* [Topics](/en/topics "Topics") * [Artificial intelligence](/en/topics/ai "Artificial intelligence") * What is enterprise AI? What is enterprise AI? ====================== Updated April 14, 2026•*7*-minute read Copy URL Jump to section --------------- What is enterprise AI?Why is enterprise AI important?Benefits of enterprise AI Risks of enterprise AI Challenges of enterprise AIvLLMWhat is an enterprise AI technology stack? Strategies for adopting enterprise AI How Red Hat can help What is enterprise AI? ---------------------- Enterprise AI is the integration of artificial intelligence (AI) tools and [machine learning](/en/topics/ai/what-is-machine-learning) software into large scale operations and processes. In almost every industry, organizations are adopting AI technologies to improve efficiency, and get more work done with the people and resources they have available now. Enterprises in particular need AI solutions that will work on a larger scale, across their different teams and workloads. [Explore Red Hat AI](/en/products/ai) Industries such as [healthcare](/en/topics/ai/what-is-ai-in-healthcare), [telecommunications](/en/topics/ai/understanding-ai-in-telecommunications), and [banking](/en/topics/ai/ai-in-banking) are using AI to get a competitive edge in their industry. Whether the goal is to streamline finances, improve customer experience, or simply work more efficiently. Enterprises are quickly learning how to apply both [generative AI and predictive AI](/en/topics/ai/predictive-ai-vs-generative-ai) to everyday tasks and complex, long-term problem solving. [Take a look at Red Hat AI customer stories](/en/success-stories) Why is enterprise AI important? ------------------------------- Enterprise AI creates opportunities to think about business differently. Now, businesses can solve problems in weeks rather than years. The speed and accuracy of AI technology allows large companies to sort through massive amounts of data and experiment with new business ideas quickly and confidently. It’s helpful to understand what those opportunities could look like, how they can help your business, and how to overcome the challenges AI can bring along the way. This video can't play due to privacy settings To change your settings, select the "Cookie Preferences" link in the footer and opt in to "Advertising Cookies or try disabling adblockers." It's also important to understand that [inference](/en/topics/ai/what-is-ai-inference) is equally important. Simply put, there is no AI without inference. And when big models are required to execute enterprise-level strategies, things can get complicated. That's why the hardware and software that support enterprise AI inference capabilities can make or break your AI strategy. Successfully deploying your AI workloads at scale depends on how efficiently and effectively your moving pieces are working together. Specifically, inference servers that can support larger AI models (like [LLMs](/en/topics/ai/what-are-large-language-models)) and more complex inference capabilities are essential to scaling AI workloads for the enterprise. These AI tools can make inference more powerful and help engineers use resources more efficiently: * [**llm-d**](/en/topics/ai/what-is-llm-d)**:** LLM prompts can be complex and nonuniform. They typically require extensive computational resources and storage to process large amounts of data. An open source AI framework like llm-d allows developers to use techniques like distributed inference to support the increasing demands of sophisticated and larger reasoning models like LLMs. * [**Distributed inference**](/en/topics/ai/what-is-distributed-inference)**:** Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices. Think of it as the software equivalent of the saying, “many hands make light work.” * [**vLLM**](/en/topics/ai/what-is-vllm)**:** vLLM, which stands for virtual large language model, is a library of open source code maintained by the vLLM community. It uses GPU storage more efficiently to help LLMs perform calculations more efficiently at scale. * [**Mixture of Experts (MoE)**](/en/topics/ai/mixture-of-experts)**:** MoE is a model architecture technique that speeds up AI inference by routing tasks to the most capable part of the model. MoE models are specially trained to answer specific subcategories quickly and accurately. [Why you should care about AI inference](/en/artificial-intelligence/inference) Red Hat AI ---------- [Learn more about this product](/en/products/ai "Learn more about this product") Benefits of enterprise AI ------------------------- Enterprise AI solutions provide benefits that help companies create better business models and reduce obstacles that slow them down. Some of those benefits include: * **Reduced costs.