Decision Intelligence with AI

[Work In Progress - Estimated Beta Launch: August 2026]

Contents:
💡 About
👣 Getting Started: Choose Your Path
📖 Printed Book
🧱 Workshop Modules
✅ Requirements for Interactive Decision Notebooks

About

Navigating decisions effectively is important, serving as a key differentiator for successful individuals. Therefore, decision-making ability is regarded by executives as one of the most highly desired business skills! However, effective and quality decision-making is not simple. Perhaps the intersection of Decision Theory and Generative AI can help.

This Decision Intelligence with AI workshop is an interactive course that is paired with an upcoming book called "Decision Intelligence with AI". This course was designed with a focus on three key areas:

• Illustration of important Decision-Making concepts
• Introduction of the Decision Intelligence Framework that applies systematic decision-making
• Application of Decision Intelligence theory with Generative AI
• Interactive Notebooks for AI engineers/software engineers to execute the examples with an orchestration framework (Semantic Kernel & OpenAI)

The unique workshop structure with diverse focus areas, allows this workshop to be consumed in several ways. Depending on your persona, familiarity with Decision Theory or AI acumen, you have several paths to get started.

Getting Started: Choose Your Path

1. 👓 Reader Persona - Read-Only (Non-Technical) - Ideal for non-technical users not familiar with Decision Intelligence nor Generative AI

• Nothing to install! No special requirements to read any of the workshop content. All of the text, links & images are available by just navigating to the Notebooks folder and reading the section modules sequentially. Notebooks folder: https://github.com/bartczernicki/decision-intelligence-with-ai/tree/main/Notebooks
• All Generative AI output is pre-run and available to read in a browser
• Recommend further detailed book resources provided in the workshop to immerse yourself in Decision Theory, Decision-Making, Cognitive Theory etc.
• Recommend companion book "Decision Intelligence with AI" (coming late 2025) for a more complete reading experience and advanced content not in the workshop

2. 💻 Technical Persona - Interactive (Technical) - Ideal for technical users that might not be familiar with Decision Intelligence nor Generative AI, but are familiar with Jupyter Notebooks interactivity. The persona can change basic connection strings and make simple code changes.

• All the considerations from the "Read Persona" Getting Started applies
• Set up the Requirements for Interactive Decision Notebooks to add custom interactivity
• Run the workshop Notebook modules sequentially (no coding required!), as the Decision Intelligence & AI concepts "build on" each other
• C#/.NET Code will be executed but simple modifications to decision prompts, agents, configuration files are clear to added decision exploration

3. 🧑‍💻 AI Engineer - Interactive (Advanced Technical) - Ideal for a technical user that is familiar with programming and Generative AI

• Most of the considerations from the "Technical Persona" Getting Started applies
• Set up the Requirements for Interactive Decision Notebooks to add custom interactivity
• Notebook modules do not "build on each other" from a programming dependency; only from a Decision Intelligence concept dependency. Therefore, you can jump in and modify where needed
• Recommended to "Fork and Hack" and gravitate to the Decision Scenarios with most impact (change AI models, try different APIs or knowledge stores, advanced AI programming techniques, port code to your projects etc.). Advanced users are encouraged to fork this repository and try out different models or techniques.

Printed Book

What is the difference between the workshop the printed companion book? The Printed Book has the following features:

• Deeper focus on Decision Intelligence theory with more advanced application of Generative AI rather than a code-first focus.
• Additional advanced real-world examples with Artificial Intelligence.
• Chapters are organized using the Decision Intelligence Framework. A great deal less code illustrated in the book, making it easier to consume for non-technical readers.
• References in text content to further deepen decision-making knowledge.
• Extra Curated Chapters with "Table of Contents Lists" for Easy Lookup of: Decision Intelligence Quotes, Decision Rules, Decision Intelligence Resources.

