Author: Volnyanskyi A.
Generative Artificial Intelligence (GenAI) is a type of artificial intelligence capable of creating new content, such as text, images, or audio, by analyzing large volumes of data. Unlike traditional AI, which focuses on analyzing and classifying existing information, generative AI generates original content. In this article, we explore what generative AI is, its key differences from traditional AI, and its modern areas of application.
- What is Generative AI in Simple Terms?
- Where is Generative AI Applied? Industry Use Cases
- Most Popular Generative AI Models
- Key Points About Generative AI
What is Generative AI in Simple Terms?
Generative AI (GenAI) is one of the fastest-growing branches of artificial intelligence, creating content that didn’t previously exist, including text, audio, and visuals. Generative models use datasets as a foundation for training but create content from scratch rather than merely combining data based on a prompt.
How Did Generative AI Emerge?
Generative AI has been widely discussed in recent years, though the concept isn’t new—it aligns with Alan Turing’s test proposed in 1950. He argued that a machine could be considered intelligent if it generated responses indistinguishable from human ones. Generative models were developed in the 1960s and 1970s, but the most advanced ones, like deep learning models, emerged in the 1990s, capable of generating realistic text and replicating speech. The latest wave of popularity came with OpenAI’s GPT-3, the foundation for ChatGPT. According to a Bloomberg Intelligence study, by 2032, generative AI is projected to become a $1.3 trillion market, growing at an average annual rate of 42% over the next 10 years.
What is the Difference Between AI and Generative AI?
The operating principle of generative AI differs from traditional AI, also known as rule-based artificial intelligence. The latter performs specific tasks using predefined rules and algorithms, making decisions based on logical reasoning. Thus, traditional AI systems are mainly used for data analysis and data-driven decision-making, while generative AI creates entirely new content. The main differences between generative AI and traditional AI are outlined in this table:
| Criterion | Traditional AI | Generative AI |
| Primary Goal | Solves tasks based on predefined rules | Creates new, original content |
| Strengths | Efficient, interpretable, excels at specific tasks | Creative, handles ambiguity in task formulation |
| Weaknesses | Completely lacks a creative approach | Less effective at image recognition and solving specific tasks |
| Key Features | Decision trees, pattern recognition, predictive modeling | Deep learning, neural networks, creative data generation |
| Data Requirements | Suitable for structured data | Uses large sets of structured and unstructured data for training |
| Approach | Structured, sequential, analytical | Dynamic and creative |
| Transparency | Structured analysis and logical processes | Less transparent due to complex algorithms, making results hard to trace |
| Applications | Predictive analytics, fraud detection, personalized recommendations, process automation, decision-making systems | Automated content creation: text, visuals, and audio |
How Does Generative AI Work?
The process of generative AI can be represented in three stages: training, fine-tuning, and direct generation with periodic adjustments and evaluations.
Training is used to create a foundational model that underpins generative AI applications. Most often, this is a Large Language Model (LLM) used for text generation, supporting natural language processing (NLP) tasks like text generation, sentiment analysis, translation, and question answering. Other models create creative videos, images, or music, while multimodal models support multiple functions. Training results in a neural network that autonomously generates content based on user prompts.
How Large Language Models work was discussed in a previous article, “What is a Context Window in AI?” Several training methods exist, but the main ones are supervised and unsupervised learning. In supervised learning, labeled datasets are used, and the algorithm knows which results are correct. This method is best for medical diagnostics, spam and fraud detection, customer churn prediction, and recommendation systems. In unsupervised learning, large, unlabeled datasets are used, and the model identifies patterns independently. This suits tasks like customer segmentation (e.g., micro-segmentation in marketing), image compression, social media analysis, anomaly detection, and more.
The original article includes a diagram illustrating the simplest schemes for supervised, unsupervised, and semi-supervised learning.
Fine-tuning adapts a general model to specific tasks. This can be done through complex, labor-intensive fine-tuning or reinforcement learning with human feedback (RLHF), where humans evaluate generated content to improve accuracy and relevance. Users continuously refine the model through evaluations or Retrieval-Augmented Generation (RAG), a framework that extends the model to access the most current information.
This training and adaptation process makes generative AI an effective tool for generating various content types, listed below:
- Text: Supports all text formats, from business documents and web content to fiction. It automates routine tasks like creating concise product descriptions for retail or marketing materials—likely, you already know how to use ChatGPT or its analogs for such tasks.
