AI . , AI AI . AI 2024 67 , 2030 257 . 2024 2030 (CAGR) 25.2% . jihyun.lee@foundryco.
AI (AI Private Cloud) , , AI . , AI . , (Kyndryl Bridge) AI (AI Enterprise) NIM . AI GPU , IT . , AI AI , . , , . AI , , , AI . AI AI ML LLM . AI , , . AI , AI , AI . , AI . AI (AWS), , HPE, , . dl-ciokorea@foundryco.
Speaker: Claire Grosjean, Global Finance & Operations Executive
Finance teams are drowning in data—but is it actually helping them spend smarter? Without the right approach, excess spending, inefficiencies, and missed opportunities continue to drain profitability. While analytics offers powerful insights, financial intelligence requires more than just numbers—it takes the right blend of automation, strategy, and human expertise.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content