Generative AI explained in simple terms

Generative AI is a type of AI that creates content by first learning the patterns in existing data and then generating new content that follows those patterns in a similar manner.

That’s how generative AI can create a short story based on the style of a particular author, generate a realistic image of a person who doesn't exist, compose a symphony in the style of a famous composer, or create a video clip from a simple text description.

Generative AI vs other types of AI

Generative AI is unique compared to other types of AI in how it creates new combinations based on identified patterns in datasets. It does this by learning the statistical relationships between words, for example, to predict what comes next.

Here’s how generative AI compares and contrasts with other forms of AI:

Generative AI vs traditional AI

Traditional AI refers to AI systems that perform specific tasks by following predetermined rules or algorithms. They are primarily rule-based systems that cannot learn from data or improve over time without direct human intervention. Generative AI, on the other hand, can learn from data and generate new forms of it.

Generative AI vs machine learning

Machine learning enables a system to learn from data rather than through explicit programming. In other words, machine learning is the process by which a computer programme adapts to, and learns from, new data independently, leading to the discovery of trends and insights. Generative AI makes use of machine learning techniques to learn from and create new data.

Generative AI vs conversational AI

Conversational AI enables machines to understand and respond to human language in a human-like manner. While generative AI and conversational AI are similar—particularly when generative AI is used to generate human-like text—their primary difference lies in their purpose. Conversational AI is used to create interactive systems that engage in human-like dialogue, whereas generative AI is broader, encompassing the creation of various content types, not just text.

Generative AI vs artificial general intelligence

Artificial general intelligence (AGI) refers to highly autonomous, but currently hypothetical, systems that can outperform humans in most economically valuable tasks. If realised, AGI would be able to understand, learn, adapt, and implement knowledge across a wide range of functions. While generative AI can be a component of such systems, it is not equivalent to AGI. Generative AI focuses on creating new data instances, whereas AGI denotes a broader level of autonomy and capability.

What distinguishes generative AI from other types of AI?

Generative AI is having a profound impact on business applications by accelerating idea generation, creating highly tailored experiences, and streamlining workflows by reducing manual effort.

Some examples of tasks that generative AI accelerates:

Innovation

• Rapid prototyping: Generating multiple design concepts quickly to help designers and engineers iterate.
• Creative content generation: Enabling writers, artists, and musicians to explore new styles or ideas with AI-generated drafts.
• Scientific discovery: Generating new molecular structures by learning patterns from existing chemical databases, enabling scientists to predict chemical properties before synthesising them.
• Product development: Simulating user feedback or market responses to new concepts before launch.

Personalisation

• Custom content: Sending personalised emails, adverts, or product recommendations tailored to individual user behaviour.
• Adaptive learning: Creating lessons or quizzes tailored to a student’s pace and style.
• Healthcare: Generating personalised treatment plans or health insights based on patient data.
• Entertainment: Adapting storylines or visuals in games to match user preferences.

Automation

• Content creation: Assisting creatives with brainstorming through image generation, video editing, and more.
• Customer support: Assisting human agents in handling enquiries. AI chatbots help customers resolve issues and escalate them if they cannot.
• Code generation: Automating repetitive coding tasks or generating boilerplate code.
• Document processing: Summarising, translating, or extracting key information from large volumes of text.

How generative AI works

Generative AI operates on the principles of machine learning. However, unlike traditional machine learning models that learn patterns and make predictions or decisions based on those patterns, generative AI takes a step further—it not only learns from data but also creates new data instances that mimic the properties of the input data.

The cornerstone of generative AI is deep learning, a type of machine learning that mimics the human brain’s processing of data and creation of patterns for decision-making. This is achieved through the use of artificial neural networks, which consist of many interconnected layers that process and transfer information, mimicking neurons in the human brain.

Here’s a general workflow for putting generative AI to work:

Learning from data

Generative AI models begin by ingesting vast amounts of data—text, images, audio, or other formats. During training, the model identifies statistical patterns and structures within the data, which form the foundation for its ability to generate new content.

Recognising patterns and relationships

Once trained, the model recognises complex relationships between elements in the data. For example, in language models, this includes understanding grammar, context, tone, and even intent. In image models, it might involve recognising shapes, textures, and spatial arrangements.

Using prompts to generate new content

Generative AI responds to prompts—user inputs that guide the model in producing new content. These prompts can be questions, instructions, or examples. Based on the patterns it has learned, the model generates outputs that are coherent, contextually relevant, and often indistinguishable from content created by humans.

