MHHS arrives with more granular data than most settlement systems have ever processed. Half-hourly consumption per meter, per customer, at scale, on a regulated cadence. The use cases this enables — better tariff design, sharper anomaly detection, faster vulnerable-customer identification, more accurate billing — are exactly the use cases AI is good at. They are also exactly the use cases where unassured AI causes the largest customer harm.
Why MHHS expands the AI surface.
Three reasons. The data volume invites models that did not previously have enough signal to be interesting. The data granularity invites decisions that did not previously have enough basis to be defensible. And the data cadence invites automation that did not previously have enough freshness to be safe. Each of those invitations is reasonable. Each is also a place where the AIIA either exists in advance or does not.
What MHHS readiness reviews actually ask.
Not, in the first instance, AI questions. They ask about data governance, anomaly handling, vulnerable-customer protections and customer-communications discipline. But because AI now mediates each of those, the answers either invoke the AI assurance or expose its absence. A supplier that can show, alongside its MHHS plan, an AIIA inventory covering the AI in settlement, billing, anomaly handling and vulnerability identification will be marked as low-risk. A supplier that cannot will be asked the same questions twice, in writing.
What good looks like.
A single line of sight from MHHS data feeds, through the AI use cases that consume them, to the AIIAs that govern those use cases, to the human review that backstops them. Stand the line of sight up before the readiness review, not during it. Bring the supplier's published Code of Practice into the same line of sight; an MHHS-era AI decision that satisfies the regulator but contradicts the supplier's own Code is still a finding waiting to happen.
What suppliers underestimate.
The compounding effect. Every additional AI use case enabled by MHHS is one more AIIA, one more refresh cycle, one more frontline review queue. Suppliers that built for one or two AI systems will discover that ten AI systems require not ten times the discipline but a different operating model. The time to build that operating model is before MHHS is in flight.