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.

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Enterprise AI vs. AI at Small to Medium Businesses: What Is the Difference?

While both enterprise AI and regular AI utilize artificial intelligence technologies, they differ significantly in their scope, scale, and application. Regular AI typically refers to AI applications developed for general use, often aimed at solving specific problems or performing specific tasks. These applications might include virtual assistants, recommendation systems, or simple chatbots.

Enterprise AI, on the other hand, is tailored for large-scale, complex business environments. It involves integrating AI into various business processes across an organization, providing a cohesive and comprehensive framework that can handle large volumes of data and complex analytics.

  • Scope and Scale: Enterprise AI integrates AI across the organization to handle large datasets and complex processes, while regular AI focuses on specific tasks as standalone applications.
  • Customization and Flexibility: Enterprise AI is highly customizable to meet diverse business needs, whereas regular AI is more generic and less customizable.
  • Data Integration: Enterprise AI integrates data from multiple, diverse sources, while regular AI operates with more limited, homogeneous data sets.
  • Security and Compliance: Enterprise AI includes robust security and compliance features, unlike regular AI, which has less advanced security measures.‍
  • Collaboration and Usability: Enterprise AI facilitates collaboration among various stakeholders, whereas regular AI offers limited collaborative functionality

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.

Maintenance

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.

Decision-Making

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

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.

What Is Required to Operate AI at Enterprise Scale?

Enterprise scale refers to the capability of AI systems to handle the extensive data, complex processes, and infrastructure needs of large organizations. This involves:

  1. Data Management: Handling diverse and massive datasets from various sources, including structured databases, unstructured documents, and streaming IoT data.
  2. Scalability: Ensuring the AI infrastructure can scale horizontally and vertically, maintaining performance with increasing demands through distributed workloads and optimized resource use.
  3. Integration: Seamlessly connecting with existing IT systems like ERPs and CRMs to support enterprise-wide processes.
  4. Security and Compliance: Implementing stringent security measures and adhering to regulations such as GDPR and HIPAA, including encryption, access controls, and auditing.
  5. Reliability: Offering high availability and reliability with failover mechanisms, redundancy, and disaster recovery plans.
  6. Advanced Analytics: Providing deep insights and predictive analytics to identify patterns, forecast trends, and inform strategic decisions.
  7. Automation: Automating repetitive tasks to free human resources for strategic activities across customer service, supply chain, and finance.
  8. Collaboration: Supporting collaboration with user-friendly interfaces and tools for data visualization, model management, and workflow orchestration.

These components ensure AI systems can meet large organizations' demands, driving innovation and competitive advantage.

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.

Best Practices for Implementing Enterprise AI

Assess Data Preparedness and Develop Data Strategy

Before embarking on an enterprise AI project, it's crucial to assess your organization's data readiness. This involves evaluating the current state of your data in terms of volume, variety, and quality. Identify gaps and inconsistencies, and determine whether the data is clean, well-organized, and suitable for AI applications.

Developing a comprehensive data strategy is the next step. This strategy should outline how data will be collected, stored, processed, and analyzed. It should also address data governance, ensuring compliance with relevant regulations and maintaining data privacy and security. Establish clear protocols for data integration from various sources to create a unified, accessible data repository that will serve as the foundation for your AI initiatives.

Build a Cross-Functional Team

Successful implementation of enterprise AI requires a multidisciplinary team that brings together diverse skills and perspectives. This team should include data scientists, software engineers, IT specialists, business analysts, and domain experts. Each member plays a crucial role in different phases of the AI project, from data preparation and model development to deployment and maintenance.

Encouraging collaboration among these experts is essential. Regular communication and a shared understanding of the project's goals help ensure that the AI solutions developed are not only technically sound but also aligned with the business objectives. Establishing a culture of continuous learning and adaptability will help the team stay updated with the latest AI advancements and best practices.

Launch a Pilot Program

Launching a pilot program is a strategic way to test the feasibility and impact of your AI project on a smaller scale before a full-scale rollout. Start by selecting a specific use case that is manageable and has clear, measurable objectives. This could be a process improvement in a particular department or a targeted customer service enhancement.

The pilot program allows you to identify potential issues and address them early, minimizing risks. It provides an opportunity to gather feedback from users, refine models, and improve system integration. Use the insights gained from the pilot to make informed adjustments to your approach, ensuring a smoother transition to full deployment. Documenting lessons learned and best practices during this phase will be valuable for scaling the AI solution across the enterprise.

Implement Ethical AI Practices

Ethical considerations are paramount when developing and deploying AI systems. Establish guidelines to ensure the ethical use of AI, focusing on fairness, accountability, and transparency. Develop mechanisms to identify and mitigate biases in data and models.

Create a governance framework that includes ethical review boards or committees to oversee AI projects. Regularly audit AI systems to ensure they align with ethical standards and do not perpetuate harmful biases or practices. Promote transparency by documenting AI decision-making processes and providing stakeholders with clear explanations of how AI-driven decisions are made.

Monitor and Maintain AI Systems

Once the AI system is deployed, continuous monitoring is essential to ensure it operates as intended and delivers the expected outcomes. Establish performance metrics and benchmarks to track the system’s effectiveness. Regularly review these metrics to identify any deviations or performance issues.

Maintenance should include updating models with new data to improve accuracy and relevance. Implement automated monitoring tools to detect anomalies or unexpected behaviors in real-time. Ensure there is a process for periodic model retraining to adapt to changing data patterns and business needs.

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.

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