Workshop Modules

The Table of Contents below illustrates the structure of the Decision Intelligence with AI Workshop. The workshop is structured on concepts broken down into Notebook modules (chapters). Each Notebook module consists of Decision Intelligence concepts and/or interactive Generative AI features that can be dynamically executed.

⚠️ This workshop is currently being developed in conjunction with the companion book. During this time, there will be frequent changes. Content that is available (even if partially) is noted below. Content that does not have the Availability checkmark is in early stages or not available yet. Please check back for updates!

Module (Chapter)	Decision Intelligence	Available	Link
1a - Decision Intelligence - Introducing the Decision Intelligence Framework	Framework Introduction Enhanced with Generative AI Scenario: Application with Eisenhower Decision Priorotization	✅	Link
1b - Decision Intelligence - Decision Framing	Decision Framing Introduction Reframing Alternative Decision Options Systematic Frameworks	✅	Link
1c - Decision Intelligence - Gathering Intelligence	Gathering Intelligence Introduction Historical Example: Battle of Edington Intelligence with AI	✅	Link
1d - Decision Intelligence - Decision Execution	Decision Execution Introduction Three Forms of Execution Can AI Replace a CEO?	✅	Link
1e - Decision Intelligence - Decision Execution with Intuition	Intuition Execution Introduction Scenario: Split-Second Decision Making Intuitive Generative AI	✅	Link
1f - Decision Intelligence - Decision Execution with Decision Rules	Rules Execution Introduction Sample list of Generic Decision Rules Scenario: Exploration vs Exploitation Scenario: Optimize Sales Performance with Price's Law Sample list of Domain Specific (Industry) Decision Rules Customized Rules Frameworks Rules Powered by Generative AI	✅	Link
1g - Decision Intelligence - Decision Execution with Quantitative Methods	Quantitative Execution Introduction Math is Hard for Humans Data Analysis Impossible for Humans Arriving at Quantitative Conclusions using Simulations Introducing the Monte Carlo Scenario: Monte Carlo for Total Cost of Car Ownership	✅	Link
1h - Decision Intelligence - Decision Communication	Decision Communication Frameworks How Generative AI transformed Decision Communication	✅	Link
1i - Decision Intelligence - Applying the Decision Intelligence Framework	End-to-end application of the Decision Intelligence Framework	✅	Link
1j - Decision Intelligence - Enterprise Decision Intelligence	Decision Intelligence in the Enterprise Decision Support Systems Decision Management Systems	✅	Link
2a - Workshop (Setup) - Code Execution Requirements		✅	Link
2b - Workshop (Setup) - About Microsoft AI Extensions		✅	Link
2c - Workshop (Setup) - Microsoft Agent Framework		✅	Link
3a - Workshop (AI Extensions) - Simple Decision Prompts	Understanding Decision-Making Frameworks Built-Into AI Create Custom Decision-Making AI Personas Scenario: Deciding between Comunity College or a University	✅	Link
3b - Workshop (AI Extensions) - Decisions with Chat Completions & Responses APIs	Decision conversations with Chat Completions and Responses APIs Multi-turn decision context and model interactions	✅	Link
3c - Workshop (AI Extensions) - Decisions with Reusable Prompts and Native .NET Functions	Reusable decision prompts Native .NET functions for decision workflows	✅	Link
3d - Workshop (AI Extensions) - Private AI Decision Intelligence	Local and private AI decision workflows Scenario: Decision Intelligence with open-source AI models	✅	Link
4a - Decision Framing - Six Thinking Hats	Decision framing with Six Thinking Hats Multiple perspectives for better decision quality	✅	Link
5b - Gathering Intelligence - Collective Intelligence	Collective intelligence for decision-making Aggregating diverse judgments and perspectives	✅	Link
10a - Appendix - Book Resources	Curated Decision Intelligence book resources	✅	Link
10b - Appendix - Decision Quotes	Decision Intelligence quote collection	✅	Link
10c - Appendix - Glossary	Glossary of workshop terms	✅	Link

⚠️ The modules below are being updated from Semantic Kernel to Microsoft Extensions for AI and Microsoft Agent Framework.

