What Is Generative AI And Why Does It Matter Right Now?

by admin Wednesday, October 28, 2026

Generative AI uses deep learning and neural networks to learn patterns from large sets of data to create text, images, audio, code and video.

ChatGPT had over 1 million signups in 5 days after being released to the public in November 2022. This shows how quickly AI tools have become available to everyday users.

Large language models require large amounts of data and computing power. GPT-3 required approximately 45 terabytes of text to train. This means that the quality, cost and environmental impact of models can be an important consideration.

It has been estimated by McKinsey that Generative AI could contribute up to $4.4 trillion to the global economy each year. This is particularly in knowledge based roles, software development, customer service and content creation.

Organisations will need to utilise human review, data governance, provenance, copyright review, identity security and security controls to reduce hallucinations, bias, deepfakes and data leakage.

What Is Generative AI And Why Does It Matter Right Now?

What Is Generative AI?

A tired guy works at a messy desk filled with coding.

Generative AI is about creating new content based on patterns found within training data. Unlike traditional AI models which are used for classification, scoring and prediction, Generative AI can be used for text generation, image creation, coding and audio/video generation.

According to the U.S. Government Accountability Office, Generative AI is AI systems that generate original content based on patterns and relationships derived from a large dataset. It is important to note that despite the high quality of content produced using Generative AI, there may be inaccuracies, fabrications or similarities to existing content which will need to be checked.

Generative AI can be used using large language models like GPT-3.5 and BERT, image models, diffusion models, generative adversarial networks and variational autoencoders. Each model uses different methods to process data. The correct model will depend on what you want to focus on: text, visual, audio or coding.

What is generative AI and what are its key features?

Generative AI is a branch of AI that generates content based on training patterns. These can be used to generate content such as text, images, code, audio, video, summaries, simulations, or structured data.

Generative AI can be seen as different to traditional AI models because instead of classifying emails into spam or predicting demand, it will create something new. Generative AI does utilise machine learning principles so will require data collection, cleansing, normalisation and testing to ensure it can be trusted by users within a real environment.

Large Language Models will be built using Transformers to better understand relationships between words and other tokens. Image generators will often use diffusion models, GANs or VAEs.

Many Generative AI models will use Reinforcement Learning from Human Feedback (RLHF). This ensures the generated outputs are more beneficial and safe for the user. This works by having humans compare or score the outputs of the model and feed this preference back into the model.

Prompt driven outputs – The model will follow your prompt and include the correct formatting, tone, restrictions and background context.

Predictive Patterns – Neural networks will identify the most likely next word, image, audio or code.
Multi-modal – Many AI models today can work with text, images, audio, spreadsheets, and files.
Human validation – A human must review the output to ensure facts, critical decisions, rights, and branding have been considered.

Generative AI can be used to support analytical, predictive, fraud, personalisation, content generation, and security processes. However, it will not replace non-AI tech at a set time. If a vendor is claiming something, it is a good idea to test use cases.

What can Generative AI create?

Text, Images, Code, Audio, Video and Synthetic Data. However, you should ask yourself if they can create what you need to a level of accuracy, privacy, and approval.

Tools such as OpenAI DALL·E, Stable Diffusion, Suno, Udio, Veo, LTX, and Sora demonstrate how advanced Generative AI can be with creating content beyond text. Each output will need to be reviewed differently. If people, medical data, legal, or confidential customer data is included then this should be reviewed.

Text – Large Language Models can generate reports, summaries, emails, code documentation, chatbot replies and draft policies. If the task requires fact finding, ask for a list of sources or comparison table.
Images – Image synthesis tools can generate concept art, product mock-ups, diagrams and campaign visuals. Hands, logos, text and branding should be reviewed as image models can cause distortion to small details.
Audio/Music – Tools like Suno and Udio can generate music. Text to speech can also be used to generate narration. You should get permission to clone someone’s voice.
Video – Veo, LTX and Sora can generate short videos, storyboards and product pre-views. Any synthetic media should be labelled if it can be confused with a real event or person.
Code – LLMs can generate scripts, web elements, APIs and tests. Security checks and code reviews should be conducted before going live due to potential weak authentication and unsafe dependencies.
Science/Medical – Models can be used to help generate visualisations for research but outputs should be validated by the researcher.
Business – Generative AI can be used to generate advertising copy, logos, sales material and mock-ups. There should be a record of approvals for any claims, licensed assets and final changes.

What are some common examples of Generative AI tools?

Many Generative AI tools can generate drafts, images, voices, translations, and code. You should choose the best tool for your content type, data control and review process.