How people work with generative AI

Depending on their objectives and the tools they use, individuals interact with generative AI in a variety of ways:

• Writing and communication: Tools such as Grammarly and ChatGPT assist with drafting emails, refining tone, correcting grammar, and generating content ideas. Whether you are writing a report or composing a social media post, these tools help streamline the process and improve clarity.
• Coding: GitHub Copilot supports developers by suggesting code snippets, identifying bugs, and generating entire functions.
• Productivity and organisation: AI assistants can help their users with instant answers, routine tasks (such as scheduling meetings and data entry), and decision support. SAP’s Joule, for example, can provide users with insights based on the context of business data and automate repetitive tasks such as invoice matching. In fact, users can tailor Joule to their role and responsibilities, from finance to HR and more.
• Research and learning: Students and professionals use AI productivity assistants to explain complex topics, summarise articles, and brainstorm ideas.

By taking on routine and tedious tasks, generative AI tools free up time for people to take on more strategic responsibilities.

Types of generative AI

Generative AI models differ in what they do and how they’re built. Their strengths and problem-solving capabilities depend on their architecture. These differences matter because they shape how AI works in real-world scenarios, from writing and coding to image creation.

At a high level, generative AI models fall into several categories, each with its own approach to learning and generating new data:

1. Transformer-based models: Models built on transformer architectures use attention mechanisms to understand relationships between words or tokens across long sequences. This enables conversational and assistant AIs to generate coherent, context-aware text, even across paragraphs or entire documents
2. Generative adversarial networks (GANs): GANs consist of two neural networks, a generator and a discriminator. The generator creates new data, while the discriminator evaluates to determine their authenticity. Over time, this competitive relationship leads to refinement. Examples of this include digital image creation tools, which utilise GANs to generate and manipulate visuals.
3. Variational autoencoders (VAEs): One application of VAEs is the generation of music. They work by combining an encoder, which compresses data into a latent space, and a decoder, which reconstructs data from that space. The decoder introduces randomness, allowing for diverse outputs. In other words, music creation tools are trained on audio data and attempt to reconstruct it based on the sequences and patterns they identify.
4. Autoregressive models: These models generate data one step at a time, predicting the next element based on previously generated elements. This approach is commonly used in language modelling, where each word or token is generated sequentially. Autoregressive models power several popular generative AI tools.
5. Normalising flow models: This class of generative models transforms simple probability distributions into complex ones using a series of invertible functions. They are particularly useful for tasks where exact likelihood estimation is important, such as image generation.

Examples and use cases of generative AI

With its unique ability to create new content, generative AI is enabling a diverse range of interesting applications.

Enterprise use cases

Generative AI is transforming various industries by streamlining workflows and enabling innovation.

• Human resources: Generative AI is automating tasks such as drafting job descriptions and generating tailored interview questions based on candidate profiles. For example, Mahindra & Mahindra, the Indian automobile manufacturer, uses generative AI to make better hiring decisions more quickly.
• Supply chain management: AMD, the computing technology company, developed an AI-driven troubleshooting tool that analyses sales order confirmations, detects allocation issues, and identifies inventory shortages. Employees interact with the tool through a natural language chatbot, making complex data insights more accessible and actionable. This demonstrates how generative AI is enabling more intelligent decisions and more efficient operations.
• Professional services: By surfacing key metrics, generative AI is alerting users to risks and informing narratives with data-driven insights. At Accenture, it has empowered finance teams by reducing their workload and helping them make quicker, more informed decisions.

Text and conversational AI

Generative AI is revolutionising communication by producing human-like text that enhances user interaction. It enables advanced chatbots and virtual assistants to maintain natural, human-like conversations. These systems are more responsive and context-aware than previous generations, making them valuable tools for customer service, personal assistance, and more.

Also, tools such as writing assistants are helping people to express themselves with greater clarity and confidence. Whether they are drafting emails, summarising documents, or generating creative content, these text generation tools provide them with coherent, relevant, and grammatically correct language based on their prompts.

Images and design

In creative fields, generative AI is a powerful tool for visual iteration. In graphic design and architecture, it helps professionals rapidly generate unique design concepts and efficient floor plans based on training data. In art, platforms transform user-submitted images into artworks styled after renowned painters. Convolutional neural networks can also produce surreal, dream-like visuals, pushing the boundaries of digital creativity.

Music and video

Advanced models can now compose music across a wide range of genres, simulating multiple instruments and styles with impressive coherence and emotional depth.

In video production, cutting-edge generative AI systems can even create short, realistic clips complete with synchronised audio, ambient sound, and even dialogue. These models support cinematic and animated styles, incorporating user-provided references to personalise scenes—such as inserting a likeness of a person into a generated video. With physics-aware motion and lifelike rendering, these tools are opening up new possibilities for music videos, short films, and immersive digital experiences.

