What Is Enterprise AI?

When you implement enterprise AI, you can solve previously unsolvable challenges. It helps you drive new revenue sources and efficiencies in a large organization.

Drive innovation

Large enterprises typically have several hundred business teams, but not all have the budget and resources for data science skills. Enterprise-scale AI allows leadership to democratize artificial intelligence and machine learning (AI/ML) technologies and make them more accessible across the company. Anyone in the organization can suggest, experiment with, and incorporate AI tools into their business processes. Domain experts with business knowledge can contribute to AI projects and lead digital transformation.

Enhance governance

Siloed approaches to AI development provide limited visibility and governance. Siloed approaches reduce stakeholder trust and limit AI adoption—especially in critical decision-making predictions.

Enterprise AI brings transparency and control to the process. Organizations can control sensitive data access according to regulatory requirements while encouraging innovation. Data science teams can use explainable AI approaches to bring transparency to AI decision-making and increase end-user trust.

Reduce costs

Cost management for AI projects requires careful control over development effort, time, and computing resources, especially during training. An enterprise AI strategy can automate and standardize repetitive engineering efforts within the organization. AI projects get centralized and scalable access to computing resources while ensuring no overlap or wastage. You can optimize resource allocation, reduce errors, and improve process efficiencies over time.

Increase productivity

By automating routine tasks, AI can reduce time wastage and free human resources for more creative and productive work. Adding intelligence to enterprise software can also increase the speed of business operations, reducing the time needed between different stages in any enterprise activity. A shortened timeline from design to commercialization or production to delivery can provide an immediate return on investment.

Successfully deploying enterprise AI requires organizations to implement the following.

Data management

AI projects require easy and secure access to enterprise data assets. Organizations must build up their data engineering pipelines, whether streaming or batch data processing, data mesh, or data warehousing. They must ensure systems like data catalogs are in place so data scientists can quickly find and use the data sets they need. Centralized data governance mechanisms regulate data access and support risk management without creating unnecessary obstacles in data retrieval.

Model training infrastructure

Organizations must establish a centralized infrastructure to build and train new and existing machine learning models. For example, feature engineering involves extracting and transforming variables or features, such as price lists and product descriptions, from raw data for training. A centralized feature store allows different teams to collaborate, promoting reuse and avoiding silos with duplicate work efforts.

Similarly, systems that support retrieval-augmented generation (RAG) are needed so that data science teams can adapt existing AI models with internal enterprise data. Large language models (LLMs) are trained on vast data volumes and use billions of parameters to generate original output. You can use them for tasks like answering questions, translating languages, and natural language processing. RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model.

Central model registry

A central model registry is an enterprise catalog for LLMs and machine learning models built and trained across different business units. It allows model versioning, which lets teams accomplish many tasks:

• Track model iterations over time
• Compare performance across different versions
• Ensure that deployments are using the most effective and up-to-date versions

Teams can also maintain detailed records of model metadata, including training data, parameters, performance metrics, and usage rights. This enhances collaboration among teams and streamlines the governance, compliance, and auditability of AI models.

Model deployment

Practices like MLOps and LLMOps introduce operational efficiency to enterprise AI development. They apply the principles of DevOps to the unique challenges of AI and machine learning.

For example, you can automate various ML and LLM lifecycle stages, such as data preparation, model training, testing, and deployment, to reduce manual errors. Building ML and LLM operational pipelines facilitates continuous integration and delivery (CI/CD) of AI models. Teams can rapidly iterate and update models based on real-time feedback and changing requirements.

Model monitoring

Monitoring is crucial to managing AI models, ensuring the reliability, accuracy, and relevance of AI-generated content over time. AI models are prone to hallucinate or occasionally generate inaccurate information. Model output can also become irrelevant due to evolving data and contexts.

Organizations must implement human in the loop mechanisms to effectively manage LLM output. Domain experts periodically assess AI output to ensure its accuracy and appropriateness. Using real-time feedback from end users, organizations can maintain the integrity of the AI model and ensure it meets the evolving needs of stakeholders.