Enterprise AI · Start here
Use AI to prepare asset decisions and operational work
MaxIron applies AI to condition review, asset-data correction and support preparation around IBM Maximo and MAS. We connect approved information, configure the assistance and record each review. Engineers, data stewards and change authorities approve the outcomes that affect the estate.
Operational examples
Three decisions AI can help prepare
These illustrative scenarios show how MaxIron scopes assistance around a business question. Approved information, a review owner and a retained record are part of each workflow.
Turn available evidence into a decision people can act on.
Condition signals, work history and procedures become useful when they answer an operational question. We scope AI assistance to assemble relevant evidence and prepare options, with sources and uncertainty available for review. Your authorised people retain the decision and approval.
Explore a worked condition decision01Can this pump remain in service until the planned outage?
- Information used
- Vibration trends, failure history, current duty and the planned outage date, linked to the asset record.
- AI assistance
- Assemble a cited condition summary, identify missing observations and draft the intervention options for review.
- Human decision owner
- The reliability engineer recommends an option; the engineering authority accepts the operating risk and the planner approves any work.
- Control and record
- The reviewer checks source timestamps and uncertainty. Approved work and its rationale are recorded in Manage.
02Which asset records need correction before reliability analysis?
- Information used
- An approved asset-class export, the classification standard, location relationships and work history.
- AI assistance
- Identify inconsistent classifications and duplicate identifiers, then prepare field-level correction proposals with the source evidence.
- Human decision owner
- The asset-data steward approves each proposal against the standard; the platform owner authorises the import.
- Control and record
- The scope is limited to approved records. Validation and a recorded acceptance precede each update; rejected proposals remain visible.
03What evidence does a support engineer need before proposing a change?
- Information used
- Approved runbooks, environment inventory, incident history and relevant logs within the engineer’s access permissions.
- AI assistance
- Summarise the incident, cite the applicable procedure and draft a remediation sequence with validation steps.
- Human decision owner
- The service engineer checks the diagnosis; the platform owner and change authority approve execution within the agreed change process.
- Control and record
- Execution permissions are separate from drafting. The run retains sources, reviewer edits, approval and the resulting change record.
Decision register
Five decisions, with the operating condition named
Data standards precede modelling where the record cannot carry the decision. Review gates precede deployment where assurance approval is outstanding.
Do we intervene on this asset now, or hold to the next planned outage?
Trace one pump intervention →Operating context
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.
Planning consideration
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?
Set the decision-ready data standard →Operating context
Asset classification and work history need an agreed standard before model evaluation can begin.
Planning consideration
A correction programme needs a named standard owner and recurring measurement to keep new records consistent.
Which analytics capability earns its place next?
Sequence the analytics investment →Operating context
Monitor, Predict or Health adoption requires a sequence tied to data readiness and operational priorities.
Planning consideration
Agreeing a Predict scope before failure history and a comparable asset population support model evaluation.
What does this look like day to day on an estate somebody else operates?
Define the operated AI service →Operating context
A live AI-assisted workflow needs agreed review coverage, access boundaries, evidence retention and handover arrangements.
Planning consideration
Review capacity, a deputy and escalation cover form part of the operating scope.
What will our auditor, risk function or public-sector buyer ask us to produce?
Set review gates and assurance records →Operating context
Risk, audit or procurement requires evidence of approved scope, review authority and the controls demonstrated before release.
Planning consideration
Supplier security evidence and workflow-specific model controls answer separate assurance requirements.
Delivery responsibilities
Software capability and operational accountability
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
Assist: chat in Manage, MCP from an external tool, governed role work in Portal, and data proposals a person accepts.
The named decision owner, review gate and assurance records agreed for that work.
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.
Operating rules
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.
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. Acceptance tests use representative records and check sources, omissions and incorrect proposals before deployment.
- 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.
- How do we sequence more than one AI decision?
- Start with the dependency that governs the others. Where the asset data cannot carry a decision, decision-ready data comes first. Where assurance blocks deployment, establish the review gates and evidence records first.
Bring one decision you want improved.
Bring a recurring condition review, asset-data correction or support workflow. We assess the available sources, define the assistance and human approval steps, and agree a bounded first trial.
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
- The known gaps in the asset data behind that decision
- The last question your audit, risk or procurement function asked you about AI