Python Day 1: AI Readiness Assessment

Build an AI Readiness Assessment Tool with Python in Google Colab and save it to GitHub.

Introduction

30 Second Summary

Organizations everywhere are trying to figure out if they are ready for AI. The first step in any AI strategy is assessing where things stand today.

In this project, you will build an AI Readiness Assessment Tool using Python in Google Colab. You will learn the three most fundamental Python skills (variables, print, input) while creating a professional tool that collects organizational data and generates a formatted executive summary.

What You'll Build

A polished Google Colab notebook containing an interactive AI Readiness Assessment Tool that uses Python fundamentals to collect and format organizational data into an executive summary report.

By the end of this project, you'll have:

  • A working Google Colab notebook with your first Python programs saved to your Google Drive.
  • An AI Readiness Assessment tool that collects organizational data and generates a formatted summary report.
  • A portfolio-ready notebook with markdown explanations, saved to GitHub and downloaded as a .py Python script.
  • Secret Mission: Add a scoring system that categorizes organizations into AI Maturity Stages based on their answers.

Are there any prerequisites?

You just need a Google account to access Google Colab. Everything runs in your browser with no software to install.

Before We Start

Before We Start...

Set Up Your Google Colab Notebook

Google Colab is your coding workspace for this entire 15-day series. It runs Python directly in your browser, so there is nothing to install on your computer.

In this step, you will create your first notebook and brand it as a professional consulting tool. Every notebook you build in this series doubles as a portfolio piece for your AI Socio-Technical Strategist brand.

In this step, get ready to:
  • Create a new Google Colab notebook and rename it.
  • Add a branded introduction with your name and tool description.
  • Confirm your workspace is ready for writing Python code.
Create and rename your notebook
  • Open your browser and go to:
  • colab.research.google.com
  • Sign in with your Google account if prompted.
  • Click File in the top menu bar.
  • Click New notebook.

A new notebook opens with a default name at the top left corner.

  • Click the notebook title at the top left (it says Untitled0.ipynb or similar).
  • Replace it by typing Day_1_AI_Readiness_Assessment_Tool.ipynb and press Enter.

What is Google Colab?

Google Colab is a free, browser-based coding environment built on Jupyter notebooks. It lets you write and run Python code without installing anything on your computer.

Your notebooks are automatically saved to Google Drive, so you can access them from any device and share them like a Google Doc.

🙋‍♀️ Don't see File in the menu?

Make sure you are on colab.research.google.com and not the Google Drive homepage. If you see a welcome dialog, close it first and the menu bar will appear at the top.

help me find the File menu in Google Colab

Add your branded introduction

Every professional notebook starts with context. You will add a Markdown text cell that tells readers who made this tool and what it does.

  • Click the +Text button in the toolbar to add a text cell above the code cell.
  • Type the following markdown into the text cell:
# AI Readiness Assessment Tool

**Ms. LJ Hall, MAOL**

*A Python-powered tool for evaluating organizational AI readiness*

Date: [[TODAYS_DATE="today's date, e.g. June 24, 2026"]]

What does this markdown do?

The # symbol creates a large heading. Double asterisks ** make text bold, and single asterisks * make text italic.

When you click outside the text cell, Colab renders the markdown into formatted text. This is how you add professional documentation between your code cells.

  • Click anywhere outside the text cell (or click the empty code cell below it).

Your text cell now renders as a formatted heading with your name in bold and the subtitle in italics. You should see one empty code cell sitting below it.

Still seeing raw markdown symbols?

If you still see the # and ** symbols instead of formatted text, click outside the text cell to deselect it. Colab only renders markdown when the cell is not in edit mode.

help me get my markdown to render in Google Colab

✔️ Awesome, I've got everything!

Great. Make sure you have saved your notebook by pressing Ctrl+S (Windows) or Cmd+S (macOS).

ⓧ I'd like to double check the full code

Your notebook should have one text cell with this content:

# AI Readiness Assessment Tool

**Ms. LJ Hall, MAOL**

*A Python-powered tool for evaluating organizational AI readiness*

Date: [[TODAYS_DATE="today's date, e.g. June 24, 2026"]]

Below the text cell, you should have one empty code cell. That is all you need for this step.