- Images, Videos, and Graphics: AI image generators create unique, realistic images and videos from text descriptions, edit and stylize media, and generate characters, environments, and effects for video games and virtual simulations.
- Sound and Music: Produces natural-sounding speech for voice assistants or dubbing and original musical compositions in specified styles.
- Code: Generates, optimizes, and tests code in various programming languages, significantly speeding up development and debugging.
- Scientific Data: Generates synthetic data and complex structures, like new molecular compounds for pharmaceutical drug development.
Per Goldman Sachs, generative AI could boost global GDP by 7% (nearly $7 trillion) and increase productivity by 1.5 percentage points over the next 10 years. A VentureBeat study shows 18.2% of large companies worldwide use this technology, but only one-fifth plan to increase GenAI spending next year due to limited IT budgets or lower prioritization.
Advantages and Risks of Using Generative AI
The main advantage of generative AI is increased efficiency: it speeds up content creation, answers queries, automates routine tasks, reduces costs, and frees time for more creative or high-priority tasks. Benefits include:
- Enhanced Creativity: Actively used by artists, writers, designers, marketers, developers, architects, and others for diverse content types.
- Faster Decision-Making: Identifies patterns and generates numerous hypotheses quickly.
- Real-Time Personalization: Creates content in real time, delivering unmatched customer experiences.
- Constant Availability: Operates 24/7 without fatigue, vital for chatbots providing automatic responses.
- Lower Barriers to Entry: Enables people with minimal skills to create creative texts, simple apps, music, and more.
- Scalability: Generates large content volumes (e.g., text-to-image or product descriptions for large online stores) or thousands of lines of code quickly.
- Measurable Results: Top AI marketing tools increase retention by 38% and sales revenue by 26% (source data in the original article).
Despite its advantages, generative AI poses challenges:
- Lack of Transparency: The logic and operation of GenAI systems are often unclear, even to developers, complicating control and predictability.
- Inaccurate Information: Generated content may be misleading and requires manual verification.
- Bias: Models may reflect biases in training data, like discrimination or intolerance.
- Data Privacy: Lack of robust protections risks making user inputs publicly accessible.
- Cybersecurity Risks: Malicious actors use GenAI for advanced cyberattacks, including deepfakes for social engineering.
- High Energy Consumption: Systems require significant energy resources, negatively impacting the environment.
Where is Generative AI Applied? Industry Use Cases
Generative AI is used in numerous fields and incredible projects, from education and healthcare to big data processing and predictive analytics. Key areas where it delivers maximum impact include:
Marketing
GenAI assists in creating website content, marketing materials (text, audio, video), personalized customer interactions, A/B testing, improving customer experience via chatbots and virtual assistants, and generating new ideas.
Example: Coca-Cola used GenAI (OpenAI and Bain) for its “Create Real Magic” campaign, generating videos and graphics, increasing social media engagement and user-generated content quality by 40%.
Tools like Taluno by Colobridge combine GenAI and predictive analytics to create hyper-personalized messages delivered at optimal times to micro-segmented audiences, boosting engagement, satisfaction, retention, and sales.
E-Commerce
Applications include generating product descriptions, AI-driven images (e.g., products in real-world settings), and virtual assistant bots.
Example: Marks & Spencer used GenAI to personalize online shopping, offering 40 million fashion recommendations, attracting 450,000 users to a style quiz, and automating 80% of product descriptions, increasing online sales by 7.8%.
Entertainment
GenAI transforms entertainment by creating personalized multimedia content from user prompts and entirely new virtual worlds, including original music, realistic or fantastical sound effects, infinite gaming worlds, or non-player characters.
Example: Ubisoft’s Ghostwriter AI tool automates dialogue writing for characters, allowing writers to focus on plot and emotions, speeding development, and ensuring immersive gameplay.
Finance
GenAI creates chatbots and AI assistants to improve customer service speed and quality, offer personalized consultations and product recommendations, and identify fraudulent schemes or unreliable borrowers.
Example: Klarna’s AI assistant handles two-thirds of inquiries, resolving most in under two minutes, increasing customer satisfaction by 85% and saving the Swedish fintech company millions annually.
Healthcare
GenAI aids in drug development, medication testing, creating synthetic patient datasets for AI training, simulating clinical trials, and studying rare genetic diseases.
