The Definition, Challenges, and Solutions of Enterprise AI

General-purpose LLMs can handle a wide range of tasks from research and translation to coding, but when input data is biased or incomplete, hallucinations and incorrect answers increase. Enterprise AI combines task-specific models (TSMs) tailored to high-value tasks like customer support, document search, and summarization with internal documents to boost accuracy and control.

Cases like the 2023 Mata v. Avianca incident, where ChatGPT fabricated nonexistent case law and citations, show how easily large models can lose trust in real-world work. A 2023 KPMG survey also found that about 73% of respondents worldwide were concerned about the risks of AI.

The solution is grounding and RAG. By designing models to search an organization's internal knowledge base for supporting evidence and to limit answers rather than guess when information is insufficient, both accuracy and security can be improved together. API-based pre-tuned models also allow organizations lacking AI specialists to adopt them quickly.

AI21 offers the following examples:

• Banking: automating term sheet summarization achieved 98% accuracy in testing.
• Retail: generating multilingual product descriptions and augmenting data reduced new product onboarding time by more than 25%.
• Healthcare: built a patient-facing medical response system in 8 weeks, providing safe answers.

The key point is that, moving past the experimental phase of general-purpose AI, enterprise AI is now entering real production environments with lower cost and higher reliability.