Enterprise AI · Start here
Five AI decisions on a Maximo estate, and the page that settles each
Every route below names the signal that you are on it, the artefact the page hands you, and the move that looks right from there. Three limits hold on all five. The tools underneath are AI Engine, AI Smart Data, Assist and AI Crew.
Before the routes
Three limits we hold on every route
These apply on all five routes and to all four products.
Every write to your estate passes a named reviewer
Work orders, asset records, classifications and configuration reach a client estate through a person. Where controlled automation is used, the boundary is agreed in advance with a named owner and the outcome stays reviewable.
The model follows the data that can carry the decision
Where asset hierarchy, classification or work history falls short, data work is sequenced first and the model follows. That is the most common reason an AI programme in asset management stalls, which is why decision-ready data is a route of its own.
Accountability sits with your named decision owners
Intervening on an asset, accepting a risk, approving a data change or signing a regulator-facing number belongs to a person in your organisation with the authority to do it. MaxIron holds the controls and the evidence.
The five routes
Pick the decision you are stuck on
Where the asset data behind a decision will not carry it, route two goes first, because the other four depend on it. Where assurance is the blocker, start at route five.
Do we intervene on this asset now, or hold to the next planned outage?
Condition Insight, one pump worked →You are on it if
Condition data exists, trends get carried forward in a weekly review pack, and nobody is certain who is permitted to defer an intervention on a critical asset.
The wrong move here
Licensing a predictive capability before anybody holds the authority to act on what it says. Better evidence then produces better-informed indecision.
Is our asset data good enough to model anything on yet?
Decision-ready data, six failure modes →You are on it if
Data quality work is scoped as an enabler, funded last and cut first. Four sites classify the same pump three different ways and nobody can settle it.
The wrong move here
A one-off clean-up with no standard owner and no re-measurement. Expect about a year of benefit and then a return to the same gaps.
Which analytics capability earns its place next?
Advanced analytics, five rungs →You are on it if
You hold Monitor, Predict or Health licences that are not in use, or you owe an analytics roadmap and have no defensible way to sequence it.
The wrong move here
Licensing the rung above the one you are standing on. Predict without consistent failure history produces confident output nobody acts on.
What does this look like day to day on an estate somebody else operates?
MaxIron Intelligence, how we run it →You are on it if
You are past whether AI can help and into how it is staffed, refused, evidenced and handed back at the end of a contract.
The wrong move here
Treating the review commitment as free. An AI capability arrives as standing review work that has to be named, diarised and covered during leave.
What will our auditor, risk function or public-sector buyer ask us to produce?
Governance, Q1 to Q7 and A1 to A4 →You are on it if
A working capability is parked because assurance cannot sign it, or an AI questionnaire has arrived with four questions nobody can answer.
The wrong move here
Presenting an information security certificate as evidence of model governance. Your risk function will notice they are different questions.
Two questions asked as one
The tool question and the operating-model question have different answers
The tool question
What does this software do, step by step? Asked by platform owners, application teams and data leads.
The operating-model question
Who is accountable when a model informs a decision, and what exists afterwards to show it? Asked by asset directors, heads of risk and procurement panels.
Where it is answered
The four product pages. AI Engine, AI Smart Data, Assist and AI Crew each set out their own procedure.
The five routes above, and the assurance registers on the governance page.
Who signs it off
The platform owner, against a deployment and a configuration.
A named decision owner per decision type, agreed at scoping.
How it fails
A tool is bought, deployed, then used outside any boundary because nobody set one.
A policy is written that no tool implements, so oversight exists on paper.
Constant on all five routes
Four rules that hold whichever tool is in play
On a MaxIron-managed estate the order is fixed: the system proposes, work is classified by consequence rather than confidence, a named person decides, then something changes. Maximo stays the system of record.
- Bounded context
- Inputs are restricted, per stream of work, to operational sources your organisation has approved for that purpose. The answer to what the model could see is a written list.
- Separated engines
- Generative stages handle variable language: drafting, summarising, triage support. Deterministic checks handle fixed-outcome requirements, including compliance-critical calculations and structured data conversions.
- Often read as Two interchangeable ways of arriving at the same answer.
- Explicit gates
- Validation, review and release are separate stages. A run can start from a request, a schedule or an event, and higher-impact work stops and waits for a name.
- Named accountability
- Material decisions stay owned by named roles, with the run record retained for audit and procurement. Confidence in an output is never a permission to act on it. The artefacts are catalogued as A1 to A4.
The tools, once
Four product pages carry the procedure detail
- Product MaxIron AI Engine
Holds the modes, the review gates and the run record behind every governed workflow.
- Product MaxIron AI Smart Data
Proposes classification, hierarchy, duplicate and enrichment changes for analyst review before any load.
- Product MaxIron Assist
Answers a Maximo user from their own organisation procedures, under the permissions that user already holds.
- Product MaxIron AI Crew
Assists inside IBM MAS Manage and hands higher-impact execution into a governed workflow.
Enterprise AI for asset management, frequently asked questions
- What does MaxIron mean by Enterprise AI in asset management?
- AI inside day-to-day operations with named accountability: planning decisions, data quality, support response, and evidence a risk function can review. The test is whether a named person can explain the decision that followed a model output.
- Who is accountable when a model informs a decision?
- A named person in your organisation who holds the authority to take that risk. MaxIron holds the operating discipline, the controls and the evidence trail. Those names are agreed during scoping rather than identified during an audit.
- How do you control hallucination and context risk?
- Context is scoped to approved sources, higher-risk steps require explicit review, and deterministic checks are used where the outcome must be exact. We publish no accuracy figure. The number that matters is how many consequential changes reached the estate without a person, and that number is zero.
- How does this fit IBM Maximo and MAS?
- MaxIron software and services complement IBM Maximo and MAS. Maximo remains the system of record for everything written, and we strengthen the data quality, decision speed and governance around it.
- What if more than one route applies to us?
- Most estates are on two at once. Where the asset data behind a decision will not carry it, decision-ready data comes first. Where assurance is the blocker rather than the data, start at governance.
Bring one decision you want improved.
Name one operational decision that takes too long, gets argued about, or cannot be evidenced afterwards. We will say whether AI helps it, whether your data supports it yet, and which role would have to own the outcome.
Bring this to the first call
- One recurring operational decision, and roughly how long it takes today
- The role that would approve the outcome, and whether that person has the time
- An honest view of the asset data behind that decision, including what you know is wrong with it
- The last question your audit, risk or procurement function asked you about AI