What is Generative Artificial Intelligence and Where is it Applied?

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?

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:

CriterionTraditional AIGenerative AI
Primary GoalSolves tasks based on predefined rulesCreates new, original content
StrengthsEfficient, interpretable, excels at specific tasksCreative, handles ambiguity in task formulation
WeaknessesCompletely lacks a creative approachLess effective at image recognition and solving specific tasks
Key FeaturesDecision trees, pattern recognition, predictive modelingDeep learning, neural networks, creative data generation
Data RequirementsSuitable for structured dataUses large sets of structured and unstructured data for training
ApproachStructured, sequential, analyticalDynamic and creative
TransparencyStructured analysis and logical processesLess transparent due to complex algorithms, making results hard to trace
ApplicationsPredictive analytics, fraud detection, personalized recommendations, process automation, decision-making systemsAutomated 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:

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:

Despite its advantages, generative AI poses challenges:

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.

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:

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

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