Your workspace is ready. Next up, you will write your first lines of Python code and see them run instantly in the browser.

Write Your First Python Code

Your notebook is set up and ready. Now it is time to make Python do something visible.

In this step, you will write code that displays text on screen, store information in variables, and format professional output using f-strings.

In this step, get ready to:
  • Write and run your first Python code using print().
  • Create variables to store tool information.
  • Use f-strings to format a professional tool header.
Write a welcome message with print()

The print() function displays text on screen. Anything you put between its parentheses appears as output when you run the cell.

  • Click into the empty code cell below your text cell.
  • Type the following code into the cell:
print("Welcome to the AI Readiness Assessment Tool")
  • Press Shift+Enter to run the cell.

You should see "Welcome to the AI Readiness Assessment Tool" printed directly below the cell.

What does print() do?

print() takes whatever you put between its parentheses and displays it as output. Text must be wrapped in quotes (either single ' or double ").

In Google Colab, output appears directly below the code cell after you run it.

🙋‍♀️ Don't see any output?

  • Make sure you pressed Shift+Enter, not just Enter. In Colab, Enter adds a new line inside the cell. Shift+Enter runs the code.
  • Check that your code has matching quotes on both sides of the text.

Why isn't my Colab cell producing output?

Store tool information in variables

Variables let you store information and reuse it throughout your program. Instead of typing the same text over and over, you save it once in a named container.

  • Click back into the same code cell (the one you just ran).
  • Replace all the code in the cell with the full version below:
# AI Readiness Assessment Tool
print("Welcome to the AI Readiness Assessment Tool")
print()

# Variables - storing tool information
tool_name = "AI Readiness Assessment"
version = "1.0"
author = "Ms. LJ Hall, MAOL"
purpose = "Evaluate organizational readiness for AI adoption"

# Printing variables
print(tool_name)
print(version)
print(author)
print(purpose)
  • Press Shift+Enter to run the updated cell.

What does this code do?

  • print() with empty parentheses prints a blank line, creating visual spacing in your output.
  • Each variable (like tool_name = "AI Readiness Assessment") stores a piece of text. The name on the left is the label. The value on the right is what gets stored.
  • When you pass a variable name to print(), Python displays the value stored inside it, not the variable name itself.

You should see "Welcome to the AI Readiness Assessment Tool" followed by a blank line, then each variable's value printed on its own line: AI Readiness Assessment, 1.0, Ms. LJ Hall, MAOL, and the purpose statement.

Only seeing the welcome message?

  • Make sure you replaced ALL the code in the cell, not just added to the end. The full cell should start with # AI Readiness Assessment Tool.
  • Check for typos in variable names. Python is case-sensitive, so Tool_name is different from tool_name.

Help me fix my variables in Google Colab.

Format a professional header with f-strings

F-strings let you embed variables directly inside text. Instead of printing each variable on its own line, you can combine them into formatted sentences and layouts.

  • Click the +Code button to add a second code cell below your first one.
  • Type the following code into the new cell:
# f-strings - combining variables into formatted text
tool_name = "AI Readiness Assessment"
version = "1.0"
author = "Ms. LJ Hall, MAOL"
purpose = "Evaluate organizational readiness for AI adoption"

header = f"Tool: {tool_name} v{version} | Created by: {author}"
print(header)

description = f"Purpose: {purpose}"
print(description)

print(f"{'=' * 50}")
print(f"  {tool_name}")
print(f"  Version {version}")
print(f"  Developed by {author}")
print(f"{'=' * 50}")
  • Press Shift+Enter to run the cell.

What does this code do?

  • The f before the opening quote makes it an f-string. Anything inside {} curly braces gets replaced with the variable's value.
  • header = f"Tool: {tool_name} v{version} | Created by: {author}" combines three variables into one formatted line.
  • '=' * 50 repeats the = character 50 times, creating a visual divider line. This is string multiplication.
  • The variables are redefined at the top of this cell so it can run independently of Cell 1. Each Colab cell should work on its own.

