Search across ServiceNow
By Workflow
Put AI to work for people
Put AI to work for people
Bring AI to every corner of your business with the Now Platform. Empower your teams with AI today.
Partner information
Partner information
Whether you need help from a ServiceNow partner or would like to become a partner, we offer options to fit your business.
K24 nav image
Knowledge is waiting for you
Discover the sessions, keynotes, and insight to strengthen your company's digital transformation strategies.
Grow your skills and RiseUp with ServiceNow
Rise up and join the digital revolution. Help fill the need for people with ServiceNow expertise.
Now Platform Dashboard

What is generative AI?

Generative AI is a type of artificial intelligence that creates realistic content by learning from existing data. Generative AI can provide realistic and creative text, images, and other media to transform industries such as entertainment, marketing, and healthcare.

Artificial intelligence (AI) as an idea has existed for thousands of years. Mechanical beings, living statues, and other artificial lifeforms can be found in mythology dating back to antiquity, and for nearly as long as there have been philosophers there have been philosophical musings about whether it would ever be possible to mimic human cognitive processes through mechanical (or digital) means.

Today, those musings have taken on greater significance. Modern AI is no longer confined to the realm of speculation; widely available and increasingly powerful, automation and AI are changing the way that people across all walks of life approach tasks, get information, and share ideas. At the forefront of this revolution is generative AI.

Generative AI is a form of artificially-intelligent computer system or algorithm capable of creating original content—quickly, effectively, realistically, and with some human oversight (to identify and eliminate inaccuracies). This can include anything from in-depth and highly accurate written articles to unique music and original images and is even capable of extensively altering the content of digital video clips.

As groundbreaking as generative AI is it is not an entirely new concept. It has a significant history, built on many of the foundational advances in automation technology that were pioneered during the 20th and early 21st centuries.

History of generative AI

The roots of generative AI can be traced back to the earliest advances in ‘machine learning,’ which was first introduced in the late 1950s. Attempts to create new data using algorithms started to make it possible for digital systems to do more than simply regurgitate the same information they had been fed. The Markov Chain, a statistical model with origins dating back to 1903, was one of the first examples of generative AI capable of creating new, unique sequences of data based on input.

Unfortunately, the lack of computational power and data resources throughout most of the 20th century hindered generative AI’s progress. It was only in the 1990s and 2000s, with the availability of advanced hardware and much larger pool of available digital data, that machine learning finally became a viable technology.

Generative AI as we know it today stems from the emergence of neural networks. These models process and learn from data through interconnected layers of ‘neurons.’ Neural networks can recognize patterns in a dataset and make decisions or predictions without explicit programming—much like the human brains these models are designed to mimic.

Woman reading Now Intelligence Ebook on phone

Introducing Now Intelligence

Find out how ServiceNow is taking AI and analytics out of the labs to transform the way enterprises work and accelerate digital transformation.

Given how closely tied the rise of generative AI is to the slow march of digital progress across the last century and a half, it is difficult to identify a single ‘creator’ responsible for introducing real generative AI to the world. That said, a few names stand out as those to whom generative AI owes its existence.

In addition to Russian Mathematician Andrey Andreyevich Markov (the brain behind the aforementioned Markov Chain), one of the earliest major contributors to today’s generative AI is Joseph Weizenbaum, the computer scientist who developed ELIZA. One of the first natural language processing computer systems ever created, ELIZA was developed during the 1960s. She and the generations of chatbots that would follow her had distinct limitations, but the promise of generative AI was there; it was simply waiting for technology to catch up.

The true birth of generative AI can in many ways be attributed to Ian Goodfellow and others who introduced the concept of Generative Adversarial Networks (GANs) in 2014. GANs revolutionized the field by introducing a framework where a generator network and a discriminator network interact and compete against each other—the generator network tries to produce content that the discriminator network classifies as real, while the discriminator network aims to correctly identify the generated content as fake. This adversarial relationship between the two networks drives them to learn, adapt, and improve.

With organizations such as OpenAI now playing a pivotal role in advancing generative AI, we are seeing even more significant breakthroughs in generating responses that are both coherent and contextually relevant. This advancement is only accelerating as more and more users embrace the possibilities of AI-generated content—both personally and professionally.

To reiterate, generative AI operates on the principle of machine learning through the application of neural networks, allowing the AI to generate new content from provided prompts. But before a prompt is ever introduced, the AI needs to be properly trained through detailed, extensive data sets.

