What is generative AI?

Businesses have sought different forms of automation and intelligent data processing for years, but generative AI has opened the floodgates of investment and enterprise interest.

Formed on the back of machine learning and natural language processing (NLP), generative AI entered the public eye through the popularity of ChatGPT and now dominates discussions around the technology.

This type of AI is being used to generate detailed text and image outputs through simple user input. Increasingly, it has been integrated within business environments to automate a range of tasks.

Generative AI has also sparked interest across the business world in the past four years because of the degree to which it can be personalized. With the right approach, it can radically improve worker productivity and help companies provide customers with more intuitive user experiences.

How does generative AI work?

As a term, ‘generative AI’ refers broadly to AI systems capable of producing outputs based on a prompt. These come in a few different forms but the most popular models rely on complex artificial neural network (ANN) architecture known as a transformer. In simple terms, transformers take a prompt and then output a response based on statistics and the exhaustive training the AI model has been subjected to.

Transformers convert inputs into context, breaking words down into mathematical values that inform the model’s output. For example, when a user inputs “Which city is Microsoft’s HQ in?”, the transformer converts the words into ‘tokens’ of data. These are combined to form a coherent ‘vector’ of context used to produce a statistically relevant output.

If the model in the example had been trained well, and on a specific company’s data, it could pick out the contextual significance of the terms ‘HQ’ and ‘city’ to produce a relevant output, such as “Microsoft is headquartered in Redmond, Washington”.

As opposed to forms of AI that categorise information, generative AI instead relies on modelling for its understanding of the dataset structure and then it generates examples that might relate or match.

Two main forms of neural networks are at play here – Generative Adversarial Networks (GANs) and Transformers – working in slightly different ways. The former is usually involved with AI prompts to create visual and multimedia content from images and text, while the latter takes information from the internet to generate textual output from the questions users ask of it.

What is the history of generative AI?

Generative AI’s popularity in recent years is a result of many years of experts working on the technology in theoretical and practical applications. Dutch researchers, for example, wrote about the philosophical underpinnings of generative AI as far back as 2012. Indium Software released a white paper [PDF] in the early 2020s highlighting how generative AI could be used both creatively and in high-friction workplaces like healthcare.

It has since gone on to become embedded in a multitude of industries and businesses across the globe with transformer models rising to prominence through the research paper Attention Is All You Need. These models quickly became prized for the efficient and performant way they could be used to produce coherent AI output.

This was a significant breakthrough fed directly into subsequent products at companies like OpenAI and Google DeepMind.

Now though, generative AI has moved on to encompass Diffusion Models for producing high-quality images and video alongside Variational Autoencoders (VAEs) to generate data points. Hybrid models including both Transformers and GANs are also common.

Many generative AI product names are now very well-known to the general public such as ChatGPT, Claude, Copilot, and Gemini.

What are some examples of generative AI?

The generative AI market has grown incredibly quickly; Mordor Intelligence suggested that in 2026 it’s worth nearly $30bn with projections it could grow to $125bn within the next five years. There are now innumerable Large Language Models (LLMs) and tools such as AI agents that rely on the technology.

For many businesses and consumers, the standout is still OpenAI’s ChatGPT while Microsoft users have access to Copilot, which works within a wide range of Microsoft apps in its 365 suite and Bing search.

Google’s Gemini powers the company’s AI product offering – including generative search – while AWS has invested heavily in AI tools and its own AWS Trainium chips.

Elsewhere, AI pair programmers such as Code Llama, Gemini Code Assist, or GitHub Copilot are making coding simple, offering code suggestions based on a company’s private codebase or making existing code more efficient. These tools can also analyze code to provide user-friendly explanations of the functions being served while producing comments or translating from one programming language into another.

Open-source AI models like OpenClaw have also grown in popularity. This also includes models from AI community Hugging Face, with its space rapidly approaching the sophistication of some proprietary ones. Experts have though questioned whether these models are truly open, as developers impose usage restrictions on some models in their licenses, e.g. prohibiting use by firms with huge numbers of users, without the express permission of the developers.

The latest flagship models of the tech giants for generative AI include OpenAI’s GPT-5.5, Google’s Gemini 3 Pro, Meta’s Llama 4, and Anthropic’s Claude Opus 4.6. These are multimodal, meaning they can process text, images, video, or audio as inputs and produce outputs in a variety of different formats.

What are the benefits of generative AI?

Part of the reason for generative AI’s popularity is its ease of use relative to its power and potential. Generative AI has opened the door to far more detailed responses to natural language inputs, with LLMs able to unpick meaning from user queries and provide informed responses on its own.

