GenAI tools for non techies

Daniel Basso

AI tools

Copyright © 2025 by Daniel Basso

All rights reserved. No part of this publication may be reproduced, stored or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise without written permission from the publisher. It is illegal to copy this book, post it to a website, or distribute it by any other means without permission.

First edition

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Contents

Introduction

1. Table of Contents

2. Artificial Intelligence AI

3. History of AI

4. AI Winter

5. Major forms of AI and its approaches

6. Generative AI

7. Data and its importance

8. 2024: GenAI and E-commerce

9. 2025: GenAI and E-commerce

10. AI investments

11. AI and the Job Market

12. Business Analytics tools

13. Market research

14. Building Persona

15. SEO Optimization

16. Newsletter Content

17. Advertising More Easily

18. Social Media

19. Image Generation

20. Copyright

21. Websites Builders

22. Chat Bot

23. Presentations

24. Automation

25. Prompts

26. UI-UX

27. Logo generator

28. Design

29. Productivity

30. For product Managers

31. Videos

Introduction

There are many business needs that AI tools can help solve: logistics, market analysis, customer support, inventory prediction, actually it’s an endless listing. But without a technical background, I mean, considering you are not a techie, it can be difficult to make the connection between these challenges and the AI technology and tools that can help solve them. This pocketbook links common business needs to practical AI-based solutions tools, so you can build or buy the best AI systems for your organization, also if you are an entrepreneur or a professional in the workforce that needs to learn how to navigate this digital tools features and applications.

Hi there, I’m Daniel Basso. With over 15 years of experience working for multinational companies like Nestle, Coca-Cola, and Samsung, I’m not a book writer, however, I understand the value of having a quick and practical road map at your fingertips considering you are a not tech professional or do not have tech knowledge but need it to develop your daily business tasks. In today’s fast-paced world, and where cross-functional tasks dominate our schedules, diving deeply into non-core subjects can be challenging, and that is the objective of this material, bring you a concise, accessible, and action-oriented pocketbook with great business tools based on AI. While these tools have suited my needs, it’s important to note that the market is vast, offering countless options tailored to different requirements—whether B2B or B2C, small businesses, or large enterprises. Your choice will depend on your unique goals and workflow. One thing is certain: these tools can help unlock your potential and provide the fuel to go the extra mile.

I will start this pocketbook recapping AI and GenAI history and development and the current scenario mainly for digital commerce. However, you can skip it and jump directly to the tools topic.

Please PARDON any imperfections in my writing style. The intention of this pocketbook is not to serve as an academic resource but rather as a practical and effective guide to help streamline and simplify everyday job tasks.

Wishing you every success!

1

Table of Contents

There’s no table of contents—surprise! 😄 This way, you get to stumble upon all the book’s topics without cherry-picking. Trust me, it’s better this way!

2

Artificial Intelligence AI

AI has many definitions based on the nature of its techniques, its usage, and also the timeline of its research. However, the most common definition is as follows—AI is the intelligence and capability exhibited by a computer to perceive, learn, and solve problems, with minimal probability of failure. A more modern concept, AI is the ability of machines to think, learn, and solve problems like humans. It uses data and algorithms to perform tasks such as recognizing speech, making decisions, and creating content without human input.

The ability of AI to compute and achieve results within a shorter period of time than humans has made computers the cornerstone of automation across various industries. The computational work of humans is often prone to errors, is time-consuming, and exhibits diminishing accuracy as the problem gets harder to solve. However, computers have been able to fill this role for a long time, from the early beginnings of automation that can be observed in many passive forms in our daily life. One of the best examples of such automation is the introduction of optical Character Recognition (OCR), which converts embedded text in an image or document into a text source ready for computation. Computers enabled with OCR devices are more accurate and consume less time in reproducing the content than humans. Similarly, barcode scanners have led the way to faster checkout times at retail shops. Although the early systems were not completely intelligent per se, they are still recognized for their efficiency.

Although there was a lack of general criteria for AI in the early days, we will consider the major efforts made by researchers over the past eight decades in the following section to explain this ecosystem development.

3

History of AI

Numerous depictions of AI in the form of robots, artificial humans, or androids can be observed in art, literature, and computer science dating back to as early as the Greek mythology times. AI research and development gained mainstream progress in only the early 20th century. Here goes the story…the phrase artificial intelligence was coined during a summer workshop held at Dartmouth College in 1956 in New Hampshire. The workshop was called the Dartmouth Summer Research Project on Artificial Intelligence and was organized by Prof. John McCarthy, one of the mathematics professors at Massachusetts Institute of Technology (MIT). This workshop led to the development of AI as a special field within the overlapping disciplines of mathematics and computer science.

However, it is also notable that two decades before the Dartmouth workshop, the British mathematician Alan Turing had proposed the concept of the Turing machine, a computational model that can process algorithms, in 1936. He later published the paper Computing Machinery and Intelligence in which he proposed the concept of differentiating the response of machine intelligence from a human. This concept is widely known as the Turing test today.

Almost a decade after the summer workshop at Dartmouth College, the first chatbot, named Eliza, was showcased by AI researcher Joseph Weizenbaum at MIT in 1966. It was one of the first few chatbots to attempt the Turing test. After the invention of Eliza, a new range of expert systems and learning patterns evolved over the next two decades until the 1980s.

With the preceding basic understanding of AI, its history, and slow development, let’s consider some of the impediments and difficulties faced by researchers in the early days of AI in the following section.

4

AI Winter

AI winter is a term used by many in the IT industry to define a period of time when AI researchers