Generative AI uses machine learning (ML) models, often built with neural networks, to learn patterns and relationships within training data. That training data may include text, images, code, audio, or other relevant information, depending on what the model is designed to do. During training, the model adjusts its internal parameters as it learns statistical relationships within the data. Once trained, a model can use those patterns to generate a new output in response to a user prompt or other input.
Many current generative AI tools make this process accessible through natural-language prompts rather than specialized programming interfaces. Users can describe what they need, review the result, and refine subsequent prompts with additional context or feedback.
Generative AI can create or transform several forms of content and data. Different models are designed around different input and output types. Multimodal systems also exist that work across more than one format.
Generative AI is one part of the broader artificial intelligence (AI) landscape. Its defining purpose is generating new content from learned patterns, while other AI approaches may focus on prediction, classification, conversation, learning from data, or autonomous action. These categories can overlap. For example, a conversational or agentic AI system may incorporate both generative AI and machine learning as part of the same experience.
Several widely used generative AI systems are built on large language model (LLM) types that can interpret prompts, generate content, and support increasingly complex tasks:
Conversational AI assistants use generative models to answer questions, generate and analyze content, support coding, and hold interactive, contextually relevant conversations. They are commonly used for virtual assistance, customer support, and other tasks that call for natural language understanding.
Some generative AI systems are designed for advanced reasoning, long-context processing, and careful handling of complex or sensitive tasks. These models are well suited to analyzing large documents, summarizing complex information, and working through detailed, multistep reasoning problems.
Multimodal generative AI systems can process and generate more than one type of content — text, images, code, and other data — within a single system, allowing them to work across different forms of information in the same task or interaction.
Hybrid AI combines more than one AI approach within a system or workflow. For example, predictive models can estimate likely outcomes, generative models can create or summarize content, and rules-based systems can apply defined business logic, all within the same hybrid generative AI system.
Key benefits of hybrid AI include:
1. Greater flexibility across use cases
Different AI techniques can be matched to the work they are best suited to perform.
2. More controlled decision-making
Rules, predictive signals, or other controls can complement generative outputs where greater consistency is required.
3. Stronger workflow integration
Multiple AI capabilities can support different stages of a business process instead of requiring one model to perform every task.
ChatGPT
ChatGPT, developed by OpenAI, is a conversational AI assistant built on GPT models. It can generate and analyze content, answer questions, support coding, and assist with a broad range of language-based tasks. ChatGPT's strength lies in its ability to engage in interactive and contextually relevant conversations, making it useful for virtual assistance, customer support, and other tasks that require natural language understanding.
Claude
Claude, developed by Anthropic, is a family of large language models designed for advanced reasoning, long-context processing, and safe AI interactions. Claude is useful for supporting tasks such as analyzing large documents, summarizing complex information, generating content, and working through detailed reasoning problems.
Gemini
Gemini, developed by Google, is a multimodal AI model capable of processing and generating text, images, code, and other data types within a single system. Its multimodal capabilities allow it to work across different forms of information within the same task or interaction.
- Aerospace and defense
Generative AI enhances simulation, design optimization, and testing processes for complex systems, improving efficiency and performance across both commercial and defense applications. Companies such as Boeing, Lockheed Martin, and Airbus are exploring generative design and simulation tools to help reduce development cycles. - 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. Automakers like Tesla, BMW, and Ford are applying generative AI to design optimization and autonomous systems. - Consumer marketing
Generative AI can analyze customer behavior and generate personalized marketing efforts and recommendations based on past behavior, preferences, and other relevant data. This allows for targeted advertising and promotes increased customer engagement. Organizations such as Coca-Cola, Netflix, and Amazon are using generative AI for personalized content and recommendations. - Healthcare
The increasing need for dedicated healthcare services is outpacing the number of trained medical personnel. Generative AI makes it possible for 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. Healthcare leaders, including Mayo Clinic, Pfizer, and Siemens Healthineers, are exploring generative AI for diagnostics and drug discovery. - Manufacturing
Generative AI can be applied to circuit design within the electronics industry. Incorporating learnings from previous designs, generative AI is able to develop new advanced systems while also enhancing efficiency. Companies like Siemens, General Electric, and Bosch are implementing generative AI for design and process optimization. - 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 optimizes financial modeling while also helping realign focus on improving customer experiences. Financial institutions such as JPMorgan Chase, Goldman Sachs, and Morgan Stanley are deploying generative AI to improve fraud detection and automate analysis. - Pharmaceutical
Generative AI is effective in drug discovery and virtual compound screening. This makes it possible to accelerate the research, development, testing, and deployment of new drugs and treatments. Organizations like Moderna, Novartis, and Roche are using generative AI to accelerate drug discovery.
In a recent survey by Stanford University, 79% of organizations already using AI reported using generative AI regularly in at least one business function in 2025. With its ability to create original content with minimal oversight from people, generative AI offers numerous benefits across a range of sectors. Even so, there are certain challenges and risks that organizations should be aware of when incorporating generative AI systems.
