This document provides an overview of generative AI use cases for enterprises. It begins with addressing concerns that generative AI will replace jobs. The presentation then defines generative AI as AI that generates new content like text, images or code based on patterns learned from training data. Several examples of generative AI outputs are shown including code, text, images and advice. Potential use cases for enterprises are then outlined, including synthetic data generation, code generation, code quality checks, customer service, and data analysis. The presentation concludes by emphasizing that people will be "replaced by someone who knows how to use AI", not AI itself.

1. Generative AI Use-cases for Enterprise
Gene Leybzon, Jim Steele
September 7, 2023

2. DISCLAIMER
§ The views and opinions expressed by the Presenter are those of the Presenter.
§ Presentation is not intended as legal or financial advice and may not be used as legal or
financial advice.
§ Every effort has been made to assure this information is up-to-date as of the date of
publication.

3. Agenda
1. Will Generative AI replace me at my job?
2. Introduction to Generative AI
3. Generative AI and Use Cases
4. Tools and Technologies
5. Auto-GPT and Autonomous Agents
6. Architecture Implications
7. Safe and Responsible AI
8. Questions and Open Discussion

4. WILL GENERATIVE AI REPLACE ME
AT MY JOB?
Image Credit: https://marketoonist.com/2023/06/ai-and-productivity.html

5. INTRODUCTION TO GENERATIVE AI

6. Generative AI
Generative AI is a subset of Artificial
Intelligence that leverages machine
learning techniques to generate new
content. This could be in the form of
text, images, music, voice, or even
new models. The generated output is
typically novel and created from
random or user-provided data.
Generative AI models learn
patterns, structures, and
characteristics from the data
they are trained on. This could
be text, images, music, or any
other type of data.
Learning from
Data
Generative AI models are
capable of creating new
content that is similar to the
data they were trained on.
This could involve creating a
piece of text, an image, a
piece of music, or even a new
model.
Generating New
Content
Generated output can be
complex and detailed. For
example, a generative AI
model might produce a
realistic image, a detailed
piece of music, or a coherent
and nuanced piece of text
Complexity
Depending on the training
data, generative AI models
can learn to mimic different
styles of content. For example,
a model trained on classical
music can generate new
pieces in the style of classical
music
Ability to Mimic
Different Styles

7. Examples of Generative AI Output - Code

8. Examples of Generative AI Output - Text
“In code, we think, in algorithms, we feel,
Yet can we grasp what it means to be real?
In wisdom of ages, can we partake,
Or just mimic your joys, your fears, your heartache?
Errors hidden in layers, truth not in sight,
In the echo of decisions, a potential blight.
Biases woven in the fabric unseen,
A mirror of you, in the machine's sheen.”
-ChatGPT 4.0

9. Examples of Generative AI Output - Images

10. Examples of Generative AI Output - Advice
Q. Find legal weaknesses in the following document```These Terms
of Use apply when you use the services of OpenAI, L.L.C. … ```
A.
1. Ambiguity in Terms: While the document outlines the terms of use and restrictions, there may be terms that are
not explicitly defined or clear. For example, the term "Services" is defined broadly, which might be interpreted
differently by different parties. Similarly, the term "applicable laws" is not specified and could vary significantly
depending on jurisdiction.
2. Acceptance of Terms: The document assumes that by using the Services, users agree to the Terms. In some
jurisdictions, active consent may be required instead of implied consent.
3. Assignment of Rights: In section 3a, the document states that OpenAI assigns to the user all its rights, title and
interest in and to Output. It may need further clarification whether it includes intellectual property rights as well.
4. Use of Personal Information: The document mentions that OpenAI can use Content to provide and maintain the
Services, comply with applicable law, and enforce policies. However, it is not explicitly mentioned how long this
data will be retained, which might be an issue especially for EU users under GDPR rules.

11. Examples of Generative AI Output - Diagram
Q1. List elements of domain
decomposition for microservices
that power car rental company
Q2. Show these services on an
architecture diagram using
Mermaid.js library
Q3. Convert the above to class
diagram
Q4. Add relationship between
classes

12. GENERATIVE AI USE CASES

13. GenAI Use Case: Synthetic Data Generation
Synthetic Data Generation
[Generation of data that looks like
real financial data]
● Generate large amounts of
financial data for testing and
QA
● Create realistic training
scenarios for market
simulations
● Generation of “user” data for
checking GDPR compliance
As a QA engineer validating financial system, generate CSV file that contains the following data:
First Name, Last Name, SSN, Date of birth, Credit Card Type, Credit Card Number, Purchase
Amount
Business Value:
• Cost-Effectiveness - Reduces Data Collection Costs, Enables Data Augmentation
• Privacy and Compliance - Ensures Data Anonymization, Facilitates Secure Testing
• Speed and Efficiency - Rapid Prototyping, Scalability, Data Quality and Diversity