You should see a formatted header line, a purpose line, then a boxed display with your tool name, version, and author surrounded by = dividers.

Seeing a syntax error?

  • Check that every f-string starts with f" (the letter f immediately before the opening quote, no space).
  • Make sure every opening { has a matching closing }. A missing brace causes a syntax error.
  • Verify that variable names inside the curly braces match the definitions exactly (e.g., tool_name not toolname).

Help me fix my f-string syntax error in Colab.

✔️ Awesome, I've got everything!

Great. Make sure both code cells show output below them before moving on.

ⓧ I'd like to double check the full code

Here is what each code cell should contain:

Code Cell 1:

# AI Readiness Assessment Tool
print("Welcome to the AI Readiness Assessment Tool")
print()

# Variables - storing tool information
tool_name = "AI Readiness Assessment"
version = "1.0"
author = "Ms. LJ Hall, MAOL"
purpose = "Evaluate organizational readiness for AI adoption"

# Printing variables
print(tool_name)
print(version)
print(author)
print(purpose)

Code Cell 2:

# f-strings - combining variables into formatted text
tool_name = "AI Readiness Assessment"
version = "1.0"
author = "Ms. LJ Hall, MAOL"
purpose = "Evaluate organizational readiness for AI adoption"

header = f"Tool: {tool_name} v{version} | Created by: {author}"
print(header)

description = f"Purpose: {purpose}"
print(description)

print(f"{'=' * 50}")
print(f"  {tool_name}")
print(f"  Version {version}")
print(f"  Developed by {author}")
print(f"{'=' * 50}")

Your Python code is alive and producing formatted output. Next up, you will build the interactive assessment that collects real organizational data using input().

Build the AI Readiness Assessment

In Step 2, you created variables and used f-strings to format them into a polished tool header. Now it is time to make your tool interactive.

A real assessment tool needs to collect information from the person using it. Python's input() function lets you ask questions and store the answers in variables. You will use this to build a tool that collects organizational data and generates a professional executive summary.

In this step, get ready to:
  • Collect organizational data using input() prompts.
  • Generate a formatted executive summary report using f-strings.
  • Document your code with a markdown explanation cell.
Add a description cell and collect assessment data

Before writing the assessment code, you will add a text cell that explains what this section of the notebook does. This keeps your notebook readable like a consulting document.

  • Click the +Text button to add a new text cell below your last code cell.
  • Type the following into the text cell:
This tool collects information about an organization to assess their AI readiness.
  • Click the +Code button to add a new code cell below the text cell.
  • Paste the following code into the new code cell:
# Collect organizational information
print("=" * 50)
print("  ORGANIZATIONAL AI READINESS ASSESSMENT")
print("=" * 50)
print()
print("Please answer the following questions about your organization.")
print()

org_name = input("Organization name: ")
industry = input("Industry (e.g., Healthcare, Finance, Education): ")
employees = input("Number of employees: ")
challenge = input("Biggest operational challenge right now: ")
uses_ai = input("Does your organization currently use any AI tools? (yes/no): ")

What does this code do?

The print() lines at the top create a professional banner using string multiplication ("=" * 50 repeats the equals sign 50 times).

Each input() line displays a prompt and waits for the user to type a response. The response gets stored in a variable (like org_name or industry) so you can use it later in the report.

  • Run the cell by pressing Shift+Enter.

How does input() work in Google Colab?

When you run a cell with input(), a text field appears below the cell in the output area. Type your answer and press Enter to move to the next question.

Colab will show each prompt one at a time. The cell keeps running until all five questions are answered.

  • Answer each question as it appears. You can use real or example data (e.g., "Acme Corp", "Healthcare", "250").

After answering all five questions, you should see your banner text followed by the five prompts with your typed answers.

Not seeing the input field?

Make sure you clicked inside the code cell and pressed Shift+Enter to run it. The text field only appears in the output area after the cell starts executing.

If the cell seems stuck, check that your browser tab is in focus. Colab needs the tab active to display input fields.

Help me troubleshoot why my input() cell is not showing a text field in Google Colab.

