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Introducing Content Cloud + Generative AI

Generative AI at Sitecore

The power of Content Cloud meets the revolutionary capabilities of generative AI, empowering editors, writers, and content creators.


Content creation, reimagined

Leverage the power of generative AI with Sitecore Content Cloud to transform your content strategy and tell more powerful stories.

  • Editorial automation

    Transform your editorial process with generative AI. Empower content creators with intelligent suggestions to enhance the quality of your content and automate time-consuming tasks.

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  • Brand voice consistency

    Integrate your brand's voice, personality, and values into every piece of content you create. By integrating generative AI into Content Cloud, you can help ensure your content be easily adapted to your brand voice, tone, and style

  • AI tool integrations

    Streamline integrations between Sitecore products and other AI connectors to harness GPT-4 and Dall-E from OpenAI. Enable employees to build and interact with LLM-powered automations - all within your CMS.

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The evolution of headless CMS

Generative AI in action

See how generative AI enables effortless content creation and enhancement in Sitecore Content Hub ONE.

Watch demo
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Innovating for the future

"Kudos to Sitecore for demonstrating one of the main value propositions of leveraging a composable, API-first product ecosystem."

Adam Wolf, CTO of Wunderman Thompson, and seven-time Sitecore MVP

Frequently asked questions

  • What is generative AI?

    Generative AI is a type of artificial intelligence (AI technology) that often uses generative adversarial networks (GANs) to create realistic and original content. Generative AI using GANs involves the use of two neural networks, known as the generator and the discriminator, which work together in a competitive manner. 

    The generator network is tasked with creating new data, such as new content, images, or music, based on patterns it has learned from a given dataset during the training phase. The discriminator network acts as a judge and tries to differentiate between the generated data and real data from the training set. As the training progresses, both the generator and discriminator become more skilled. The generator learns to produce data that increasingly resembles the real training data, while the discriminator becomes better at distinguishing real data from the generated samples. 

  • What is an example of generative AI?

    Popular examples of generative AI include OpenAI’s ChatGPT, including GPT-3 and GPT-4, and Google’s Bard, a chatbot based on the LaMDA family of large language models (LLMs). Other providers include DALL-E, Midjourney, DeepMind, the open-source provider HuggingFace, and  open-source deep learning framework Apache MXNet.

  • How can generative AI be applied?

    Generative AI has applications in various modalities, including image synthesis, video generation, prompt engineering, and music composition. Generative AI models and AI applications have shown remarkable potential, and use cases are being explored across healthcare and medical imaging, software development, data augmentation, content creation, image generation, and technology startups.
  • What are the advantages of generative AI?

    Like other AI technologies, generative AI can increase productivity by automating tasks and streamlining workflows. It can enable the faster creation of new content and assist with the optimization of existing content; it can also facilitate deep learning and the analysis of data sets that are both large and complex, which is especially helpful in fields like healthcare.  
  • What are the disadvantages of generative AI?

    Generative AI is a tool, and as such has limitations. Like other AI tools, generative AI is only as good as the data it is trained on and there is enormous potential for bias in the training data; this can and has led to biased and discriminatory outputs. Generative AI is like other AI systems in that it is resource intensive and can be cost prohibitive. 

    There are also ethical concerns. Generative AI can be used to create deepfakes and other unethical content, and AI-generated content may infringe on the intellectual property and/or copyright of other works if the AI had access to the copyrighted material and produced a substantially similar output. 
  • What is the difference between predictive and generative AI?

    The foundation models of the two are the same: both use machine learning models. While generative AI turns machine learning inputs into content, predictive AI uses machine learning to predict outcomes.

A flexible tech stack can help reap the benefits of generative AI

According to a recent survey, generative AI is seen as a pivotal, game-changing advancement for brand marketers searching for solutions that can deliver rapid-fire personalization to customers expecting intuitive, engaging digital experiences.


of respondents list AI among their top martech investment priorities.


are planning to move at least some of their marketing tech stack to composable software for flexibility, speed, and cost efficiency.

More AI resources

Dive deeper into artificial intelligence

AI and the Future of Content with Ann Handley

Watch the webinar

10 ways generative AI can elevate your ecommerce

Read the blog

AI and Sitecore Content Hub with Horizontal Digital

Watch the webinar


Sitecore's AI-powered content discovery solution

By implementing our own AI-driven solution, Sitecore Search, we are increasing the level of contextual relevance dramatically for our site visitors. The Sitecore website can now provide better answers more quickly through our seamless search experience.

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Sitecore Search