Module (Chapter)	Decision Intelligence	Available	Link
4d - Semantic Kernel - Gathering Intelligence with Generative AI	Gathering Intelligence with Generative AI	🛠️	Link
4x - Workshop (AI Extensions) - Premortem (What-If) Decision Analysis	Premortem decision analysis What-if reasoning for decision quality	🛠️	Link
5a - Semantic Kernel - Scale Decision Processes with Plugins	Gathering Intelligence for AI decisions with external data stores	🛠️	Link
5b - Semantic Kernel - Plugins for Decision Communication	Decision Communication using the Minto Pyramid Framework Minimizing judgement during communication	🛠️	Link
5c - Semantic Kernel - Custom Plugins for Decision Recommendation	Scenario: Decision support using internet-gathered intelligence	🛠️	Link
5d - Semantic Kernel - Diverse Plugins for Decision Making	Decision execution using various methods Combining decision recommendations using analytics and machine learning	🛠️	Link
6a - OpenAI - Improving Decisions with OpenAI LogProbs		🛠️	Link
6b - OpenAI - Measuring GenAI Probabilistic Accuracy with LogProbs		🛠️	Link
6c - OpenAI - Validating Multiple Decisions with Aggregated Brier Scores		🛠️	Link
7a - Semantic Kernel - Decisions with AI Agent Personas	Creating AI decision agent personas Optimizing AI decision agents	🛠️	Link
7b - Semantic Kernel - Decisions with Multi-AI Agent Personas	Decisions with multi-agent personas Advanced multi-agent decision orchestration	🛠️	Link

Requirements for Interactive Decision Notebooks

1. Visual Studio Code running on your local workstation or VS Code with GitHub CodeSpaces
   • Install VS Code: Download Visual Studio Code
2. Install .NET 10.x SDK:
   • Install .NET 10 SDK: https://dotnet.microsoft.com/en-us/download/dotnet/10.0
3. Install C# & .NET Visual Studio Code Extensions
   • C#: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp
   • C# Dev Kit: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csdevkit
   • Juypyter Extension for VSCode: https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter
4. Install Jupyter (JupyterLab)
   • Option 1 - Insructions: https://jupyter.org/install
   • Option 2 - Anaconda Distribution: https://www.anaconda.com/distribution
5. Configure Jupyter Extension to use .NET Kernel

• Open a Terminal and run the following Jupyter command, by default you will only see the Python kernel listed

> jupyter kernelspec list
  python3            /opt/homebrew/Cellar/jupyterlab/4.5.4/libexec/lib/python3.14/site-packages/ipykernel/resources

• Install .NET Interactive using the following command

> dotnet tool install --global Microsoft.dotnet-interactive

• Install .NET Kernel for Jupyter using the following command

> dotnet interactive jupyter install
Installing using jupyter kernelspec module.
Installed ".NET (PowerShell)" kernel.
Installing using jupyter kernelspec module.
Installed ".NET (C#)" kernel.
Installing using jupyter kernelspec module.
Installed ".NET (F#)" kernel.

• Verify the installation worked, you should see 3 new .NET kernels added

> jupyter kernelspec list
  python3            /opt/homebrew/Cellar/jupyterlab/4.5.4/libexec/lib/python3.14/site-packages/ipykernel/resources
  .net-csharp        /Users/bart/Library/Jupyter/kernels/.net-csharp
  .net-fsharp        /Users/bart/Library/Jupyter/kernels/.net-fsharp
  .net-powershell    /Users/bart/Library/Jupyter/kernels/.net-powershell

6. Clone or fork this GitHub Repository
7. Have access to Azure OpenAI or OpenAI (Endpoint, GPT-5.x-mini model recommended)
8. When running the notebooks, ensure .NET (C#) is the selected kernel for your Notebooks. The Notebooks are saved by default to use the .NET kernel.