** [AI automation](/en/technologies/management/ansible/ai-automation) can automate everyday tasks and reduce repetitive work, so people can focus on the tasks that deserve their full attention. * **Improved customer experience.** AI excels at analyzing data and identifying patterns—including human behavior. These real-time insights can improve how your customers interact with your brand. * **Error prevention.** AI has the ability to not only identify patterns but also predict what could come next, including anomalies. Predictive AI can help detect errors or malfunctions—before they happen—to avoid extensive downtime and prevent a significant loss in productivity. [Learn about predictive AI use cases](/en/products/ai/predictive-ai) One of the key benefits of AI at the enterprise is streamlined cross-functional collaboration. Without it, these other benefits fall flat at the enterprise level. Enterprise AI platforms should allow your teams to collaborate more easily, where previously there was room for miscommunication. When your teams can work faster and smarter, inefficiencies decrease across the board—especially when one platform works for everyone. [Read in-depth use cases for enterprise AI](/en/topics/ai/ai-ml-use-cases) Risks of enterprise AI ---------------------- Enterprise AI solutions can give businesses opportunities to grow, but can also create potential pitfalls. Understanding the risks can help you prepare and experience less unpredictability. Common risks include: * **Harmful bias.**  Because machine learning models learn from historical data, they can learn bias and discrimination that could inform decision-making. Bias can show up in [generative AI](/en/topics/ai/what-is-generative-ai), in the form of wrong answers, and predictive AI, where it can lead to inaccurate predictions. Healthy data can improve accuracy and lead to better predictions. * **Unreliable information.** AI can develop hallucinations—information that appears legitimate, but is incorrect. Some of these results are merely annoying (an image of a human with 6 fingers on one hand), while others can be dangerous (a chatbot’s misguided advice to those seeking healthcare). * **Security and legal risks.** AI systems can pose security risks. Users might enter sensitive information into apps that were not designed to be secure, increasing the risk of a data breach. In addition, generative AI responses can introduce legal risks by reproducing copyrighted content or appropriating a real person’s voice or identity without their consent. [How to protect your AI workloads](/en/topics/ai/what-is-ai-security) Challenges of enterprise AI --------------------------- Enterprise AI platforms can provide a wealth of opportunities, but require significant resources and consistent collaboration to make an impact. A few common challenges enterprises face are: * **Skill and talent gaps.** A new skill set is required to navigate AI and use it to your advantage. Hiring, onboarding, and training your team can take considerable time and resources. * **High costs.** Enterprises require extensive resources to manage AI systems and operate at high speeds. The computing power needed to keep the technology running, as well as funding trained personnel, is expensive. * **Inability to scale.** Scaling AI at the enterprise can take significant computational and storage resources. Sophisticated and larger reasoning models can be complex and nonuniform, which makes inference slower. A framework like [llm-d](/en/topics/ai/what-is-llm-d) can solve the challenges of [distributed inference](/en/topics/ai/what-is-distributed-inference), empowering developers and AI engineers with a resilient and observable system that speeds up inference at scale. * **Mistrust of AI.** It can be difficult to adapt when change happens fast and there are a lot of unknowns. AI can seem mysterious and untrustworthy. It may take extra effort to secure buy-in from your team and encourage the collaboration you need for success. [Scaling AI with Red Hat AI](/en/products/ai/scaling-ai) vLLM for the enterprise ----------------------- [AI inference](/en/topics/ai/what-is-ai-inference) is being used in high-volume, high-variable use cases. But deploying LLMs consistently at scale requires a lot of computing power, resources, and specialized operational skills. vLLM can overcome these challenges by making more efficient use of the hardware needed to support AI inference for the enterprise. This is why vLLM is especially attractive to industries that need flexibility and control in addition to speed. As an open source solution, vLLM allows companies to: * Own and manage their GPUs. * Control their data. * Experiment with state-of-the-art models as soon as they are released. [vLLM](/en/topics/ai/what-is-vllm) can be deployed across a variety of hardware including NVIDIA and AMD GPUs, Google TPUs, Intel Gaudi, and AWS Neuron. vLLM is also not restricted