What is enterprise AI?

Enterprise AI is the integration of artificial intelligence (AI) tools and 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

Many businesses are using AI to get a competitive edge in their industry. Industries such as healthcare, telecommunications, and banking are using AI to streamline finances, improve customer experience, and work more efficiently. Enterprises are quickly learning how to apply both generative AI and predictive AI to everyday tasks and complex, long-term problem solving. 

Enterprises use AI tools and techniques such as large language models (LLMs), retrieval-augmented generation (RAG), and machine learning operations (MLOps) to update their operations and introduce new services. 

Read Red Hat AI customer stories 

Enterprise AI creates opportunities to think about business differently. 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. Now, businesses can solve problems in weeks rather than years. 

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. 

Learn about generative AI use cases 

Learn about predictive AI use cases 

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 can automate everyday tasks and reduce repetitive work, so people can focus on certain 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. 

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 

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, 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. 

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. Distributed data, hardware and software can make it even harder to integrate AI applications across large enterprises.
• 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. 

Learn how agentic AI can help your business 

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, runtimes, hardware accelerators, and of course, your own enterprise-specific data. 

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 as a solution. 

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, 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. With InstructLab, you can fine-tune your models locally on your laptop. 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 → 

• Operationalize AI. An AI platform dedicated to operationalized 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. 

How to build an AI strategy for long-term growth → 

The thing is, every enterprise is different. Your enterprise is unique and your AI goals will be too. 

Red Hat® AI is a portfolio of solutions that includes a holistic, accessible AI platform that can help you achieve enterprise-specific goals, big or small. 

Our AI portfolio includes:

• An AI platform that allows cross-team collaboration.
• Small, purpose-built models such as IBM’s Granite.
• Accessible model-tuning capabilities with InstructLab.

We also provide a wide range of partner vendors to choose from, so you can stay flexible as you scale. 

Red Hat AI puts you in control of both generative and predictive AI capabilities—in the cloud, on-premise, or at the edge. Regardless of where your data resides, our AI platform can help you deploy consistently across the hybrid cloud.

Explore Red Hat AI