Example: Insilico Medicine used GenAI to develop a fibrosis drug candidate in 46 days (versus 1–1.5 years) by analyzing protein targets and proposing effective compounds.
Manufacturing
GenAI optimizes product design, reduces material costs, improves productivity, and enhances logistics planning.
Example: Siemens’ Industrial Copilot reduced turbine component weight by up to 75% and development cycles by 30%, saving resources and increasing efficiency.
Automotive
GenAI designs new car models, microchips, and vehicle components.
Example: Ferrari uses GenAI for rapid prototyping of lightweight parts and personalized digital car features, accelerating design and improving customer satisfaction.
Energy
GenAI improves customer service, develops energy efficiency programs, and optimizes electricity production.
Example: Shell’s AI processes over 20 billion data rows weekly from 10,000+ equipment units, reducing failures by 40%, maintenance costs by 20%, and downtime by 35%, enhancing reliability and efficiency.
Real Estate
GenAI creates property listings, automates lease and purchase contracts, accelerates market data analytics, and more.
Example: JLL, a commercial real estate company, uses AI to analyze lease contracts, speeding processing by 30%, improving manager efficiency, and reducing dispute risks.
Online Education
GenAI supports personalized learning materials and scenarios, automates grading, creates interactive learning environments, and detects AI-generated content in assignments.
Example: Khanmigo, an AI tutor from Khan Academy, personalizes learning, guiding students step-by-step and increasing classroom engagement, with 95% of teachers confirming its effectiveness.
Most Popular Generative AI Models
Many companies actively implement or test solutions based on leading Large Language Model (LLM)-based GenAI models for content creation. The top models as of 2025 include:
- GPT (OpenAI): A cutting-edge multimodal model processing and generating text, images, and voice, excelling in speed, reasoning, and contextual understanding, ideal for writing tests, answering questions, and natural language conversations.
- Claude (Anthropic): Focuses on safe, cautious responses with long dialogue memory, perfect for customer support and high-responsibility tasks.
- Gemini (Google): Designed for deep integration with Google tools, excelling in code development, testing, research, and providing accurate, comprehensive answers.
- Mistral: An open-source, lightweight, flexible model, ideal for companies needing AI control on their infrastructure.
- LLaMA (Meta): Developer-focused, easily adaptable for specific tasks with minimal computational resources, accessible to a wide audience.
- DALL·E (OpenAI): Turns text descriptions into detailed, creative images for design and marketing.
- Sora (OpenAI): A promising model for creating short video clips from text, with potential in marketing and creative industries.
- Midjourney: A leader in high-quality image generation, widely used in design, branding, and creative projects for its artistic capabilities.
- MusicGen / Suno: Platforms for creating original music from text, useful for content creators and musicians needing quick audio or unique melodies.
- RunwayML: A toolkit for generating and editing professional-quality video, suitable for filmmakers and marketers.
- GitHub Copilot X (Microsoft/GitHub): Assists developers in writing code, debugging, and suggesting features within development environments, speeding up coding.
The most popular GenAI tools in 2025 are fully operational, addressing one or several related tasks.
Colobridge Expert:
“Generative intelligence is revolutionizing industries by generating high-quality, personalized content quickly. This relies on robust IT infrastructure to handle complex workloads. To deliver the high computational power GenAI requires, it often uses GPUs and specialized Tensor Processing Units (TPUs) for TensorFlow. Early signs suggest ‘Generative AI as a Service’ may emerge in leading cloud providers’ portfolios, making it more accessible across industries. Infrastructure based on dedicated servers, tailored to individual needs, is optimal for AI workloads. Colobridge specialists can design and implement a project meeting your expectations, with optional infrastructure management and administration. Contact us to learn more about Colobridge’s platform for hosting your IT services.”
Key Points About Generative AI
- Generative AI (GenAI) creates new content: text, images, audio, and video.
- Unlike traditional AI, it generates content from scratch, not just analyzes data.
- Operates using neural networks and Large Language Models (LLMs) through training and fine-tuning.
- Main applications: marketing, e-commerce, finance, healthcare, manufacturing, and education.
- Top GenAI models: GPT, Claude, Gemini, DALL·E, Midjourney, Copilot, Runway, and more.
- Advantages: Automation, personalization, time and resource savings.
- Risks: Lack of transparency, fake content, data leaks, high energy consumption.
- Companies like Coca-Cola, Klarna, Ubisoft, Siemens, and Ferrari implement GenAI.
