Only one-third of IT asset management teams can reliably account for costs and benefits of AI projects, while over 50 percent report AI spending without measurable added value.
Organizations should evaluate dependency on public AI APIs as an operational risk and incorporate private or self-hosted models into their IT risk strategy.
Agents with explicit rules are suited for known patterns and deterministic decisions, while LLMs demonstrate their value in interpretation-intensive tasks without predefined solution paths.
A two-stage pipeline using Amazon Nova 2 Lite for structured extraction and Claude Sonnet 3.5 for spatial reasoning reduces document digitization costs by two-thirds.
PAR Technology does not treat LLM models as security boundaries for multi-tenant data, but instead locks down data access through cryptographic signing, semantic validation, and programmatic SQL isolation.