Category	Tool or Service	Typical Outputs	Best Use and Review Need
Conversational agents	ChatGPT, Claude, Microsoft Copilot, DeepSeek, Google Gemini	Longform text Summaries Code snippets	Use for outlines, drafts, analysis, and knowledge work. Verify factual claims, calculations, citations, and any code before relying on the result.
Text-to-image generators	DALL-E 2, Stable Diffusion, Midjourney, Adobe Firefly	Photorealistic images Illustrations Style concepts	Use for concept art, social graphics, and mockups. Check licensing terms, visual defects, and whether the image could imply a real event or endorsement.
Text-to-speech	15.ai, ElevenLabs	Natural-sounding voices Voice narration	Use for demos, accessibility, and narration. Get written permission before using a recognizable voice or voice clone.
Translation services	Google Translate, DeepL	Fast translation Context-aware drafts	Use for first-pass translation and internal comprehension. Have a qualified reviewer check contracts, medical content, product safety text, and public communications.
Content automation platforms	Jasper	Marketing copy Ad variants Blog drafts	Use for idea generation and testing message variants. Keep brand approval and fact checking with a human editor.
Multimodal suites	Combinations of LLMs with image and audio models	Scripts with storyboards Interactive help systems	Use when one project needs text, images, voice, and video. Review every asset as a package so the message stays accurate and consistent.

How Does Generative AI Work?

A tired man works at a cluttered desk with multiple monitors.

Generative AI is trained on large volumes of data using machine learning algorithms. The model uses patterns it has learned from the data to generate predictions and responses. Generative AI models take your prompt and convert it into tokens or other formats that can be understood by a machine. They work out what is most likely to happen and provide outputs based upon probabilities.

By providing high quality data, clear instructions and testing to measure performance, along with human feedback, Generative AI models will improve. Using tools available within the Denodo platform, AWS, Azure and Oracle Cloud can help organizations connect to approved data sources. There needs to be controls in place before a wide audience can have access to the models.

How do machine learning models and training datasets enable generative AI?

Generative AI is powered by machine learning models which look at the statistical patterns within large amounts of training data. They do not store every single example but instead identify patterns that will help it generate a likely response to a request.

Machine learning uses statistical techniques developed over centuries and concepts from the early days of computing such as those from Alan Turing in the 1930s/40s. Modern day deep learning makes these concepts achievable on a larger scale with lots of parameters to tune in neural networks.

The huge amount of data GPT-3 was trained on (around 45 Terabytes of text) means the quality of data, cost of computing and impact on the environment is important. Having more data doesn’t mean you’ll get a better system. If your data isn’t relevant, has bias, contains sensitive information or isn’t labelled well you’ll get poor outputs.

Collect – Ensure you are using correct data for your task.
Clean – Remove any duplicate, damaged, sensitive, personal or poor quality data.
Train – Use machine learning to identify patterns in text, images, audio, video or code.
Test – Use your model on real examples, expected outputs, safe examples and failure examples.
Monitor – Monitor error rates, misuse of prompts, data drift and user feedback.

Use variational autoencoders, generative adversarial networks or large language models depending on what you want to identify patterns in. Decide what type of model family you want to use and have a quantifiable measure of quality.

What is the role of Large Language Models (LLMs)?

Large language models provide text focused generative AI by predicting the next token in a prompt. They can be used for generating text, summarising, classifying, translating, reasoning using patterns and generating code. However, they can confidently give incorrect information.

Large language models such as GPT-3 and Google’s BERT use a transformer architecture (introduced in 2017). This uses attention mechanisms to help the model determine which words/relationships are important.

Large language models can be used well for generating documentation, preparing customer service, enterprise search, data analysis summaries and code generation. However, they can be less safe to use if the user asks them to make decisions relating to medical, legal, financial, recruitment or security decisions.

Microsoft Copilot, Google Gemini, ChatGPT, Claude, GitHub Copilot, Google NotebookLM and Zoom AI Companion all include LLM capabilities. These can be seen as assistants with a specific task, using approved data and having clear outputs.

To ensure you’re using LLMs reliably you should start with a question. What should your model output? What data can you use? Who will approve the final answer?

How do I ensure generative AI provides accurate and reliable outputs?

By using generative AI on trusted data, having a clear task and testing your outputs against known answers you’ll get more reliable outputs. As generative AI is predicting likely outputs it can’t guarantee to be factually accurate.

Transformer based models use self attention and contextual embeddings to link relevant parts of your prompt. GANs use a generator and discriminator to compete. Diffusion models improve a noisy image through an iterative process. VAEs use compression and decompression.

Before using generative AI in a high risk scenario you should measure how reliable it is. Monitor how factually accurate your outputs are, how accurate your references are, how many things you’re missing, how many harmful outputs you’re getting, how many tasks you’re completing and how many answers need human intervention.