Generate the executive summary report

Now that your variables hold real organizational data, you can format everything into a professional report. This is where f-strings and triple-quoted strings combine to create multi-line formatted output.

  • Click the +Code button to add a new code cell below the previous one.
  • Paste the following code into the new code cell:
# Build the executive summary report
report = f"""
{'=' * 50}
  AI READINESS ASSESSMENT - EXECUTIVE SUMMARY
{'=' * 50}

ORGANIZATION PROFILE
--------------------
Organization: {org_name}
Industry:     {industry}
Size:         {employees} employees
AI Status:    {"Currently using AI tools" if uses_ai == "yes" else "Not yet using AI tools"}

INITIAL ASSESSMENT NOTES
------------------------
Primary Challenge: {challenge}

This organization operates in the {industry} sector
with {employees} team members. Their primary operational
challenge is: {challenge}.

{"They have begun their AI journey and are already leveraging AI tools." if uses_ai == "yes" else "They have not yet adopted AI tools, presenting a greenfield opportunity for strategic AI implementation."}

NEXT STEPS
----------
- Schedule a detailed AI capability assessment
- Identify quick-win AI use cases for {industry}
- Develop an AI roadmap aligned with solving: {challenge}

{'=' * 50}
Report generated by: {tool_name} v{version}
Strategist: {author}
{'=' * 50}
"""

print(report)

What does this code do?

The triple quotes (f"""...""") create a multi-line f-string. Everything between the triple quotes becomes one big formatted string stored in the report variable.

Inside the curly braces, Python inserts the value of each variable. The if/else expressions (like "Currently using AI tools" if uses_ai == "yes" else "Not yet using AI tools") choose different text based on what the user answered.

The report also pulls in tool_name, version, and author from Code Cell 2. Variables persist across cells in Google Colab as long as you ran the earlier cells first.

  • Run the cell by pressing Shift+Enter.

You should see a cleanly formatted executive summary with your organization's data filled in, section headers, and a "Next Steps" recommendation list.

Seeing a NameError?

If you see something like NameError: name 'org_name' is not defined, it means Code Cell 3 (the input cell) was not run first. Go back and run Code Cell 3, answer all five questions, then run this cell again.

If the error mentions tool_name or author, run Code Cell 2 first. Colab needs all previous cells run in order so variables are available.

Help me fix a NameError in my Google Colab report cell.

✔️ Awesome, I've got everything!

Great. Make sure your report printed correctly with all your data filled in before moving on.

ⓧ I'd like to double check the full code

Here are both code cells added in this step. Compare them with what you have in your notebook.

Code Cell 3:

# Collect organizational information
print("=" * 50)
print("  ORGANIZATIONAL AI READINESS ASSESSMENT")
print("=" * 50)
print()
print("Please answer the following questions about your organization.")
print()

org_name = input("Organization name: ")
industry = input("Industry (e.g., Healthcare, Finance, Education): ")
employees = input("Number of employees: ")
challenge = input("Biggest operational challenge right now: ")
uses_ai = input("Does your organization currently use any AI tools? (yes/no): ")

Code Cell 4:

# Build the executive summary report
report = f"""
{'=' * 50}
  AI READINESS ASSESSMENT - EXECUTIVE SUMMARY
{'=' * 50}

ORGANIZATION PROFILE
--------------------
Organization: {org_name}
Industry:     {industry}
Size:         {employees} employees
AI Status:    {"Currently using AI tools" if uses_ai == "yes" else "Not yet using AI tools"}

INITIAL ASSESSMENT NOTES
------------------------
Primary Challenge: {challenge}

This organization operates in the {industry} sector
with {employees} team members. Their primary operational
challenge is: {challenge}.

{"They have begun their AI journey and are already leveraging AI tools." if uses_ai == "yes" else "They have not yet adopted AI tools, presenting a greenfield opportunity for strategic AI implementation."}

NEXT STEPS
----------
- Schedule a detailed AI capability assessment
- Identify quick-win AI use cases for {industry}
- Develop an AI roadmap aligned with solving: {challenge}

{'=' * 50}
Report generated by: {tool_name} v{version}
Strategist: {author}
{'=' * 50}
"""

print(report)
Document your code with a markdown explanation

A portfolio-ready notebook explains what the code does, not just shows it. Adding a markdown cell here helps future readers (and future you) understand the Python concepts at a glance.