Through generative AI-powered chatbots and agentic commerce, businesses can provide customers with personalized answers based on questions they input to improve the overall digital experience of their websites, whether for customer service queries or personalised sales communication and help. Using generative AI, enterprises can also automate manual tasks such as drafting text, collating data across different sources, or identifying anomalous details in files or images.

Code generation is another major use case for generative AI, and this has massive applications for legacy codebases, which may be written in older languages like COBOL. Easy code translation saves companies time and money and prevents critical outages, as experts with firsthand experience of these old languages are now much harder to find.

More recent developments have allowed generative AI models to be used for tasks such as live video analysis through computer vision, which has applications for accessibility in tech or rolling out more autonomous robots in a manufacturing environment.

In manufacturing, generative AI now helps to produce better products by analysing vast pools of sales data or customer feedback, then suggesting new designs or improvements. It can also run computer modelling to enable retailers to understand how much stock to hold while offering factories instant alerts when a machine needs repairing; this is often before human workers have even seen a problem with their own eyes.

What are the concerns surrounding generative AI?

Widespread use of generative AI has been matched with concerns over its potential risks and harms for many years now. From the earliest models in public use, it has been clear AI has drawbacks such as ‘hallucinations’ – the term used to describe incorrect statements confidently reported by an LLM.

Hallucinations are just one problem with generative AI, however, with agentic AI and data sovereignty presenting new challenges.

Increases in regulatory oversight of AI by governments are also dominating their thoughts and discussions, much as GDPR’s role in data did. Politicians around the world have been playing catch-up, often making laws after the fact to restrict generative AI or to have its safety validated.

The UK government now has its own ‘Generative AI Framework’ [pdf] for use within its own internal departments to mitigate against any risks for the general public. This is critical given generative AI is now being integrated by the UK to help teachers manage and understand data about their pupils or schools, to assist the NHS with analysing scans or to type medical notes, and in making policing and defence more capable by introducing faster ways to identify fraud/crime risks or monitor enemies on satellite imagery.

The European Union has its own AI Act, too, which it describes as “the first comprehensive regulation on AI by a major regulator anywhere”. This has been dominated by thoughts and fears around the importance of ethical AI.

This is because one of the most basic concerns around generative AI models is who owns the data used to train them. Some developers are already facing lawsuits from artists, writers, and publishing houses over the alleged use of copyrighted material for training LLMs.

This is the tip of the iceberg for the legal issues of generative AI; governments around the world are still progressing AI legislation to control the risks and harms that AI could pose.

As of early 2026, the US still has no single comprehensive piece of AI legislation like there is in the EU. America’s AI Bill of Rights was drafted in 2022 as a framework for shaping future regulation, but conversations are still underway on a joined-up approach. This means the US lags behind the UK and Europe, although it has made headway with the National Policy Framework for Artificial Intelligence to prevent a patchwork State-by-State approach.

Elsewhere, specific US agencies have their own ways of dealing and regulating for AI while the TAKE IT DOWN Act of 2025 deals with deepfakes generated by AI.

Overall, it has become more critical than ever for businesses to know how their AI is being used and have a good understanding of what data is being processed to train the models they are using – otherwise they will face the potential for hefty fines.

What about generative AI job losses?

AI-linked job cuts are a major point of concern for employees, and the speed and sophistication of generative AI has fanned anxieties in this space. In February 2026, a report from Morgan Stanley suggested UK companies had reported net job losses of 8% linked to AI over the past year. A November 2025 study from Stanford University [pdf] used payroll data to show how employment among early-career workers in occupations that were highly exposed due to AI’s influence had fallen by 13%.

However, job losses due to generative AI are not a given. Leaders can pursue upskilling to prevent AI cuts, insulating workers with AI skills that will keep them in their roles for longer.

With more and more people having access on their smartphones and computers to generative AI tools, often free to use, experimentation is now more widespread in 2026. This is likely to enable some staff to use their DIY learnings to remain in employment.

However, there is still some resistance to generative AI’s use. According to the Pluralsight AI Skills Report 2025, published in August that year, 61% of people questioned believed using generative AI tools for work was “lazy”. However, attitudes like these are certain to change in a very short space of time, especially among younger generations now growing up with the technology in their lives.

How is governance and compliance linked to generative AI?

These are two big areas where organisations need to understand the risks of generative AI.

