Part 4/13:
Cost and Complexity: Building production-grade AI systems involves integrating multiple components such as data pipelines, models, and applications. The cost of tokens, infrastructure, and skilled personnel can be substantial.
Reliability and Trust: High-stakes use cases, such as medical insurance or travel planning, demand dependable responses. Ensuring the AI’s outputs are trustworthy, explainable, and compliant with regulations is a major concern.
Data Governance and Security: Protecting enterprise data and ensuring it isn’t used improperly during model training or inference is critical. Organizations want assurances that their data remains private and secure.