Two years ago, in an article for Deloitte, I explored the potential of Large Language Models (LLMs) in planning & forecasting. It was the early days of Generative AI, and my approach was to be cautious amid considerable hype.

What did I get wrong, and what did I get right?

Looking back, I was too conservative. Today, I would say we are entering a new era for forecasting and planning.

A collection of interesting forecasting & planning tools

A collection of interesting forecasting & planning tools

My Original Prediction

My initial article predicted three ways LLMs would support better forecasting and planning:

  1. Forecasting Code Gen (Forecasting Copilots, LLMs assisting in writing code for forecasting.).
  2. Structuring Unstructured Data (e.g. Feature Engineering, using LLMs and embedding models to transform unstructured data into new features for forecasting models).
  3. Assisting the Planning Team (AI Assisted Planning with agents supporting planning teams by gathering, organizing, and summarizing both structured and unstructured data, and providing reports & insights.).

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Notably absent was the idea of LLMs directly producing forecasts. At the time, I aimed to dispel a common misconception that went something like this:

“Previously we’ve used “AI” for forecasting, now this new, better “AI” will make even better forecasts.” - a common misunderstanding in the early days of Generative AI

The reality has proven more nuanced. LLMs are now being used directly for forecasting through innovative approaches like those from Chronulus AI. Furthermore, LLMs have spurred the development of foundation models for forecasting—pretrained models built with a similar approach and architectures.

What I Got Right

Forecasting Copilots:

Two years ago, general models like ChatGPT and GitHub Copilot weren't great at writing forecasting code. I predicted the emergence of forecasting-specific copilots.

Since then, some of the issues I faced with general models have been mitigated by tools that make it easier to inject online documentation into your context.

Perhaps more interesting though, specialized tools for data science and forecasting have emerged. Traversaal and Julius are two interesting examples:

Screenshot from the Traversaal website

Screenshot from the Traversaal website

Screenshot from the Julias website

Screenshot from the Julias website