Reliability Check	How to Test It	What to Do if It Fails
Factual accuracy	Compare answers with a verified source set and known correct answers.	Require retrieval from approved documents and add expert review.
Hallucination risk	Ask the same question with missing or conflicting context and inspect unsupported claims.	Tell the model to state uncertainty and block unsupported citations.
Security	Test prompts that try to expose secrets, bypass rules, or trigger unsafe code.	Use access controls, red-team testing, endpoint security, and audit logs.
Bias and fairness	Compare outputs across relevant demographic and language scenarios.	Document the issue, adjust the workflow, and add human escalation.

NIST’s 2024 Generative AI Profile recommends managing risk across the AI lifecycle rather than treating evaluation as a one-time launch task. That means repeating tests after model updates, new data connections, or changes in user prompts.

Why generative AI is important today

A focused man in his cluttered workspace reviews code and AI designs.

AI capabilities are embedded within many common writing, design, coding, research, collaboration and business applications. Generative AI makes these capabilities available to more people. It makes it simple to generate text, images, videos, data analysis and code without training a model.

This provides a tangible benefit where AI can be used to save time by quickly generating a draft, searching through a pre-approved database and allowing more creative freedom. It also reduces time spent on repeated tasks.

What improvements have been made in Generative AI recently?

Advancements in transformer models, multimodal inputs and improved tools and evaluation have all helped improve generative AI. Users can now ask one system to read a document, analyse a file, write a draft, create an image prompt and explain its process in simple language.

Advancement	What Changed	Why It Matters to AI Users
Transformer foundations	Transformer-based foundation models trained on large text collections and later received task-specific tuning.	ChatGPT’s November 2022 public release made fluent conversational AI available to a broad audience.
Variational methods	Variational autoencoders introduced in 2013 gave researchers a method for generating and reconstructing images and speech.	VAEs helped establish methods that support image and audio generation.
Multimodal integration	Models began combining text, images, audio, files, and video in the same system.	You can move from a written concept to a storyboard, narration draft, and visual prompt in one workflow.
Private data connections	Organizations can connect approved internal knowledge to models with permissions and access controls.	Teams can ask questions about current internal documents instead of relying only on public training data.
Reliability testing	Developers added evaluation suites, red-team tests, and monitoring for unsafe behavior.	You have clearer ways to measure whether a model is ready for a particular job.
Tool ecosystem	APIs, software development kits, TensorFlow, PyTorch, and cloud services made experimentation easier.	Teams can test focused AI applications without building a foundation model from scratch.

Which industries are most affected by generative AI?

Industries such as healthcare, financial services, creative teams, software development and business often see the biggest impact. The best use cases are those that are high volume, clear, working with reliable data, have a quantifiable outcome and where there is an owner of the final decision.

Industry	High-impact Use Cases	Direct Benefit	Required Guardrail
Healthcare	Clinical note drafts, patient communication drafts, medical literature summaries, and administrative coding support.	Less time spent on repeat documentation and information retrieval.	Protect PHI, require clinician review, and never let a draft replace clinical judgment.
Financial Services	Fraud investigation summaries, customer service drafts, report generation, and research support.	Faster review of large document and transaction sets.	Validate calculations, follow compliance controls, and keep humans accountable for advice.
Creative and Marketing	Content creation, campaign concepts, ad variants, images, video storyboards, and sales scripts.	More concepts and test variants in less time.	Check claims, trademarks, licenses, likeness rights, and brand standards.
Business Operations	Meeting summaries, report drafts, process documentation, data analysis, and internal knowledge assistants.	Less manual formatting and faster access to approved knowledge.	Use role-based access, data governance, and review for material decisions.
Software Development	Code generation, test creation, bug explanations, documentation, and security review support.	Faster prototyping and less repetitive coding work.	Run tests, dependency checks, secret scanning, and peer review before release.

Care must be taken when using generative AI in healthcare as the FDA guidance released in January 2026 highlights that clinical decision support software shouldn’t be used to override or influence decision making by a health professional. It should not be used to make patient specific diagnosis or treatment decisions.

A safe workflow should be created. This should include removing any extra personal information, using an authorised platform, restricting it to just creation or summary, checking each clinical statement against the medical record, having an authorised person sign off and a record of this. This is also applicable to legal, finance, HR and security, where having a poor outcome can impact someone.

Why is Generative AI becoming popular across multiple industries?

Generative AI is becoming popular due to the increase in capabilities of models, increased availability on the cloud and ease of use. You can experiment with a use case without having to develop your own AI system from scratch.

It has been estimated by McKinsey that Generative AI applications can give up to $4.4 trillion of value to the global economy per year. This means focusing on workflows which are easily repeatable, have clear cost or time saving, have reliable data and an accountable owner.

Choose a repeatable process such as summarising, knowledge searching, creating content or documenting code.
Establish a starting point for how long, quality, error rate and cost the task takes.
Use prompts to ensure you know who the target audience is, what source data will be used, what rules and format is required.
Have a human sign off if the output will be used by customers, regulated, security or has high impact.
Only scale once the quality has met the level required in your pilot.

