AI Ethics & Explainability for IBM Analytics Practitioners

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.

Artificial Intelligence, Cognos Analytics, Planning AnalyticsCraig ColangeloJanuary 10, 2022Craig Colangelo, AIComment

Setting Up Planning Analytics AI Forecasting

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.

Planning AnalyticsDan SwanMay 9, 2021IBM Planning Analytics, Planning Analytics Workspace, Forecasting, AIComment

Embedding Predictive Analytics Into the Planning Process

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.

5 Ways Your AI Projects Fail, After Action Reviews and Post-Mortems

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.

Artificial IntelligenceChristopher S. PennMay 7, 2020AI, Christopher PennComment

5 Ways Your AI Projects Fail, Part 5

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.

Artificial IntelligenceChristopher S. PennFebruary 5, 2020Strategy, AI, Project Management, Christopher PennComment

5 Ways Your AI Projects Fail, Part 4

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.

Artificial IntelligenceChristopher S. PennJanuary 10, 2020Strategy, AI, Project Management, Christopher Penn

5 Ways Your AI Projects Fail, Part 3

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.

Artificial IntelligenceChristopher S. PennDecember 10, 2019Strategy, AI, Project Management, Christopher Penn

5 Ways Your AI Projects Fail, Part 2

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.

Artificial IntelligenceChristopher S. PennNovember 6, 2019Strategy, AI, Project Management, Christopher Penn

5 Ways Your AI Projects Fail, Part 1

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.

Artificial IntelligenceChristopher S. PennSeptember 30, 2019Strategy, AI, Project Management, Christopher Penn