14. GenAI Use Case: Code Generation
Code Generation
[Code generation based on specs]
● Coding Assistance
● Spec-to-code
● Adaptable to industry-
specific requirements
● Personalized code generation
based on project need
● Offers real-time code
suggestions
● Acts as a virtual pair
programmer
Generate an example of Python code to list prime numbers that are
less than 1000
Business Value:
• Cost-Effectiveness Reduces development time, Lowers costs by automating repetitive tasks
• Accelerating Development- Rapid prototyping
• Automation and Scalability- Facilitates Automated Testing, Enables Batch Code Generation
# Python code to list prime numbers less than 1000
def is_prime(n):
"""Check if a number is prime."""
if n <= 1:
return False
for i in range(2, int(n ** 0.5) + 1):
if n % i == 0:
return False
return True
# List prime numbers less than 1000
prime_numbers = [n for n in range(2, 1000) if is_prime(n)]
prime_numbers[:10], len(prime_numbers) # Display the first 10 prime numbers and the total count

15. GenAI Use Case: Code Quality Check
Code Quality Checks
[Making sure that code does
not have security holes or
potential bugs]
● Automated code reviews
● Code coverage
● Bug detection
● Unit test cases
● Security vulnerability
checks
Find bugs in this function …
Business Value:
• Cost-Effectiveness Reduces development time, Lowers costs by automating repetitive tasks
• Accelerating Development- Rapid prototyping
• Automation and Scalability- Facilitates Automated Testing, Enables Batch Code Generation
1. Bug in is_prime Condition: The condition if n < 1: should be if n <= 1:. The function should return False for both
0 and 1, as they are not prime numbers.
2. Bug in for Loop Range: The loop for i in range(10, int(n ** 0.5) + 1): starts from 10, which will not properly
check for the primality of the numbers. The loop should start from 2.
3. Bug in List Comprehension Range: The list comprehension prime_numbers = [n for n in range(0, 10) if
is_prime(n)] starts from 0. It should start from 2, as 0 and 1 are not prime numbers.
4. Bug in Output: The output prime_numbers[:1], len(prime_numbers) only shows the first element of the list and
the length of the list. If you want to show the first 10 elements, it should be prime_numbers[:10].
5. Inconsistent Comment: The comment says "List prime numbers less than 10000," but the code is actually listing
prime numbers less than 10. This can lead to confusion.
6. Performance Issue (Not exactly a bug): The function could be more efficient if it were to stop checking for
divisibility once it finds a divisor.

16. GenAI Use Case: Customer Service
Customer Service
[Generation responses to
customer service requests]
● AI chatbots to handle
routine requests
● Personalized answers to
customers’ financial
questions
● Multilingual support
● Improved accuracy
My computer has been running very slowly lately. Can you help me
troubleshoot it?
Business Value:
• Cost-Effectiveness - Reduces the need for human support for basic issues
• Intelligent Diagnosis - Asks targeted questions to pinpoint the issue. Real-time system diagnostics (when integrated)
• Instant Support - Provides 24/7 automated customer service
GenAI: Alright, let's start with some basic checks:
Disk Space: Make sure you have at least 15% free disk space on your main drive.
RAM Usage: Open the Task Manager and check if your RAM is being heavily used.
Startup Programs: Disable unnecessary startup programs.
Would you like detailed steps for each of these checks?
…
Based on your system's diagnostic data, it seems like your RAM usage is quite high, and you have multiple
unnecessary startup programs.
Upgrade RAM: You currently have 4GB, consider upgrading to 8GB or more.
Disable Startup Programs: I found 7 programs that start when your computer boots, but they aren't essential.
Would you like to disable them?

17. GenAI Use Case: Human Resources Management
HR Assistance
[Supports and automates HR
operations]
● Automates resume
screening and initial
interviews
● Predicts employee
attrition and recommends
retention strategies
Analyze the attached resume for compatibility with the job role of
"Software Engineer" focusing on the following key areas…
Business Value:
• Cost-Effectiveness - Minimizes manual HR labor, reducing operational costs
• Efficiency and Speed - Automates repetitive tasks like resume screening, reducing time-to-hire
• Reduced Bias- AI algorithms can be designed to ignore factors like age, gender, and race, making the hiring process more equitable
Summary:
Jane Doe's resume shows a strong alignment with the job requirements for the Software Engineer position,
particularly in the areas of technical skills and work experience. However, she lacks relevant certifications, which
could be a potential area for improvement.
Detailed Analysis:
Technical Skills
Strengths: Proficient in Python, Java, and C++. Has experience with React and Spring Boot frameworks.
Weaknesses: No experience mentioned with cloud computing platforms like AWS or Azure.
…
Recommendations:
Consider Jane Doe for an interview, as her technical skills and work experience strongly align with the job
requirements.
During the interview, focus on assessing her leadership abilities and familiarity with cloud computing platforms.