Using business intelligence tools, LLMs, computer vision and synthetic data can help you get value from large data sets. Generative AI should be used to give value to a real decision.

Challenges & Concerns with Generative AI

A weary individual struggles with computer errors at a cluttered desk.

Generative AI has the potential to cause issues with hallucination, bias, privacy, copyright, impersonation and security. Whilst there is a lot of focus on the capabilities of generative models, it is important to consider the quality of outputs and data governance.

Within your organisation, AI applications should have an owner, a defined list of acceptable uses, limits around sensitive data and a log of significant outputs. Your security team should take generative AI into consideration within cloud security, network security, identity, endpoint, digital forensics, ransomware and incident response.

What are the key ethical considerations?

Generative AI has ethical considerations around authenticity, consent, bias, privacy, copyright and ownership. It can create very realistic content but can cause confusion if presented as real.

There can be bias, copyright and inaccuracies in the training data that can be reflected in the generated content. This can occur where you request support for recruitment, credit, health or protected group content.

There will be issues around consent if you create realistic images, video or audio of real people. In May 2026 the FTC started enforcing notice and takedown requirements under the TAKE IT DOWN Act. This applies to platforms that handle non-consensual explicit imagery including AI-generated digital forgeries.

Under US copyright laws, human authorship is still required (U.S. Copyright Office, 2025). If using AI to help with a creative project, ensure you have documentation around human authorship, editing, choosing and arranging. Do not assume AI outputs will be protected.

What are the risks and possible abuse of generative AI?

Generative AI can be abused to create misinformation, deepfakes, phishing, insecure code, privacy issues and copyright issues. All outputs should be treated as unverified until approved by a qualified individual.

Misinformation: LLMs and image tools can create realistic mistakes. Ensure all facts, dates, calculations and claims from the source are checked.
Deepfake: Synthetic audio/video can cause issues with trust and reputation. Ensure you have processes in place to identify, check provenance, keep records of consent and escalate.
Phishing/Social Engineering: Attackers can use generative AI to create sophisticated scam messages. Ensure staff are trained to check any changes to payments, requests for passwords and out of the ordinary instructions using a secondary channel.
Prompt injection: Malicious text can be added to a document/webpage to try and trick the AI. Ensure sensitive tools are kept separate, have limited access and treat any external content as untrusted.
Data leakage: Users can share trade secrets, customer data, login details or PHI by pasting into an unauthorised tool. Ensure you have a clear data categorisation policy.
Copyright issues: AI can create content that looks like protected text/images. Ensure final output has been checked for licensing, trademarks, substantial similarity and third party rights.
Environmental impact: Training and inference can cause issues with data centre resources. Ensure you are using the right model size for the task and are not creating lots of unnecessary content.

Whilst ChatGPT Business, Enterprise, Edu, Healthcare and API customer data is not used to train the model by default (OpenAI), you still need to ensure you have processes in place to manage who can access, how long data is held, what applications are approved to connect and that the product meets your legal and security requirements.

Who is liable for harmful or incorrect outputs from generative AI?

Your organisation and individuals will still be liable for any harmful or incorrect outputs from generative AI. The model cannot take on any legal, professional or ethical responsibility for any statements, decisions made by customers, recommendations or actions taken.

This depends on the circumstances, legal requirements, contracts, professional responsibilities and the harm caused. In practice, whoever chose the tool, created the process, approved the output or sent to the customer should expect to be questioned if there was foreseeable harm.

Many academic publishers and professional bodies will require human authors to be responsible. COPE guidance recommends transparency and human responsibility so having this in place gives a sensible starting point for teams.

Ensure you have a named business owner and technical owner for each AI application.
Ensure you have clear processes in place for acceptable data, prompts, model access, review and retention.
Ensure any legal, medical, financial, employment or security content has been approved by subject matter experts.
Record any incidents, user complaints, model issues and mitigations.
Ensure policies are reviewed following significant model changes, new integrations or regulations.

Generative AI governance should be embedded within quality processes. Having clear processes, human checks, security checks and measurable reliability means users will have more flexibility.

Conclusion

Coworkers collaborate at a desk, focused on computer monitors.

Generative AI can be used to quickly turn clear prompts and source materials into useful first drafts of text, images, code, audio and video.

Whilst large language models, transformers, GANs, VAEs, and diffusion models allow this to happen, they are not fully judgemental. ChatGPT, Microsoft Copilot, Google Gemini, Claude, DALL·E and similar tools will perform best with a specific brief and thorough review.

Generative AI can be used to streamline your research, content creation, data analysis and code generation, however people will still be responsible for ensuring that things are factual, correct, safe and making the final decisions.

References

artificial intelligence, generative AI 1 Comment

What Is Generative AI And Why Does It Matter Right Now?

What Is Generative AI?