Challenges and risks of implementing generative AI

Challenges and risks in implementing generative AI encompass a range of technical, organisational, and ethical concerns that leaders must address as the technology evolves. Here, we explore some of the main challenges and strategies organisations can use to navigate them effectively.

• Data requirements: Generative AI models require a significant amount of high-quality, diverse, and relevant data to train effectively. Acquiring such data can be challenging, particularly in domains where data is scarce, sensitive, or protected, such as in healthcare or finance. Additionally, ensuring the diversity and sampling accuracy of the data to avoid bias in the generated output is potentially complex. One solution to this challenge could be the use of synthetic data—artificially created data that mimics the characteristics of real data. Increasingly, niche data companies are specialising in generating synthetic data that AI systems train on, while preserving privacy and confidentiality.
• Training complexity: Training generative AI models, especially the more complex ones such as GANs or those that are transformer-based, is computationally intensive, time-consuming, and expensive. It requires significant resources and expertise, which presents a barrier for smaller organisations or those new to AI. Distributed training, where the training process takes place across multiple machines or GPUs, helps accelerate the process. Additionally, transfer learning—a technique in which developers fine-tune a pre-trained model for a specific task—reduces training complexity and resource requirements.
• Controlling the output: Generative models might produce content that is inaccurate, irrelevant, or inappropriate. Improving a model’s training by providing more diverse and representative data helps address this issue. Additionally, implementing mechanisms such as filtering systems and feedback loops helps monitor and refine outputs. Embedding explainability and fairness into model design is essential to ensure trust and relevance.
• Ethical concerns: Generative AI raises several ethical concerns, especially in terms of the authenticity and integrity of the generated content. Deepfakes, created by GANs, can spread misinformation and facilitate fraud. Generative text models are also used to create misleading news articles or fake reviews. Establishing robust ethical guidelines for the use of generative AI is crucial. Technologies such as digital watermarking or blockchain help to track and authenticate AI-generated content. Additionally, developing AI literacy among the public can help mitigate the risks of misinformation and fraud.
• Regulatory hurdles: There is a lack of clear regulatory guidelines for the use of generative AI. As AI continues to evolve rapidly, laws and regulations struggle to keep pace, leading to uncertainties and potential legal disputes.

To ensure the responsible use of generative AI, strategic collaboration among technologists, policymakers, legal experts, and the broader public is essential. This collaboration should drive the development of robust governance frameworks, ethical standards, and clear regulatory guidelines that keep pace with technological advancements.

Equally important is data preparedness. Organisations must assess the maturity of their data—ensuring it is clean, consistent, and contextual—and build infrastructure that supports this.  Solutions should integrate data across systems while maintaining strong governance and privacy protections.

History of generative AI

Several key developments and milestones have marked the history of generative AI.

In the 1980s, data scientists seeking to move beyond the predefined rules and algorithms of traditional AI laid the groundwork for a generative approach with the development of the naïve Bayes classifier.

Later in the 1980s and 1990s, models such as Hopfield networks and Boltzmann machines were introduced to create neural networks capable of generating new data. However, scaling up to large datasets was challenging, and issues such as the vanishing gradient problem hindered the training of deep networks.

A breakthrough occurred in 2006 with restricted Boltzmann machines (RBM), which enabled the pre-training of layers in a deep neural network. RBMs not only solved the vanishing gradient problem but also led to the development of deep belief networks.

In 2014, generative adversarial networks (GANs) entered the scene, demonstrating an impressive ability to generate realistic data, particularly images. Around the same time, computer scientists introduced variational autoencoders, offering a probabilistic approach to autoencoders that supported a more principled framework for generating data.

The late 2010s saw the rise of transformer-based models such as GPT and BERT, revolutionising natural language processing with human-like text generation.

Today, generative AI models continue to push boundaries, with growing emphasis on ethical use and controllability.

The history of generative AI reflects rapid progress in theory and application, offering valuable lessons for responsibly harnessing its creative potential.

The future of generative AI

Generative AI—a concept previously confined to science fiction—has rapidly become an integral part of everyday work and life. Unlike traditional AI, which focuses on learning from data and automating decisions, generative AI adds the ability to create. This leap enables applications that were previously unimaginable, from generating realistic images and writing code to producing synthetic data for training.

Generative AI is also ushering in a new era of business AI for enterprises. Embedded directly into core processes, it helps organisations automate workflows, enhance customer interactions, and drive operational efficiency.

As generative AI continues to evolve, its potential to enhance human creativity and productivity will only grow—provided it adheres to thoughtful governance and a commitment to ethical use. Companies must deploy and utilise these technologies in an ethical, transparent, and compliant manner, adhering to global regulations.

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