This article was originally published in April 2023. It has been refreshed with new information.
What started as industry curiosity has now become mainstream adoption. As of June 2026, G2 tracks generative AI tools across 15 dedicated subcategories - from foundational Large Language Models to AI Agents, AI Coding Assistants, and Generative AI Infrastructure - reflecting how thoroughly this technology has embedded itself across business functions.
Generative AI software has taken the world by storm, transforming how we create, consume, and interact with various forms of media. In this blog post, we'll dive into the different flavors of generative AI, including synthetic media, which includes image, video, text, and audio generation. We'll also discuss large language models (LLMs) and diffusion models, which are key components of generative AI technologies.
Generative AI is a subset of artificial intelligence that can create new content based on training data. Generative AI is revolutionizing business, creating new value in sales, marketing, and other parts of the company, in every industry. As of June 2026, G2's Generative AI category spans tools across 15 subcategories, with ChatGPT accumulating more than 2,645 verified reviews across small businesses (1,415 reviews), mid-market teams (706), and enterprise organizations (319). Creating audio for voiceovers and producing text and images for marketing campaigns are just a couple of examples of how generative AI has revolutionized content creation.
As companies move from curiosity to practical adoption, the state of generative AI in the workplace helps show how teams are using these tools today, where they’re seeing value, and what questions still remain around policy, productivity, and responsible use.
For instance, startups to enterprise businesses can harness the power of generative AI APIs to develop innovative applications, ranging from personalized marketing campaigns to virtual reality experiences. The potential of generative AI is vast, and as technology advances, we can expect it to play an even more significant role in shaping our digital landscape.
Industry-specific applications are also growing quickly, with generative AI in fintech helping businesses automate support, personalize customer interactions, and improve how financial services teams respond to complex user needs.
As businesses explore these possibilities, comparing generative AI tools can help them identify which platforms best support their goals, whether they need text generation, image creation, coding assistance, workflow automation, or customer-facing AI experiences.
Synthetic media encompasses any AI-generated media, including images, videos, texts, and audio. Some popular AI-powered tools in G2’s Synthetic Media category include AI generative art tools, photo generators, and drawing generators.
As businesses adopt synthetic media tools, they should also keep legal considerations for using generative AI in mind, especially around content ownership, data privacy, moderation, and how AI-generated outputs are reviewed before publication.
To qualify for inclusion in the Synthetic Media category, a product must:
Synthetic media types, encompassing images, videos, texts, and audio, offer a wide range of uses and applications across various industries. Some of the most common use cases for each media type include the following:
AI-generated text finds its use in content creation, producing blog posts, news articles, and social media content to help businesses and individuals maintain a consistent online presence.
This text can be generated using standalone platforms like ChatGPT, or applications with models like GPT-4o, Claude, Gemini, or Llama built in.
On G2, ChatGPT earns 4.6 out of 5 stars across 2,645 verified reviews as of June 2026. Enterprise users - those at companies with 1,000+ employees - account for 319 of those reviews, with more than 80% rating it 5 stars. One verified enterprise reviewer, based in the UK, described it this way: "Tasks that would previously take hours can often be completed in minutes. I can focus more of my time on building client relationships, driving revenue, and delivering commercial results
Customer support benefits from AI-generated text through chatbots and virtual assistants that provide automated support, improving response times and customer satisfaction.
This is where generative AI in customer service becomes especially relevant: the same text-generation capabilities that power chatbots can help teams answer recurring questions, summarize conversations, and give agents faster access to useful customer context.
AI-generated text is also employed in real-time translation tools, breaking language barriers and facilitating global communication. Creative writers and authors can use AI-generated text as a valuable tool for inspiration, plot suggestions, and even entire manuscripts.
For travel companies, those language and support capabilities are especially useful when customers need quick help with bookings, cancellations, policy questions, or itinerary changes. That makes generative AI in travel support a natural extension of AI-generated text and multilingual assistance.
AI-generated images, which can be produced by tools like Midjourney and DALL·E 2, have applications in advertising where they can create visually striking and personalized advertisements for digital and print media. Artists and designers can use AI-generated images for innovative art pieces, blending traditional methods with cutting-edge technology. In gaming, developers can create realistic and immersive virtual environments, characters, and objects with AI-generated images. The fashion industry can also benefit from AI-generated images, using them to visualize new designs, fabrics, and patterns for rapid prototyping and iteration.
