Enterprise GenAI Adoption & Workforce Impact Dataset (100K+ Rows)

This dataset originates from a multi-year enterprise survey conducted across industries and countries. It focuses on the organizational effects of adopting Generative AI tools such as ChatGPT, Claude, Gemini, Mixtral, LLaMA, and Groq. The dataset captures detailed metrics on job role creation, workforce transformation, productivity changes, and employee sentiment.

Data Schema

columns = [
  "Company Name",                      # Anonymized name
  "Industry",                          # Sector (e.g., Finance, Healthcare)
  "Country",                           # Country of operation
  "GenAI Tool",                        # GenAI platform used
  "Adoption Year",                     # Year of initial deployment (2022–2024)
  "Number of Employees Impacted",     # Affected staff count
  "New Roles Created",                # Number of AI-driven job roles introduced
  "Training Hours Provided",          # Upskilling time investment
  "Productivity Change (%)",          # % shift in reported productivity
  "Employee Sentiment"                # Textual feedback from employees
]

Load the Dataset

import pandas as pd
df = pd.read_csv("Large_Enterprise_GenAI_Adoption_Impact.csv")
df.shape

Basic Exploration

df.head(10)
df.describe()
df["GenAI Tool"].value_counts()
df["Industry"].unique()

Filter Examples

Filter by Year and Country

df[(df["Adoption Year"] == 2023) & (df["Country"] == "India")]

Get Top 5 Industries by Productivity Gain

df.groupby("Industry")["Productivity Change (%)"].mean().sort_values(ascending=False).head()

Text Analysis on Employee Sentiment

Word Frequency Analysis

from collections import Counter
import re
text = " ".join(df["Employee Sentiment"].dropna().tolist())
words = re.findall(r'\b\w+\b', text.lower())
common_words = Counter(words).most_common(20)
print(common_words)

Sentiment Length Distribution

df["Sentiment Length"] = df["Employee Sentiment"].apply(lambda x: len(x.split()))
df["Sentiment Length"].hist(bins=50)

Group-Based Insights

Role Creation by Tool

df.groupby("GenAI Tool")["New Roles Created"].mean().sort_values(ascending=False)

Training Hours by Industry

df.groupby("Industry")["Training Hours Provided"].mean().sort_values(ascending=False)

Sample Use Cases

• Evaluate GenAI adoption patterns by sector or region
• Analyze workforce upskilling initiatives and investments
• Explore employee reactions to AI integration using NLP
• Build models to predict productivity impact based on tool, industry, or country
• Study role creation trends to anticipate future AI-based job market shifts