Hannah Mahon, Partner in the Employment, Labour, and Pensions Group at global law firm Eversheds Sutherland, explains 2026’s rise in the use of agentic AI - systems that can perform a series of tasks on their own – now means managers are no longer just responsible for the humans they employ.

On the flip side, AI cyber security is also used by defense teams to counter threats in more sophisticated ways. Tools are increasingly prevalent that can help identify and summarize threats or suggest actions to respond more effectively.

“Given agentic AI’s autonomous capabilities,” she says, “careful oversight will be required to limit legal risks such as discrimination and data privacy breaches. Employers must also understand and comply with new and emerging global AI laws, including the EU AI Act.”

This Act throws up the risks resulting from employees who don’t fully know how, why, and when they should be bringing generative AI into their daily tasks. “A real danger lies in employees ‘dabbling’ without understanding the implications for their employers,” Mahon adds. “Robust governance, employee training programmes, and usage policies setting clear guardrails for use will be absolutely critical.”

What are the security risks of generative AI?

Generative AI threats, those specifically linked to attackers misusing generative AI to launch more sophisticated attacks on victims, are on the rise and are now a major focal point for security teams as the technology has become widespread.

That is one reason why the TAKE IT DOWN Act was passed in the US because the risk of images or videos being created as a lifelike imitation of a person – often a celebrity, prominent business leader or leading politician – is high. These can trick others into believing the output is real.

Voice cloning tools can also reproduce realistic copies of people’s accents and tones using less than a minute of sample audio. These and deepfakes are powerful weapons in this new era of social engineering as criminals and bad faith actors continue to figure out how to use AI to enhance their attacks.

The EU AI Act seeks to regulate AI models – inclusive of generative AI – according to their assessed risk. Businesses will need to know how their AI is being used and have a good understanding of the data used to train the models they use, or face hefty fines. 

What comes next for generative AI?

Enterprise AI strategist, Maria Nugroho, who works now at SAP and formerly was at Google and AWS, suggests the “most meaningful shift” seen so far in 2026 is generative AI finally moving from isolated productivity tools into the “operational core of enterprises”.

Nugroho explains this is a few steps beyond simply having a chatbot on the side; instead, it has become fully integrated into workflows, internal engineering/development, and customer management. This speeds up and optimises how teams work and how many humans businesses may continue to need to operate with.

New risks and worries are also emerging though, she says, as laid out in the 2026 International AI Safety Report, backed by the OECD, EU, and UN.

“The most pressing risks no longer sit inside the models themselves,” Nugroho adds, ”but in the complex enterprise systems being built around them. When AI agents can trigger business processes, access sensitive data, and make autonomous decisions in ways their operators don’t fully understand, you’ve created a systemic risk that traditional cybersecurity controls were never designed to manage.”

What are the arguments against generative AI?

Not everyone is lauding generative AI, however. Leonid Derikyants, CEO and Co-Founder of Mind Simulation Lab, an independent AGI research entity based in Armenia, sees an “AI arms race” emerging now that won’t necessarily lead to better end products. “Businesses face three critical risks: Soaring costs, hallucinations, and the avalanche of ‘AI slop’,” he says.

High costs from fast-growing cloud bills that “can wipe out profit margins” are also a factor, Derikyants adds, while explaining how standard autoregressive LLMs mean hallucinations “remain mathematically inevitable”. He argues a single hallucination is a critical liability that could go on to cost a company millions in compensation or reputational damage.

“Thirdly, the internet is drowning in an insane volume of cheap generated content,” he says. “We’re already seeing that consumers are exhausted by synthetic ‘slop’. The era of raw generation is over; we believe the future belongs to professionals who seamlessly integrate AI deep into complex production pipelines rather than just churning out noise.”

What does the future hold for generative AI?

While it may be tempting to see generative AI as a malign force, given the early chaos it has sewn in the creative industries and across parts of the global economy, its popularity among everyday users is now clear and strong.

People now expect to have access to generative AI and so leaders of businesses and governments must accept this but continue to find ways to tame its potential for harm and also channel what’s good about it into productive use cases.

That said, many questions about generative AI do still exist and must be addressed on an ongoing basis, with regulation, safety, and security still remaining the hottest topics for discussion.

As generative AI continues to make companies more productive and profitable – and grows in emerging use among the millions who aren’t necessarily tech-savvy – there’s no doubt this is a technology which is here to stay, even if its use cases are still being defined.

SAP’s Maria Nugroho says: “In 2026, the strongest generative AI platforms are no longer judged on novelty alone; they are judged on workflow fit - which tool removes the most friction from the work a team repeats every week.”