  • Click the +Text button to add a new text cell below Code Cell 4.
  • Type the following markdown into the text cell:
## How This Code Works

- **Variables** (`org_name`, `industry`, etc.) store the data collected from the user.
- **`input()`** displays a prompt and waits for the user to type a response. Everything entered is stored as text (a string).
- **f-strings** (`f"..."`) let us insert variable values directly into formatted text using curly braces `{}`.

Why add markdown cells?

Markdown cells turn your notebook from a raw code file into a readable document. When someone views your shared notebook, these cells explain your thinking and approach.

This is what separates a consulting deliverable from a coding exercise.

After clicking outside the text cell, your markdown renders as formatted text with a heading, bold terms, and inline code styling.

Your assessment tool now collects real data and produces a professional report. Next up, you will polish the entire notebook into a portfolio piece with clear section headings and a shareable link.

Polish Your Notebook for Your Portfolio

Your assessment tool collects organizational data and generates a formatted executive summary. The Python code works.

Now it's time to make the notebook itself look professional. A well-organized notebook with clear headings and markdown explanations reads like a consulting deliverable, not a coding exercise. This is what sets your portfolio apart.

In this step, get ready to:
  • Add a portfolio header cell at the top of your notebook.
  • Add a Key Takeaways section at the bottom.
  • Save and share your notebook with a public link.
Add a portfolio header at the top of your notebook

Anyone who opens your notebook should immediately understand what it is, what Python skills it demonstrates, and why it matters. A header cell at the very top sets that context.

  • Click on your very first cell in the notebook (the markdown title cell with your name).
  • Click the +Text button in the toolbar to add a new text cell above it.
  • Drag the new text cell to the very top of the notebook if it did not appear there already.
  • Type the following markdown into the new cell:
# Day 1: AI Readiness Assessment Tool

**Python Concepts Covered:**
- Variables
- print()
- input()
- f-strings

This tool demonstrates how Python fundamentals can be applied to AI transformation consulting. It collects organizational data and generates an executive summary, positioning the strategist as someone who builds practical tools for evaluating AI readiness.

What does this markdown do?

The # creates a large heading. The **text** syntax makes text bold. The - at the start of a line creates a bullet point.

When you click outside the text cell (or press Shift+Enter), Colab renders the markdown into formatted text.

  • Press Shift+Enter to render the markdown cell.
  • You should see a large heading, a bold label with four bullet points underneath, and a paragraph of explanation.

Cell still showing raw markdown?

Make sure you are in a text cell, not a code cell. Text cells have a different background and show a formatting toolbar. If you accidentally used a code cell, delete it and click +Text instead.

Also check that the # has a space after it before the heading text.

Help me fix my markdown cell in Google Colab.

Add a Key Takeaways cell at the bottom

A strong portfolio notebook ends with a summary. This tells anyone reviewing your work what you accomplished and signals that there is more to come in the series.

  • Scroll down to the very bottom of your notebook (below the last markdown explanation cell).
  • Click the +Text button to add a new text cell at the bottom.
  • Type the following markdown into the new cell:
## Key Takeaways and Next Steps

**What I built today:**
An AI Readiness Assessment Tool that collects organizational data through interactive prompts and generates a formatted executive summary report.

**Python skills I used:**
- `variables` to store organizational data
- `print()` to display formatted output
- `input()` to collect information interactively
- f-strings to combine variables into professional reports

**Next up (Day 2):**
Adding ROI calculations to help organizations quantify the financial impact of AI adoption.

What does this cell accomplish?

This cell wraps up your notebook like the conclusion of a consulting report. It tells a reviewer exactly what skills you demonstrated and what business value the tool provides.

The Day 2 hint shows this is part of a larger body of work, which signals ambition and follow-through.

  • Press Shift+Enter to render the cell.
  • You should see a formatted section with a heading, bold labels, bullet points with inline code formatting, and the Day 2 teaser.
Save and share your notebook

Your notebook is polished and complete. The last step is saving it to Google Drive and generating a shareable link you can put on a resume or LinkedIn profile.