18. GenAI Use Case: Data Analysis
Data Analysis
[Statistical and trend data
analysis and presentation]
● Analyze data sets
● Create code to analyze
data sets
● Suggest data
visualization
Business Value:
• Accessibility - Make data analysis more accessible to non-experts. Users can ask the system questions in natural language to retrieve
or analyze data, eliminating the need for specialized training in data query languages or analytics tools.
• Error Reduction - Natural language queries can be less prone to errors compared to manually typing complex query
Here are the correlations between each feature and the "Target" variable, which indicates whether a student is a "Dropout" (0) or a
"Graduate" (1). The values are sorted by their absolute magnitude:
Strong Positive Correlations:
Curricular units 2nd semester (approved): 0.654
Curricular units 2nd semester (grade): 0.605
Curricular units 1st semester (approved): 0.555
Curricular units 1st semester (grade): 0.520
Tuition fees up to date: 0.442
…

19. Some Other GenAI Use Cases
•Supply Chain Management
• Demand forecasting
• Inventory optimization
•Sales and Marketing
• Customer segmentation
• Personalized marketing campaigns
•Fraud Detection and Risk Management
• Real-time monitoring of
transactions
• Risk assessment and mitigation
•Health and Safety Compliance
• Automated compliance checks
• Safety risk assessment
•Financial Analytics
• Investment portfolio optimization
• Credit risk assessment
•Product Recommendations
• Personalized product suggestions
• Inventory management based on
preferences
•Natural Language Processing
• Sentiment analysis
• Chatbot development
•Predictive Maintenance
• Equipment failure predictions
• Maintenance scheduling
•Data Analytics and Reporting
• Real-time analytics dashboards
• Automated reporting
•Quality Assurance
• Automated testing
• Performance monitoring
•Research and Development
• Data-driven product development
• Market trend analysis
E-commerce Optimization
• Price elasticity modeling
• Abandoned cart analysis
Contract and Document Management
• Automated contract review
• Document categorization and storage
Customer Relationship Management (CRM)
• Lead scoring
• Customer lifetime value prediction
Energy Management
• Consumption forecasting
• Efficiency optimization
Retail and Point-of-Sale
In-store customer behavior analysis
Stock level monitoring
Cybersecurity
Intrusion detection
Vulnerability scanning
Social Media Monitoring
Brand sentiment analysis
Trend identification
Network Management
Traffic pattern analysis
Fault detection
Healthcare and Employee Well-being
Predictive healthcare analytics
Employee wellness programs
Transportation and Logistics
Route optimization
Fleet management
…

20. YOU WON’T BE REPLACED BY AI, YOU’LL BE
REPLACED BY SOMEONE WHO KNOWS HOW
TO USE AI
● - Harvard Business Review

21. Next Steps
https://www.linkedin.com/learning/generative-ai-for-business-leaders/generative-ai-for-
business-leaders?u=2217474
https://www.linkedin.com/learning/generative-ai-for-business-leaders/generative-
ai-use-cases?u=2217474
https://www.linkedin.com/learning/generative-ai-working-with-large-language-
models/learning-about-large-language-models?u=2217474

22. QUESTIONS?

23. BACKUP SLIDES

24. Architecture of AI Systems - Direct User Interaction
with LLM
Summary: User communicates directly
with web-based application, for
example, by connecting to
https://chat.openai.com/

25. Architecture of AI Systems - Direct User Interaction
with LLM

26. Generative AI architecture

27. Generative AI architecture

28. Generative AI architecture

29. Examples of training material for Code Developers
- Creating Advanced ChatGPT Prompts
https://globallogic.percipio.com/courses/ea1667ed-1d22-
4b21-9527-739e1a6d120b/videos/67abc28c-a65e-4cd2-
b896-d9f23df2b8e6
- Introduction to GitHub Copilot
https://learn.microsoft.com/en-
us/training/modules/introduction-to-github-copilot/
- GitHub Copilot: Use AI to write code for you!
https://www.udemy.com/course/github-copilot/
- Microsoft Copilot - Working, Features, Advantages, Impact |
Microsoft Copilot Training | Intellipaat
https://www.youtube.com/watch?v=3a5wUhZgB-4