to specific hardware, meaning it works across the cloud, in the data center, or at the edge. [These 3 well known organizations used vLLM to scale](/en/topics/ai/how-vllm-accelerates-ai-inference-3-enterprise-use-cases) What is an enterprise AI technology stack? ------------------------------------------ When it comes to AI, there are a lot of moving parts. But, like any technology stack, your enterprise AI stack will consist of tools, services, platforms, and software from various sources, which combine to deliver a complete solution. An AI technology stack will consist of different layers such as [large language models](/en/topics/ai/what-are-large-language-models), runtimes, hardware accelerators, and of course, your own enterprise-specific data. Protocols such as [Model Context Protocol (MCP)](/en/topics/ai/what-is-model-context-protocol-mcp) can help different pieces of the AI stack work together. The makeup of your stack can be flexible. It will depend on factors like your enterprise use case, your goals, and your available resources. It’s important to remember that an AI technology stack isn’t rigid. The parts don’t necessarily lay directly on top of each other, like a sandwich. The stack should work together, congruently, each layer doing its specific part for the whole. No matter what your stack looks like, the goal of an AI stack is to create a home for all of your AI solution’s moving parts. This will allow you and your team to identify specific areas for improvement and evaluate how the stack is working together. Strategies for adopting enterprise AI ------------------------------------- AI strategies can include forming a specific AI-enablement team or dedicating a portion of your budget to AI products and services. Here are a few things to keep in mind when adopting, implementing, or scaling AI for your enterprise: * **Decide on your goals.** Once you understand [how AI could help your business](/en/topics/ai/ai-ml-use-cases), you can identify how you want your business to grow. Knowing what your end goal looks like will help you work backwards so you know where to begin. * **Check on your data health.** Your data will be the key to a successful AI strategy. Without healthy data, the software and platforms are just empty vessels. With up-to-date, accurate, and unbiased data, you can take full advantage of your technology stack. * **Start small.** If you’re not ready to scale across all of your environments, experiment with small models on your own hardware. Getting familiar with AI on a beginner level can help you prepare for challenges when you scale. * **Lean on experts.** AI is not easy. It can get really complicated rather quickly. It’s common (and recommended) to partner with a team that knows their way around the technology. [Check out Red Hat AI Services →](/en/services/consulting/red-hat-consulting-for-ai)  * **Operationalize AI.** An AI platform dedicated to [operationalized AI](/en/products/ai/operationalize-ai) simplifies lifecycle management for AI applications. It encourages the cross-functional collaboration we mentioned earlier and allows you to scale with all of your teams, together. * **Digital sovereignty.** [Digital sovereignty](/en/topics/ai/digital-sovereignty) is an organization’s ability to control its digital assets—deciding where its data lives, how its systems run, and who has access to them. Think of it as companies “reigning” over their data and technology, rather than handing control to an external provider. Digital sovereignty can apply to infrastructure, operations, AI workloads, and more. [How to build an AI strategy for long-term growth →](/en/resources/promotes-long-term-growth-ai-strategy-brief) How Red Hat can help -------------------- [Red Hat® AI](/en/products/ai) is built for fast, flexible, and efficient inference through its [vLLM-powered](/en/topics/ai/what-is-vllm) server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our products give you full control of AI workflows from end-to-end at any scale. The Red Hat AI portfolio includes [Red Hat AI Enterprise](/en/products/ai/enterprise), a platform for deploying, managing, and scaling AI inference, agentic AI workflows, and AI-powered applications on any infrastructure. [Explore Red Hat AI](/en/products/ai) Open the future: An executive’s guide ------------------------------------- An executive's guide to navigating the era of constant innovation [Keep reading](/en/solutions/open-the-future "Open the future: An executive’s guide") The adaptable enterprise: Why AI readiness is disruption readiness ------------------------------------------------------------------ This e-book, written by Michael Ferris, Red Hat COO and CSO, navigates the pace of change and technological disruption with AI that faces IT leaders today. [Get the resource](/en/resources/the-adaptable-enterprise-ai-ebook "The adaptable enterprise: Why AI readiness is disruption readiness") Keep reading ------------ ### What is agentic AI? Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. [Read the article](/en/topics/ai/what-is-agentic-ai "article | What is agentic AI?") ### What is generative AI? Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content. [Read the article](/en/topics/ai/what-is-generative-ai "article | what is generative ai?") ### What are large language models? A large language model (LLM) is a type of artificial intelligence that uses machine learning techniques to understand and generate human language. [Read the article](/en/topics/ai/what-are-large-language-models "article | What are large language models?") Artificial intelligence resources --------------------------------- ### Related content * Blog post [The future of AI demands a hybrid foundation](/en/blog/future-ai-demands-hybrid-foundation) * Blog post [AI in production at the industrial edge: A repeatable path with Red Hat and Intel](/en/blog/ai-production-industrial-edge-repeatable-path-red-hat-and-intel) * Blog post [Fragnesia and friends: When page cache vulnerabilities keep coming back](/en/blog/fragnesia-and-friends-when-page-cache-vulnerabilities-keep-coming-back) * E-book [Generative AI in action](/en/resources/generative-ai-in-action-ebook) ### Related articles * [What is agentic AI?](/en/topics/ai/what-is-agentic-ai) * [What is AI inference?](/en/topics/ai/what-is-ai-inference) * [vLLM vs. Ollama: When to use each framework](/en/topics/ai/vllm-vs-ollama) * [RAG vs. fine-tuning](/en/topics/ai/rag-vs-fine-tuning) * [What is vLLM?](/en/topics/ai/what-is-vllm) * [SLMs vs LLMs: What are small language models?](/en/topics/ai/llm-vs-slm) * [LoRA vs. QLoRA](/en/topics/ai/lora-vs-qlora) * [What are large language models?](/en/topics/ai/what-are-large-language-models) * [What are Granite models?](/en/topics/ai/what-are-granite-models) * [What is Mixture of Experts (MoE)?](/en/topics/ai/mixture-of-experts) * [Predictive AI vs generative AI](/en/topics/ai/predictive-ai-vs-generative-ai) * [What is generative AI?](/en/topics/ai/what-is-generative-ai) * [What are intelligent applications?](/en/topics/ai/what-are-intelligent-applications) * [How vLLM accelerates AI inference: 3 enterprise use cases](/en/topics/ai/how-vllm-accelerates-ai-inference-3-enterprise-use-cases) * [What is AgentOps?](/en/topics/ai/agentops) * [What is parameter-efficient fine-tuning (PEFT)?](/en/topics/ai/what-is-peft) * [AIOps explained](/en/topics/ai/what-is-aiops) * [What is LLMops](/en/topics/ai/llmops) * [What are foundation models for AI?](/en/topics/ai/what-are-foundation-models) * [What is deep learning?](/en/topics/ai/what-is-deep-learning) * [What is MLOps?](/en/topics/ai/what-is-mlops) * [What is sovereign AI?](/en/topics/ai/sovereign-ai) * [What is Model Context Protocol (MCP)?](/en/topics/ai/what-is-model-context-protocol-mcp) * [What is distributed inference?](/en/topics/ai/what-is-distributed-inference) * [What is retrieval-augmented generation?](/en/topics/ai/what-is-retrieval-augmented-generation) * [Understanding AI/ML use cases](/en/topics/ai/ai-ml-use-cases) * [What is AI in the public sector?](/en/topics/ai/what-is-ai-in-the-public-sector) * [AI infrastructure explained](/en/topics/ai/ai-infrastructure-explained) * [What is AI in healthcare?](/en/topics/ai/what-is-ai-in-healthcare) * [What is explainable AI?](/en/topics/ai/what-explainable-ai) * [Understanding AI in telecommunications with Red Hat](/en/topics/ai/understanding-ai-in-telecommunications) * [What is machine learning?](/en/topics/ai/what-is-machine-learning) * [Agentic AI vs. generative AI](/en/topics/ai/agentic-ai-vs-generative-ai) * [What is AI security?](/en/topics/ai/what-is-ai-security) * [What is an AI platform?](/en/topics/ai/what-is-an-ai-platform) * [What is Model-as-a-Service?](/en/topics/ai/what-is-models-as-a-service) * [What is llm-d?](/en/topics/ai/what-is-llm-d) * [What is edge AI?](/en/topics/edge-computing/what-is-edge-ai) * [AI in banking](/en/topics/ai/ai-in-banking) * [What is InstructLab?](/en/topics/ai/what-is-instructlab) * [Why choose Red Hat Ansible Automation Platform as your AI foundation?](/en/topics/automation/automation-and-ai) * [Edge solutions for real-time decision making](/en/topics/edge-computing/edge-solutions-real-time-decision-making) * [What are predictive analytics](/en/topics/automation/how-predictive-analytics-improve-it-performance) * [How Kubernetes can help AI/ML](/en/topics/cloud-computing/how-kubernetes-can-help-ai) * [What is Kubeflow?](/en/topics/cloud-computing/what-is-kubeflow) * [Accelerate MLOps with Red Hat OpenShift](/en/technologies/cloud-computing/openshift/aiml) * [What is a data lake?](/en/topics/data-storage/what-is-a-data-lake) [More about this topic](/en/topics/ai "More about this topic")