  • Save your notebook by pressing Ctrl+S (Windows) or Cmd+S (macOS).
  • Click the Share button in the top right corner of the notebook.
  • In the sharing dialog, click Change to anyone with the link under General access.
  • Make sure the permission is set to Viewer.
  • Click Copy link and save it somewhere you can find it later.

Why share as Viewer?

Viewer access lets anyone see your notebook and its outputs without being able to edit your work. They can still make a copy to experiment with, but your original stays untouched.

This is the same way Google Docs sharing works. Your notebook is stored in Google Drive as an .ipynb file.

  • Confirm the share dialog shows Anyone with the link with Viewer access before closing it.

Don't see the Share button?

Make sure you are signed into your Google account. The Share button only appears when the notebook is connected to Google Drive. If you opened Colab in incognito mode, the notebook may not be saved to Drive yet.

Try clicking File then Save first. The Share button should appear after the file is saved to Drive.

Help me share my Google Colab notebook.

Your notebook now has clear sections with markdown headings, code cells with visible outputs, and a shareable link ready for your resume or LinkedIn.

Your notebook is polished and shareable via Google Drive. Next up, you will save it to GitHub to build a professional portfolio that hiring managers and clients can browse.

Save Your Notebook to GitHub

In Step 4, you polished your notebook with a professional header, a Key Takeaways section, and a shareable Google Drive link. Your notebook reads like a consulting deliverable.

Google Drive is great for quick sharing, but GitHub is where hiring managers and clients expect to see your technical work. A GitHub profile with real projects signals that you build things, not just talk about them. In this step, you will save your notebook to GitHub and download it as a Python script file.

In this step, get ready to:
  • Set up a GitHub account and create a repository for your Python portfolio.
  • Save your notebook to GitHub directly from Google Colab.
  • Download your notebook as a .py Python script file.
Set up your GitHub account and repository

GitHub is a website where developers and technologists store their code publicly. Having a GitHub profile with real projects is one of the strongest signals you can send to employers and clients.

✔️ I already have a GitHub account

  • Go to github.com and sign in to your account.

ⓧ I need to create an account

  • Go to github.com in your browser.
  • Click Sign up in the top right corner.
  • Enter your email address.
  • Create a password.
  • Choose a username.
  • Complete the verification puzzle and click Create account.
  • Check your email for a verification code and enter it on the next screen.

Now create a repository to store your Python portfolio notebooks.

  • Click the + icon in the top right corner of GitHub.
  • Select New repository.
  • Enter python-portfolio as the repository name.
  • In the Description field, type 15-day Python learning portfolio - AI business tools built in Google Colab.
  • Make sure Public is selected so anyone can see your work.
  • Check the box for Add a README file.
  • Click Create repository.

What is a repository?

A repository (or "repo") is a folder on GitHub that holds your project files. Think of it like a Google Drive folder, but designed for code and visible to anyone on the internet.

The README file is a welcome page for your repository. GitHub displays it automatically when someone visits your repo.

You should see your new python-portfolio repository page with a README file displayed.

Don't see the Create repository button?

Make sure you are signed in to GitHub. If you just created your account, check that you completed the email verification step. GitHub requires email verification before you can create repositories.

Help me create a GitHub repository for my Python portfolio.

Save your notebook to GitHub from Colab

Google Colab has a built-in feature that saves your notebook directly to a GitHub repository. No terminal commands or downloads needed.

  • Switch back to your Google Colab notebook tab in your browser.
  • Click File in the top menu bar.
  • Click Save a copy in GitHub.

First time connecting to GitHub?

If this is your first time using this feature, a popup asks you to authorize Google Colab to access your GitHub account. Click Authorize to continue. This is a one-time step.

If your browser blocks the popup, check for a blocked popup notification in your address bar and allow it.

  • In the Repository dropdown, select your python-portfolio repository.
  • Leave the Branch set to main.
  • In the Commit message field, type Add Day 1 AI Readiness Assessment notebook.
  • Check the box for Include a link to Colaboratory.
  • Click OK.

