Question 1

Why do energy AI pilots stall?

Accepted Answer

Most often at the operational-data layer. Sensor histories are sparse, asset hierarchies are inconsistent, and maintenance event metadata is incomplete. AI readiness work is mostly data foundation work.

Question 2

Is grid AI worth the investment for utilities?

Accepted Answer

For utilities with significant renewable integration or distributed energy resources, yes. See [AI powering energy transition](/insights/ai-powering-energy-transition) and [AI grid future energy](/insights/ai-grid-future-energy) for the use cases that pay back.

Question 3

What is the AI readiness engagement timeline?

Accepted Answer

30-day Analytics Truth Audit ($35,000 fixed). Implementation runs 12 to 24 weeks at $160,000 to $440,000 depending on asset count and workload complexity.

Question 4

What does AI readiness actually mean?

Accepted Answer

AI readiness means the data foundation is clean enough, the governance is defensible enough, and the metric layer is consistent enough that AI deployment lands without producing the kind of hallucinations or compliance issues that block production use. The work is mostly at the data layer.

Question 5

Do you build AI models or just the data layer?

Accepted Answer

We focus on the data foundation that supports AI deployment. Model selection, training, and tuning we coordinate with the client's AI team or specialist partners. The data layer is where most AI pilots stall.