There are three kinds of actions that your builder can create: skill-based actions, custom-built actions, and AI-guided actions. In this article, we will explain what each type of action does and how your builders can make them.
Read More
Anthropic just launched their newest model within its Claude family – Claude 3.5 Sonnet. This model builds on the capabilities of Claude 3 which was released earlier this year. The new model is available to Claude users right now, both on the web and within the iOS app.
Read More
In today's data-driven world, businesses gather vast amounts of information from various sources like customer interactions, market research, and internal operations. However, this data often gets stored in separate systems or departments, also known as systems of record, creating "data silos."
Read More
With the inevitable, ever-tighter AI integration and infusion with IBM’s products, analytics practitioners need to understand the AI pillars and explainability principles that IBM espouses. These principles should be understood by CA and PA developers, administrators, and product owners, as they will inform the future direction of the AI components of each core product. Most importantly, it will be necessary for doers in our space to use the explainability toolkit in order to explain why models (and integrated, downstream components) predict/project/suggest/decide what they do to our business partners.
Read More
In a prior issue, my colleague Brian Moore introduced us to the Forecasting feature now available in Planning Analytics Workspace (PAW). Today we take an in-depth look at the steps to set up a Preview forecast and to apply a forecast to your model.
Read More
Applying AI to your planning process doesn’t mean that HAL 9000 is in the back office churning out next month’s sales numbers while analysts eagerly scribble down the results. In the current sense, predictive planning typically refers to the combination of time series forecasting algorithms and seasonality patterns from historical results to project future outcomes with greater accuracy.
Read More
The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies at the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read More
The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies at the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read More
The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies at the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read More
Data requirements are the technical specifications of what data you will need for your project, who is responsible for that data, where and how it is stored and used, and what measures you’ve taken to protect and optimize it for usage.
Read More
The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies in the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read More
The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies in the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read More