Colab opens a new browser tab showing your notebook file on GitHub. You should see your notebook's code and markdown cells rendered on the page.

Don't see your repository in the dropdown?

Make sure you authorized Colab to access your GitHub account in the popup. If you dismissed the popup, close the save dialog, then try File > Save a copy in GitHub again.

If you still do not see your repository, try opening GitHub in another tab to confirm you are signed in, then return to Colab and retry.

Help me save my Colab notebook to GitHub.

Download your notebook as a Python script

Your notebook is now on GitHub as an .ipynb file. You can also download a plain .py Python script version for your local files.

  • In Google Colab, click File in the top menu bar.
  • Hover over Download.
  • Click Download .py.

What is the difference between .ipynb and .py?

An .ipynb file is a notebook that keeps your code cells, markdown text, and outputs together. GitHub renders it as a formatted document with sections and results visible.

A .py file is a plain Python script with just the code. Your markdown cells become comments. This is useful for running the code outside of Colab.

  • Check your Downloads folder. You should see a file ending in .py.
  • Open the file in any text editor. You should see your Python code with your markdown cells converted to comments.

Secret mission

Add AI Maturity Scoring

Your assessment tool collects information, but what if it could also analyze that information and categorize organizations into maturity stages? Add a scoring system that turns raw answers into a professional AI Maturity Report with stage classifications.

Clean Up Your Resources

Clean Up Your Resources

This project runs entirely in Google Colab, so there are no ongoing costs. Your notebook is saved in Google Drive automatically and takes up minimal storage.

Decide whether to keep your notebook in Google Drive and GitHub, download a local copy, or delete everything entirely.

Resources you used:

  • Day_1_AI_Readiness_Assessment_Tool.ipynb notebook stored in Google Drive.
  • python-portfolio repository on GitHub.

Keep everything running

No action needed. Choose this if you plan to continue building on this notebook for the rest of the 15-day series.

  • Your notebook stays in Google Drive for free with no storage limits to worry about.
  • Your GitHub repository remains public and accessible. You will add more notebooks to it throughout the series.
  • Access your notebook anytime at colab.research.google.com by clicking File then Open notebook.
  • Your shareable link remains active as long as the file exists in your Drive.

Pause - I'll come back to this later

Close the Colab tab to free up browser memory. Your notebook is already saved in Google Drive and GitHub, and will be there when you return.

  • Close the Google Colab browser tab.
  • To reopen later, go to colab.research.google.com and find your notebook under Recent or Google Drive.
  • Download a local backup by clicking File, then Download, then Download .ipynb.

Delete - I don't want to use this again

Remove the notebook from Google Drive and delete the GitHub repository entirely.

Delete the notebook from Google Drive:

  • Open Google Drive at drive.google.com.
  • Find Day_1_AI_Readiness_Assessment_Tool.ipynb in your files. It will be in the Colab Notebooks folder.
  • Right-click the file and select Move to trash.
  • To permanently delete it, open Trash in the left sidebar and click Empty trash.

Delete the GitHub repository:

  • Open your python-portfolio repository on GitHub.
  • Click Settings in the repository's top navigation bar.
  • Scroll to the bottom of the page to the Danger Zone section.
  • Click Delete this repository.
  • Type the repository name to confirm, then click the delete button.

Nice Work!

Nice Work!

You just built your first Python tool from scratch. Your AI Readiness Assessment notebook collects real organizational data, formats it into a professional executive summary, and lives on your GitHub profile as a public portfolio piece.

Here's what you accomplished today:

  • Built an AI Readiness Assessment Tool in Google Colab using variables, print(), input(), and f-strings to collect and format organizational data.
  • Generated a formatted executive summary report that reads like a consulting deliverable, positioning you as an AI Socio-Technical Strategist.
  • Saved your notebook to GitHub as a portfolio-ready repository and downloaded it as a .py Python script.
  • Secret Mission: Added a maturity scoring system that categorizes organizations into AI Maturity Stages with formatted output using .upper() and string multiplication.

Ready to quiz yourself?