Enormous amounts of relevant data are fed into the AI’s algorithms, and various AI engineers and machine learning specialists help the AI to correctly understand and classify the data. This data can consist of written text, graphics, images, code, or any other relevant content, and will become the foundation for the AI’s capacity to locate patterns and generate original work. The generative AI analyzes this information, extrapolates the underlying rules that govern the content, and continues to fine tune its parameters as new data is added.

The earliest versions of generative AI used complex data submission processes involving specialized tools, APIs (Application Programming Interfaces), coding languages, and extensive computer science training. But this is all changing; recent advances are focusing on improving the user experience. Rather than working within the constraints of specific systems, those who interact with today’s generative AI experiences can simply describe their requests using plain language. This approach creates a more conversational series of interactions with the AI, and even allows users to provide feedback to the AI on issues such as style, tone, and other elements which can then be applied in subsequent content iterations.

To most laypersons, the terms AI and generative AI may seem synonymous. Indeed, with the recent advent of widely available chat AIs, most AI interactions for the average user actually involve generative AI. So, what is AI, and how is it different from generative AI?

Artificial Intelligence is a broad term encompassing many different technologies, all of which facilitate or contribute to machines being able to perform tasks that typically require human intelligence. This includes a wide range of applications, such as natural language processing, computer vision, and decision-making algorithms, and can be applied to enhance task automation, data analytics and forecasting, cybersecurity threat identification and response, and more.

Generative AI, on the other hand, is a specialized branch of AI that focuses specifically on creating original and realistic content without direct human input. Generative AI goes beyond analyzing existing data and aims to create new content that resembles the patterns and characteristics of the training data it was exposed to. It leverages advanced machine learning techniques, such as neural networks, to generate original content that did not exist in the training data.

In other words, AI is a term that describes all forms of machine learning and intelligent automation, while generative AI is specifically focused on the creation of new content based on—but also capable of going beyond—the datasets it was provided with during its training phase. 

As with all forms of AI, generative AI is closely tied to machine learning. Machine learning algorithms serve as the foundation for training generative models in generative AI. 

These models learn patterns and features from large datasets, enabling them to generate new content that resembles (but is distinct from) the data they were trained on. Machine learning algorithms enable generative systems to learn the underlying patterns, styles, and distributions within the training data. This learned knowledge is then leveraged to generate new content that exhibits similar characteristics to the parameters it was originally provided. In other words, it allows it to create something that is completely artificial, but that looks, sounds, and feels as though it were made by a human.

Generative AI encompasses several types of content generation; producing text content requires a different approach than enhancing videos or creating realistic looking images. As such, each type of generative AI has its own unique characteristics and applications. These include:
Types of Generative AI

Text generation

Text generation involves training models to generate coherent and contextually relevant sentences or paragraphs. This has significant applications in natural language processing, content creation, and chatbot development where customers or users must be able to interact naturally with the AI as though it were a dedicated service agent. Models like OpenAI's GPT series have demonstrated the ability to generate human-like text and have found applications in writing assistance, storytelling, and language translation.

Image generation

Image generation focuses on creating visually realistic images, within parameters detailing things like color, style, and subject matter. GANs and other generative models have made significant strides in this domain, and can generate images that resemble objects, scenes, or even photo-realistic human faces. These models find applications in computer graphics, art generation, and visual content creation.

Video and speech generation

Although currently less widely used than text and image generation, generative AI has extended its capabilities to video and speech. From enhancing digital videos for clarity, to inserting or changing aspects of the video's subject matter, generative AI promises to change many processes associated with cinematography and video editing. Likewise, it is now possible to synthesize human-like speech, further expanding AI and taking virtual assistants outside of the chat box. 

Data augmentation

Data augmentation involves generating new and diverse data samples to enrich training datasets. Generative models can be used to create synthetic data that closely resembles real data, thereby increasing the diversity and volume of training examples for other AI models. This technique is beneficial in various domains, including computer vision, speech recognition, and natural language processing, where larger and more diverse datasets typically lead to improved performance.

The types of generative AI listed above are impressive, but they are not a complete representation of everything this technology can do. As previously stated, the field of generative AI is always expanding, and has already begun to change areas such as 3D modeling, music generation, and even coding.

