Whether boosting productivity, lowering costs, or optimizing operations, bringing AI into the enterprise offers strategic advantages. However, identifying the business applications that drive ROI and align with operational priorities is essential before investing in an AI platform.

An enterprise AI platform empowers teams by offering an end-to-end unified UI for data wrangling, mining, visualization, machine learning, and deployment. Request a demo to learn more about how Capacity can accelerate your organization.

Build a Consistent Object Model Across the Enterprise

Whether your business is tackling supply chain management or customer service issues, finding an AI approach that aligns with its business goals is essential. This means having the right tools, resources, and expertise to support your chosen technology. It also means identifying the most critical data and ensuring it’s accessible to all users.

Enterprise AI provides the tools to automate and complete tasks that would otherwise require a human. This enables teams to focus on their strategic, problem-solving roles by eliminating time-consuming and mundane tasks like scheduling meetings, sending emails, or searching documents for relevant information.

The ideal enterprise AI platform is built on a catalog of API-based microservices, allowing developers to swiftly build applications without in-depth knowledge of the underlying data formats and storage methods. The architecture should accommodate changes to the object model at runtime and allow for dynamic access rights based on user-specific permissions.

A key feature of enterprise AI is the ability to work with all types of data, including code, time-series data, tabular data, geospatial data, semi-structured data, and text combined with images. This allows you to understand your business processes better, helping you make more informed decisions and improve organizational productivity. The right solution should offer versatility, scalability, security, and reliability.

Provide Edge Deployment Options

An enterprise AI platform should enable a wide range of deployment options. It should allow data to flow in from multiple sources and support private and public clouds and various specialized services deployed within specific geographic regions to adhere to data sovereignty regulations. A multi-cloud operating environment also allows for easy scalability of the platform to meet increasing data demands.

AI solutions should provide flexibility to fit into an organization’s existing tech stack, supporting various programming languages and API integrations. AI can work with employees’ tools to promote productivity and innovation. A platform that offers compatibility with diverse devices also makes it possible to deploy enterprise AI in more use cases, delivering broader ROI and business value.

Enterprise AI solutions are poised to transform how businesses operate and deliver customer value. Whether it’s intelligent personal assistants or automated online customer support chatbots, enterprise AI has already been shown to improve employee efficiency and boost performance metrics such as customer satisfaction. However, bringing these solutions to scale requires that the power of data science be distributed throughout an organization. This is where edge (or fog) computing comes in. By offloading computational processes to a server near the data source, edge AI can reduce transmission costs and latency, enabling real-time insight for enterprises.

Enable Multi-Cloud Deployments

AI-based solutions are transforming the way businesses function. They improve customer service, reduce maintenance costs, and enhance decision-making processes. However, developing an enterprise-level AI solution is a complex process that involves carefully defining the business problem, gathering and assessing data, choosing the right AI technologies, building a data pipeline, training models, deploying the solution, and monitoring performance.

To maximize the benefits of enterprise AI, a business must ensure that the platform is multi-cloud capable. It can function on multiple public cloud platforms, access private cloud environments or specialized services like Google Translate, and adhere to regional data sovereignty requirements. This flexibility allows organizations to take advantage of the best services within each cloud without worrying about vendor lock-in.

Another critical feature of a multi-cloud solution is the ability to enable edge deployments. This enables enterprises to use AI analytics, predictions, and inferences directly at the source. This is especially useful in areas with low-latency computing needs or limited network bandwidth.

To enable a multi-cloud strategy, businesses need a robust and scalable enterprise message bus to map data from the various source systems into a standard data exchange model. Doing so can reduce the system interfaces required to develop a comprehensive enterprise AI solution. This allows data scientists, engineers, architects, and other users to access the necessary AI capabilities quickly.

Provide Easy Access to Data

Whether AI is used for customer service, supply chain management, or cybersecurity, the data that fuels these systems is vital. With easy access to this data, businesses can reap the benefits of these powerful technologies.

Companies must prioritize security, privacy, and compliance to ensure the best possible results from their AI initiatives. This includes building policies and processes to ensure the accuracy of data and the ethical use of that data, even in unexpected ways. It’s also essential to keep a close eye on the evolving landscape of data privacy regulations and ensure that all AI models are up-to-date with these changes.

Generative AI, which builds from existing training data and adapts to new scenarios, is a powerful way to achieve this. By leveraging this advancement, business leaders can avoid the cost of building each model from scratch and achieve higher ROI and faster time to market.

AI adoption rapidly moves beyond small, use-case-specific applications to a larger paradigm that places it at the core of many business operations. This helps reduce rote work for employees while enabling them to focus on fulfilling uniquely human roles that help the organization thrive.