- Productivity and cost efficiency
Generative AI can automate or accelerate repetitive, content-intensive work, giving employees more time for tasks that require judgment or specialized expertise.
- Customer service and support
Drafting responses, summarizing cases, and providing conversational assistance give service teams faster access to relevant information and helps reduce repetitive work associated with customer service and support.
- More personalized marketing
Generative AI can produce content tailored to different audiences or contexts using relevant customer, product, or campaign information. Real-time translation capabilities also allow businesses to expand their markets to include new regions or demographics.
- New products and revenue opportunities
Faster content and design cycles can support experimentation with new digital experiences, services, or AI-enabled offerings, although business results depend on how those capabilities are implemented.
- Difficulty adapting to new circumstances
A model may produce weaker results when it encounters situations, data, or terminology that differ significantly from what it was trained to handle. Businesses may be able to counter this by testing models against representative and edge-case scenarios, providing current domain-specific context through retrieval or approved knowledge sources. High-risk requests should be routed to experts.
- 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 revise biased content so as not to inadvertently spread harmful preconceptions.
- Intellectual property and data privacy
Training data, generated content, and information entered by users can create copyright, confidentiality, or privacy concerns. Establishing clear policies for what data may be shared with AI systems and how generated material can be used helps prevent data privacy from being violated and information from being misused.
- Lack of transparency
The complexity of many generative AI models can make it difficult to determine why a particular output was produced or which information influenced it. This ‘black box’ element of AI can complicate governance. Organizations can address this with documented data sources, audit trails, and review from people, especially in regulated environments.
- Cybersecurity risks
Generative AI can introduce new attack surfaces and can also be used by threat actors to create more convincing malicious content. AI systems should be included in an organization's broader security, access-control, and vulnerability-management practices.
- Accuracy and appropriateness
Generative AI can produce incorrect, unsupported, or misleading information, sometimes described as hallucinations. Outputs should be reviewed according to the level of risk associated with the task, particularly before they are used in high-stakes decisions or distributed externally.
When incorporating generative AI into business operations, adopting best practices can ensure responsible and effective utilization. By following these guidelines, organizations can maximize the benefits of generative AI while mitigating risks and challenges:
- Define the use case and success criteria
Identify the specific task the AI should support, who will use it, and how performance will be measured before deployment. - Establish ownership and governance
Assign responsibility for model selection, data access, approvals, escalation paths, and ongoing oversight so accountability is clear throughout the AI lifecycle. - Test before scaling
Evaluate the system with representative business scenarios, including edge cases, and document where review or intervention from people is required. - Integrate AI into existing processes
Determine where generative AI should assist employees, automate a step, or hand work to another system or person rather than treating it as a standalone tool. - Monitor performance over time
Track measures such as output quality, adoption, task completion, and exceptions, then adjust models, prompts, controls, or processes as requirements change. - Implementing generative AI
Adopting generative AI calls for a structured approach that aligns technology with business priorities, data readiness, and workforce capabilities. Consider starting with a small number of well-defined use cases, then assess whether the required data is accurate, accessible, and appropriately governed. Evaluate the infrastructure, integrations, security requirements, and employee skills needed to support broader deployment.
- Initial deployment and validation
Organizations launch pilot projects focused on specific use cases, such as automating internal documentation or enhancing search. These controlled deployments help validate performance and identify gaps early on. - Expansion and integration
Once initial use cases prove effective, AI capabilities are integrated into broader workflows and systems. This phase often involves connecting AI tools to enterprise platforms and refining outputs based on user feedback. - Scaling and optimization
At scale, organizations focus on performance measurement, governance, and continuous improvement. Metrics such as time savings, accuracy, and adoption rates help determine where to expand further and where to adjust.
We bring generative AI into enterprise workflows through the ServiceNow AI Platform, with capabilities for generating content, connecting external models, interacting conversationally, and taking action across integrated systems.
- ServiceNow AI Platform connects AI with enterprise data, business processes, and governance, giving generative AI access to the context needed to support work across the organization.
- Generative AI Controller connects supported third-party LLMs with ServiceNow, allowing businesses to build custom generative AI use cases with external models.
- ServiceNow AI Agents extend generative AI from creating and interpreting information to taking action. AI agents can use generative AI skills alongside other tools and systems to reason through requests and autonomously execute tasks.
- ServiceNow Otto brings Now Assist, AI agents, and ServiceNow capabilities into a conversational experience, allowing users to make requests in natural language and initiate work across systems and departments. Across the ServiceNow AI Platform, these generative AI capabilities run on top of 100 billion workflows executed annually across 85% of the Fortune 500.
Generative AI represents major opportunities for enterprise businesses. Explore ServiceNow's generative AI capabilities to see how they can help your organization capture that value while managing the risks responsibly.
Demo ServiceNow today, and put generative AI to work for your business.