Generative AI models are sophisticated algorithms, trained on vast amounts of data and designed to learn patterns within the data to generate new content. Three notable generative AI models that have recently gained public attention for their unique capabilities are:


ChatGPT, developed by OpenAI, is a conversational chatbot powered by a generative pre-trained transformer (GPT) model. It employs large-scale language models to generate near-instantaneous human-level responses to user queries. ChatGPT's strength lies in its ability to engage in interactive and contextually relevant conversations, making it useful for virtual assistance, customer support, and natural language understanding tasks.


Google is also exploring the new frontier of AI-powered conversational chatbots. Currently still in the experimental stage, Google Bard draws upon the expansive knowledge of the web to provide up-to-date, high-quality responses to user queries. Bard is fueled by the Language Model for Dialogue Applications (LaMDA) and serves as an outlet for creativity and an opportunity to find detailed, relevant information that meets the unique needs of essentially any user—simplifying complex topics and offering personalized insights built on the most recent information.

Bing Chat

Powered by Microsoft's generative AI, Bing Chat is designed to provide interactive and informative conversational experiences like those offered by other Chat AIs. It combines natural language understanding and generative capabilities to deliver personalized responses and assist users with queries, recommendations, and knowledge sharing. Possibly the biggest difference is that Bing Chat is built into the Microsoft Edge web browser, making it more integrated for Microsoft users.

While all three of these models fall under the umbrella of generative AI, they differ in their specialized focus and intended applications. ChatGPT excels in conversation and understanding natural language, Bard displays poetic creativity, and Bing Chat offers increased integration with certain Microsoft tools. Each model represents a unique approach to generative AI, catering to specific needs and domains.

At the risk of oversimplifying, it is not entirely incorrect to say that generative AI could seriously disrupt or even make obsolete the number of roles and responsibilities in business, or even majorly impact entire industries—driving innovation, streamlining processes, and transforming business operations, but also possibly forcing certain organizations to adapt to changing expectations.

But most experts believe that the positive outcomes of generative AI outweigh the dangers. And while we do not yet have enough data to accurately predict which businesses are likely to experience the greatest changes, the following industries appear most likely to be enhanced by generative AI:

  • Aerospace
    Generative AI is capable of enhancing the effectiveness of flight simulations, optimizing aerodynamics and fuel efficiency, and allowing for improved aircraft designs.

  • Architecture
    Giving architects the power to create detailed structural analysis while also generating innovative design options to meet specific criteria, generative AI has the potential to change the way architecture works with technology.

  • Automotive
    Vehicle components demand extensive design and testing to ensure improved performance while also reducing weight (to improve fuel efficiency). Generative AI aids in both processes, enhancing safety and reducing costs.

  • Consumer marketing
    Generative AI can analyze customer behavior and generating personalized marketing efforts and recommendations based on past behavior, preferences, and other relevant data. This allows for target advertising and promotes increased customer engagement.

  • Defense
    As with the aerospace and automotive industries, generative AI may play a key role in the designing and testing of components of new defense technologies.

  • Education
    Every student is different; generative AI can help educators create personal learning experiences that play to those differences, improving student engagement and helping ensure that everyone has access to a teaching curriculum that meets their needs.

  • Electronics 
    Generative AI can be applied to circuit design within the electronics industry. Incorporating learnings from previous designs, generative AI can develop new advanced systems while also enhancing efficiency.

  • Energy
    With increasing energy demands world-wide, the energy industry is extremely interested in generative AI to help improve grid management and energy-use forecasting.

  • Engineering
    Reducing material waste, enhancing energy efficiency, improving the quality of products both physical and digital—the ability to generate and test designs can greatly cut down on the engineering timelines and assist engineers in producing better designs.

  • Entertainment
    From enhancing visual effects in film and television to creating more-interactive storytelling in video gaming, generative AI may completely change the entertainment industry.

  • Finance
    Generative AI has the potential to enhance the finance industry on both sides of the counter, informing risk assessment, improving fraud detection, and generating personalized investment recommendations. This will optimize financial modeling while also helping realign focus on improving customer experiences.

  • Healthcare
    The increasing need for dedicated healthcare services is outpacing the number of trained medical personnel. Generative AI allows doctors to do more and help a larger number of people, providing assistance in personalizing treatment plans and creating accurate diagnosis. Additionally, generative AI can improve other areas such as medical image analysis.