On G2, Midjourney holds a 4.4/5 star rating (8.8/10) across 100 verified reviews as of June 2026, with users concentrated in design, graphic design, and marketing. One verified reviewer noted: "It helps my design work look polished, high-end, and more expensive.
AI-generated videos can play a significant role in film and TV production, creating realistic visual effects, virtual sets, and even entire animated films, thus reducing production costs and time. In marketing, AI-generated videos enable the creation of personalized promotional content tailored to individual customer preferences and demographics. AI-generated educational videos cater to students' unique needs and learning styles by offering customized learning materials.
AI-generated audio has various applications, including music production, where it can create unique compositions and explore new genres and styles. Podcasts and audiobooks benefit from AI-generated audio, producing high-quality, natural-sounding voiceovers for narrations. Voice assistants rely on AI-generated audio to understand and respond to user queries.
Generative AI is also revolutionizing the world of code development and creation. AI-powered tools, such as code completion assistants and automatic error detection systems, streamline the software development process, making it more efficient and accessible. By leveraging AI-generated code snippets and providing real-time suggestions, these tools help developers write cleaner and more efficient code and enable individuals with limited coding experience to participate in software development. The impact of generative AI on code development is poised to democratize access to technology and foster innovation in the software industry.
G2 users back this up. GitHub Copilot earns 4.5/5 stars across 349 verified reviews, with developers citing reduced context switching as its primary benefit. One reviewer put it simply: "Copilot keeps me in the zone by bringing those quick syntax answers directly into my IDE... By the end of the day my brain is noticeably less fatigued because I'm spending my mental energy on architecture and business logic - not memorizing syntax."
It is important to note that this category is only the beginning. Generative AI cuts across various categories, supercharging content creation for sales, marketing, human resources, biotech, IT operations, and more. For instance, generative AI in ITSM can help teams automate service requests, improve incident response, and make enterprise knowledge easier to access.
The technology behind generative AI is advancing fast with new technologies and methods such as large language models (LLMs) and diffusion models cropping up and making splashes, allowing creators to develop applications and create content quickly and efficiently.
LLMs are artificial intelligence models trained on vast amounts of text data to understand and generate human-like text. These models, such as GPT-4 by OpenAI, can generate coherent and contextually relevant text based on user input.
The primary goal of LLMs is to create AI text generators that can understand and respond to natural language queries with human-like proficiency. LLMs have been used to develop chatbots, generate news articles, and even write entire novels.
According to G2's Large Language Models category, ChatGPT and Claude lead with 4.6 star ratings (2,645 and 350 reviews respectively), followed by Gemini at 4.4 and Llama at 4.3 as of June 2026.
Diffusion models are a recent development in generative AI that focuses on creating realistic images, videos, and audio by simulating a diffusion process. Instead of relying on traditional generative techniques like generative adversarial networks (GANs), diffusion models use a denoising process to generate high-quality synthetic media.
| Note: Generative adversarial networks (GANs) create realistic images, videos, or audio using a unique "competition" between two AI components. One AI, called the generator, creates fake content, while the other, called the discriminator, tries to tell if the content is real or fake. They improve together, with the generator becoming better at creating convincing media and the discriminator becoming better at detecting fakes. This back-and-forth process continues until the generator produces highly realistic synthetic content. |
Diffusion models have shown great potential in generating AI art, with some AI-generated images being virtually indistinguishable from photographs taken by humans. As these models continue to develop, we can expect more realistic and higher-quality synthetic media in the near future.
Generative AI has opened up new possibilities for creativity and innovation across every industry. As we continue to explore the potential of technologies like LLMs and diffusion models, we can expect to see even more groundbreaking applications in the world of synthetic media. The data backs it up - the Generative AI parent category spans 15 subcategories and hundreds of products with tens of thousands of verified reviews - a market that has moved from prediction to mainstream adoption.
As adoption expands, the future of work with generative AI will be defined by how teams use these tools to automate repetitive tasks, support creative work, and make everyday business processes more efficient.
AI is moving fast - see how the tools stack up in G2's AI Coding Assistants on G2.