Enterprise AI Platforms

Integrating AI with the Enterprise

What Is Enterprise AI?

Enterprise AI is the use of artificial intelligence (AI) and machine learning (ML) to perform business processes. It involves automating everyday business management tasks, providing business insights, and informing business decisions based on data analytics. Businesses leverage enterprise AI solutions to adapt to the emerging digital age with increased efficiency, automation, and data-driven strategies. 

Organizations apply enterprise AI at organization-wide scale to provide a foundational business tool to deliver structured data, data-based insights, and easily accessible, actionable business information, which any business stakeholder can understand. 

Enterprise AI provides insights for managers, executives, and other stakeholders responsible for making business decisions. It allows businesses to combine AI capabilities with human intelligence to increase the efficiency and effectiveness of processes. 

This is part of our series of articles about machine learning engineering.

In this article:

The Need for Enterprise AI

Enterprise AI is a new paradigm in computing and programming that helps address two distinctive needs of modern businesses: 

  • Companies must aggregate massive amounts of data from diverse sources in disparate structures and formats, creating a unified data source. 
  • Deriving insights from this large-scale data requires advanced analytics capabilities powered by AI and ML. 

Organizations must leverage enterprise AI platforms to build, scale, and deploy their high-performance AI applications quickly, repeatedly, and consistently. This paradigm is similar to other historical shifts in computing standards, such as mobile access and cloud computing. Each new computing paradigm requires the wholesale adoption of a new business approach, supported by a new development platform.

An enterprise AI platform provides a comprehensive set of capabilities allowing business stakeholders (i.e., software engineers, architects, data scientists, administrators, and analysts) to access AI-driven technologies in a unified environment. It helps teams address the ongoing requirements for business applications, including the development, testing, deployment, and operation of new applications.

Historically, businesses that tried to develop AI-powered applications had to use various components (usually open source) to build the applications. These components typically do not provide a consistent experience, are not integrated with each other, and may not meet some of the operational and security requirements of a large enterprise. 

In an open source best of breed model, software developers had to produce large amounts of code to enable interoperability between all their components. This approach resulted in complex, brittle applications, which were difficult to maintain, adjust, and scale.

Today, enterprise AI solutions allow organizations to build AI applications using repeatable processes, enabling large-scale projects. This platform-based approach reduces the complexity of building an enterprise AI application and simplifies the deployment and maintenance processes. 

Related content: Read our guide to machine learning workflow

What Is an Enterprise AI Platform?

An enterprise AI platform helps organizations run large AI projects with high development velocity. It is a framework for creating and scaling AI-powered solutions, providing guidelines to implement a service-based architecture. 

A well-designed enterprise AI platform allows AI specialists and software engineers to collaborate efficiently and lower operational costs by automating tasks and eliminating duplication. 

Enterprises often use an AI platform to address skills shortages and distribute workloads efficiently. The elements of a platform fall into the following logical layers:

  • Data and integration—this layer provides access to enterprise data, allowing machine learning applications to train.
  • Experimentation—this layer allows AI scientists to build and test hypotheses, automating the feature engineering, model and feature selection, and optimization processes. 
  • Operations and deployment—this layer facilitates deployment and governance, allowing teams to conduct risk assessments to identify model bias and validate the proof-of-model. It includes experiment results. 
  • Intelligence—this layer powers the AI at runtime, exposing reusable components to intelligent APIs.
  • Experience—this layer lets users engage with the project via technologies like UI and augmented reality. It involves visual design, conversational features, and user experience specialists. 

Related content: Read our guide to machine learning infrastructure

Enterprise AI Use Cases

Businesses can use enterprise AI in various ways.

Automated Processes

AI allows organizations to fully automate various processes, including paperwork, security, and maintenance. It provides scalability and consistency and reduces the manual workload, allowing teams to focus on human-driven processes.


A predictive maintenance system can help companies keep their infrastructure stable by predicting and identifying failures. It integrates embedded IoT sensors and devices with AI to monitor and analyze equipment performance, minimizing downtime.


AI can support decision-making to help enterprises plan according to data-derived insights. An intelligent decision-making approach helps establish a more effective business strategy.


Chatbots provide an initial point of contact for customers, enabling automated communication processes. They are also useful for internal HR teams and stakeholders, helping accelerate business processes.

Data Protection

Large enterprises that process large amounts of data must maintain data privacy to comply with regulations and implement good data governance. An enterprise AI solution can organize data and help enforce data protection policies—for example, by handling privacy requests from customers.

Enterprise AI Challenges

Here are some of the main challenges when using an enterprise AI solution.

Data Quality

Many organizations think they can collect any data, and the AI will perform as desired. However, the quality of the data is important, so large amounts of irrelevant data are not useful. Finding the right data can be challenging because companies must ensure the data they collect is relevant, unbiased, and doesn’t compromise privacy. The focus should be on quality, not quantity.

Tooling and Development Costs 

Building an AI system from scratch is time-consuming and expensive, requiring access to specialized expertise and tools. Most small organizations cannot afford to develop AI projects in-house, so outsourcing is the preferred option. Another challenge is to ensure the tools are compatible with the company’s existing systems.

Leadership Issues

Implementing AI projects requires clear leadership. Often in large enterprises, there are many individuals with overlapping roles and responsibilities competing over the leadership of a project. If there is more than one project owner, the project can suffer due to clashing opinions and lack of direction. 

Organizations must establish a clear, unified vision when starting an AI project, but this is not always easy to achieve. Some team members may be more enthusiastic about adopting certain AI tools than others, and management can struggle to cope with rapid changes. The uneven adoption of AI tools across the organization can impact the efficiency and cost of the project.

Enterprise AI Resource Management with Run:ai

Enterprise AI platforms require large-scale computing resources and managing hundreds or thousands of GPUs across multiple environments. Run:ai automates resource management and orchestration for machine learning infrastructure. With Run:ai, you can automatically run as many compute intensive experiments as needed. 

Here are some of the capabilities you gain when using Run:ai: 

  • Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
  • No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
  • A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.

Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models. 

Learn more about the Run:ai GPU virtualization platform.