  • Manufacturing
    The manufacturing industry is always looking for opportunities to reduce costs and energy demands without hurting product quality or output. Generative AI can help redesign and optimize processes for optimal cost per unit manufactured, while still emphasizing quality control. In addition to supporting the development of advanced manufacturing techniques, generative AI can be applied to creating more effective safety education programs.

  • Pharmaceuticals
    Generative AI is effective in drug discovery and virtual compound screening. This makes it possible to accelerate the research, development, testing, and deployment of pharmaceutical solutions.

With its ability to create intelligent, accurate, original content without any ‘hand holding’ from human operators, generative AI offers numerous benefits for businesses across a range of sectors. By leveraging the creative power of AI, generative models have the potential to drive innovation, streamline processes, and unlock new opportunities. Key business benefits of generative AI include:

Increased opportunities in cost and productivity

Generative AI can optimize processes, automate tasks, and reduce operational costs, enhancing efficiency in areas such as content creation, design optimization, or predictive maintenance. By automating repetitive or time-consuming tasks that once demanded creative input, businesses can save resources, increase productivity, and allocate valuable professionals to more strategic endeavors.

Improved customer service and support

Traditionally, customer service and support relied on direct 1:1 interaction, with agents needing to devote their entire attention to resolving individual customer issues. Generative AI allows businesses to deploy AI-powered chatbots and virtual assistants, capable of providing human-level support at scale. These AI-powered agents can assist with queries, offer solutions, and even discuss purchasing options, enhancing customer satisfaction and loyalty. By automating customer service, businesses can reduce costs, improve overall customer experiences, and allow their trained human agents to accomplish more in less time.

Targeted marketing

Generating personalized content, creating targeted advertising campaigns, and sharing optimized product recommendations based on customer preferences and behavior—generative AI offers significant benefits in marketing. By delivering marketing strategies customized to individual buyers, organizations enjoy more customer engagement, improved conversion rates, and stronger brand loyalty. 

Enhanced revenue

Each of the advantages listed above comes back to this one benefit: increasing company revenue. Generative AI opens new opportunities, allows companies to create advanced products and services at lower cost, expands customer support and service capabilities, and gives organizations better insight into the risks that threaten their business—all of which contribute to greater financial success. 

Obviously, generative AI carries with it immense potential for optimizing and streamlining business processes and personalizing customer interactions. However, it is worth recognizing that generative AI has its share of risk and limitations. Here are some potential issues associated with generative AI that organizations need to be aware of:

  • Difficulty adapting to new circumstances
    Generative AI models may struggle to tune their outputs to new circumstances, requiring ongoing fine-tuning and adjustment to ensure relevance and accuracy.

  • Harmful bias
    Because generative AI models are built on a foundation of data, they may carry certain biases from that training data across into their outputs. Organizations must have policies or controls in place to detect and address biased content so as not to inadvertently spread harmful preconceptions.

  • Intellectual property rights
    As advanced as today’s generative AIs are, they still require training on available data to produce content. This creates murky waters when it comes to copyrighted materials—these technologies AIs may be using intellectual property that does not belong to their parent organization. At the same time, users who share confidential information with AIs may find that it is no longer confidential but has become part of the AI data set.

  • Lack of transparency
    The recent focus on ease of use and conversation-like input/output has created an environment where it can be difficult to really understand how the generative AI works and where its original data comes from. Many organizations may not wish to take full advantage of generative AI without a clearer picture of its inner workings.

  • New cybersecurity dangers
    Cybercriminals around the world are already using generative AI programs to target their victims and circumvent security layers more effectively. Attackers may even use company-based AIs as system entry points, taking advantage of vulnerabilities in the program to gain access to sensitive data. Keeping up to date on all cybersecurity advancements and vulnerability patches may be the only way to counter these growing threats.

  • Potential problems with accuracy and appropriateness
    Generative AI systems may generate inaccurate or unproven answers. Generative AI ‘hallucinations,’ where the AI may simply make up answers, represent major issues, particularly in scenarios that demand complete accuracy and validity. It is crucial to carefully assess the outputs for accuracy, appropriateness, and actual usefulness before relying on or publicly distributing any information produced by a generative AI.

