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In the ever-evolving landscape of finance, organizations are constantly on the lookout for powerful planning solutions that can streamline their processes, integrate data seamlessly, and provide actionable insights for effective decision-making. IBM Planning Analytics has emerged as a leading enterprise performance management platform, offering a comprehensive suite of tools and features to design and build advanced models.

In the realm of financial planning and analysis, the key to success often lies in the tools and technologies professionals leverage. IBM Planning Analytics stands out as one such powerful tool that can significantly transform the way finance professionals operate. This article delves into how mastering this platform can be a pivotal moment for any finance expert seeking to optimize planning and analysis processes.

In today's quickly evolving business landscape, organizations must continue to adopt innovative approaches just to stay competitive. One such approach which continues to gain significant attention is integrated planning which is the process of connecting and harmonizing operations planning and financial planning activities. By aligning these two critical processes, organizations can achieve a unified and coherent planning framework that delivers a myriad of benefits. In this article, we will explore the key benefits of integrated planning which lead to optimal business performance.

Finance leaders face the challenge of effectively integrating emerging technologies like AI and ML into their planning processes. With the abundance of hype surrounding these technologies, it's crucial to separate the noise from the strategies that truly deliver measurable value. In this blog, we will explore the top five cross-functional AI use cases that Finance should own and drive, focusing on how to begin leveraging AI to produce results. By incorporating these strategic initiatives into the planning process, Finance teams can gain valuable insights and drive outcomes that positively impact the organization's success.

Lately there’s been quite a bit of talk about how an organization could - or should - implement a data strategy. One insightful example is a recent post from IBM providing a “six-step approach”.

Consensus demand planning is a collaborative approach to demand forecasting that involves input from multiple stakeholders across an organization, and potentially outside the organization. This approach can help to improve forecast accuracy by taking into account a wider range of factors that influence demand.

This is the last (for now) in a series of blog posts about whether ChatGPT can help us with Planning Analytics (PA) / TM1, more specifically with Turbo Integrator (TI) script. In the first post in the series, I provided some background on AI technology like ChatGPT and really useful AI tools for programmers, like GitHub Copilot. In the second post in the series, I shared some ways in which ChatGPT is already able to help understand TI script, which could be useful in tasks like system documentation. One other finding in that second blog post is that although ChatGPT does a good job of accurately describing TI scripts, it is not yet capable of helping to suggest ways of optimizing or improving those scripts; it suggests things that are not possible in TI script (like CASE statements) and even makes up functions which don’t exist. As a reminder, all ChatGPT responses in this series are from ChatGPT-3.5, unless otherwise specified.

This is the second in a series of blog posts about whether ChatGPT can help us with Planning Analytics (PA) / TM1, and more specifically with Turbo Integrator (TI) script. In the first post in the series, I provided some background on AI technology like ChatGPT and really useful AI tools for programmers, like GitHub Copilot. In that post I also showed examples that highlight some of the strengths of ChatGPT, like its ability to understand TM1Py scripts, and some of its risks and weaknesses, like its hallucinations about PA’s history. All ChatGPT responses in this series are from ChatGPT-3.5, unless otherwise specified.

The machines are taking over, haven’t you heard? For all the legitimate concerns about Artificial Intelligence (AI) being used to generate deepfakes or write student essays, many of these AI tools can help us be more efficient and tackle repetitive or mundane tasks so that we can focus on far more interesting problems and things that we enjoy. This post focuses on newer natural language generative AI tools, specifically ChatGPT, which has been very much in the news.