Many buildings should not adopt AI yet. Faulty sensors, weak control sequences, inaccessible trend data, and neglected maintenance will undermine even a strong model. Adding intelligence can automate poor decisions and obscure their cause.
The answer is to treat AI as a governed supervisory capability. The existing building management system, or BMS, should continue to run deterministic controls and safety functions. AI should operate above it, interpreting data and recommending bounded actions.
This changes the investment case. The board is deciding where better decisions can produce measurable value, what authority software should receive, and what evidence is required before that authority expands.
Most enterprise AI initiatives struggle to transition from pilot demonstrations to mission-critical production. When autonomous systems fail in enterprise deployments, executive post-mortems typically blame stochastic hallucinations in the foundational language model. In reality, the primary failure mode stems from unmonitored degradation within enterprise AI memory architectures.
Enterprise leaders have good reason to be skeptical of adding another evaluation layer to AI systems. More tests can increase cost, slow releases, and create another set of metrics for teams to manage. LLM-based evaluators also introduce their own errors. Research has documented position bias, preference for longer answers, and self-enhancement bias when language models judge other models.
The response should not be more evaluation for its own sake.