When incorporating generative AI into business operations, it is essential to adopt best practices that ensure responsible and effective utilization. By following these guidelines, organizations can maximize the benefits of generative AI while mitigating risks and challenges:

  • Become familiar with common failure modes and workarounds
    Generative AI models can exhibit failure modes where the output may introduce errors or otherwise fail to meet expectations. Study the common failure modes associated with your chosen generative AI tool and develop strategies to work around these issues. This can involve refining input prompts, adjusting parameters, or employing post-processing techniques to improve the quality of the generated content.

  • Be ethical and comply with legal requirements
    When in doubt, err on the side of ethics and legality. Comply with any regulations, privacy laws, or intellectual property rights relevant to your situation. Implement measures to protect sensitive data and user privacy, and regularly assess the ethical implications of the generated content.

  • Be transparent about what content is AI-generated
    To maintain transparency and ensure users are aware that they are interacting with AI-generated content, clearly label all output created by generative AI systems. This helps manage expectations and fosters trust with users and consumers.

  • Double check for accuracy
    When the accuracy of information is critical, verify and cross-reference the output of generative AI models with primary sources. This helps ensure the reliability and integrity of the generated content, particularly in industries where accuracy is essential (such as law or healthcare).

  • Stay up to date
    Generative AI technology is constantly evolving. Keep up with advancements, research, and best practices in the field and continuously monitor the performance and outputs of the generative AI models. Iterate on these models and processes as needed to enhance their effectiveness and address any emerging challenges.

  • Watch out for biases
    As has already been addressed, generative AI models can inadvertently perpetuate biases present in the training data. Be vigilant in identifying and addressing any preconceptions or prejudices that may emerge in the generated content; regularly evaluate the output for potential biases and implement measures to mitigate their impact.

Although generative AI has been around for decades, its recent rise in terms of capability and availability has been meteoric. As this technology continues to evolve, we can anticipate enhanced creativity and innovation across essentially every industry, allowing for more sophisticated and realistic outputs in content generation, design, music composition, visual arts, and more. Generative AI will also continue to drive personalization and customization, creating laser-focused recommendations, personalized experiences, and unique products designed to address individual customer needs.

Within the next ten years, we can expect multimodal capabilities to emerge. This integration of models from various domains will lead to more immersive and interactive outputs, transforming areas like virtual reality, augmented reality, and mixed reality experiences, to name only a few. But this expansion won’t be made without caution; ethical considerations will take center stage, focusing on addressing biases, improving transparency, and ensuring responsible use of generative AI.

Additionally, the processes that support generative AI itself will become more streamlined, eventually allowing these AIs to continuously learn, adapt in real time, and become more truly ‘intelligent.’ This adaptability will make generative AI more valuable in dynamic environments and foster human-machine collaboration. While challenges such as bias detection and ethical considerations must be addressed, the future of generative AI is promising.

Generative AI holds immense power and potential, enabling organizations to enhance productivity, streamline workflows, and create exceptional customer experiences. However, it is essential to approach the use of AI-generated content responsibly, considering the risks and challenges involved. To assist organizations in effectively harnessing the benefits of generative AI, ServiceNow has introduced the Generative AI Controller and Now Assist for Search. These tools seamlessly integrate generative AI capabilities into the Now Platform®, empowering users to leverage the power of AI without the need for platform changes or complex integrations.

With the Generative AI Controller, organizations can connect the Now Platform to leading large language models (LLMs) such as OpenAI, Azure, and ServiceNow’s proprietary LLM. These integrations allow for the seamless utilization of generative AI capabilities within existing workflows, enabling enhanced search functionalities, personalized conversations, and improved user experiences. And, Now Assist for Search (powered by generative AI), enhances search capabilities further by providing more specific and direct answers to queries, ensuring users receive the information they need quickly and accurately, filling gaps in customer experience, and delivering smoother interactions.

Additionally, ServiceNow recently partnered with Nvidia to develop more extensive generative AI capabilities for enterprise business, expanding ServiceNow's AI functionality and offering new applications for generative AI for IT departments, customer service teams, employees, and developers, and more.

Generative AI represents major opportunities for enterprise business, but also carries some risk. Click here to learn more about how ServiceNow's generative AI capabilities can transform your organization, while helping ensure that you don’t fall prey to the potential dangers. Put generative AI to work for your business, on your terms, and take your organization further than ever before.

Dive deeper into generative AI

Accelerate productivity with Now Assist – generative AI built right into the Now Platform.
Loading spinner