AI Readiness Assessment for Maximo & EAM Programs
Before you invest in AI capabilities, answer these questions honestly. The gap between AI that delivers and AI that disappoints almost always begins here.
Best for: EAM program owners, operations leaders, IT leads, and reliability teams evaluating AI or MAS analytics capabilities
Why This Assessment Exists
Maximo Application Suite includes robust AI capabilities — asset health insights, predictive maintenance, condition monitoring, and optimization models. But they only work if the ground beneath them is ready.
Getting value from AI in EAM is rarely a technology problem. Organizations that struggle face a foundation problem: data that isn’t trusted, systems that weren’t designed to work together, governance that doesn’t hold, and use cases that were never tied to a specific business outcome.
This assessment is built around three diagnostic questions from Maven’s field experience. Work through each section honestly. The results will tell you where to focus before — or alongside — any AI investment.
Section 1 — Do You Trust Your Asset and Failure Data?
AI amplifies what already exists in your data. If the foundation is fragmented, AI amplifies fragmentation.
Rate each statement 0–2: 0 = Not true, 1 = Partially true, 2 = Consistently true
| Statement | Score (0–2) |
|---|---|
| Asset records are accurate, consistently structured, and actively maintained across all sites. | |
| Failure codes are applied consistently and reflect actual failure modes — not generic catch-alls. | |
| Work orders are closed out with complete, accurate data — including failure descriptions, labor, and downtime. | |
| Asset hierarchy is consistent across sites and reflects how equipment is actually organized and operated. | |
| When leadership asks for a reliability report, teams trust the data behind it enough to act on it. |
Section 1 Score: _____ / 10
| Score | Interpretation |
|---|---|
| 7–10 | Strong data foundation. AI has reliable inputs to work from. |
| 4–6 | Partial foundation. Targeted data improvements should run in parallel with any AI initiative. |
| 0–3 | Foundation work is the priority. AI investment before this is resolved will underperform. |
Section 2 — Are Your Systems Designed to Work Together Over Time?
One-off integrations create technical debt. AI capabilities in MAS require systems that share data reliably — not just once at go-live.
Rate each statement 0–2: 0 = Not true, 1 = Partially true, 2 = Consistently true
| Statement | Score (0–2) |
|---|---|
| Maximo integrations with ERP, finance, and operational systems are stable, maintained, and documented. | |
| Sensor or IoT data feeds (if present) are connected to asset records in a structured, consistent way. | |
| Integration architecture was designed for scalability — not just to solve an immediate point problem. | |
| When systems are updated or upgraded, integrations are tested and validated as part of the process. | |
| There is a clear owner responsible for integration health — not just the original implementer. |
Section 2 Score: _____ / 10
| Score | Interpretation |
|---|---|
| 7–10 | Integration architecture supports AI deployment. Data flows are reliable enough to build on. |
| 4–6 | Integration gaps exist. Map them before expanding AI scope. |
| 0–3 | Integration stability is a prerequisite. Address architecture before introducing AI complexity. |
Section 3 — Do Your Teams Know How Insights Will Drive Decisions?
AI that generates insights no one acts on does not deliver value. The use case must be tied to a specific decision before the model is built.
Rate each statement 0–2: 0 = Not true, 1 = Partially true, 2 = Consistently true
| Statement | Score (0–2) |
|---|---|
| Each AI use case being considered is tied to a specific, named business outcome — not just a capability. | |
| The teams who will receive AI-generated insights understand how to interpret and act on them. | |
| There is a defined governance process for reviewing AI recommendations before they affect maintenance decisions. | |
| Leadership has defined what "success" looks like for AI — in operational terms, not just platform metrics. | |
| AI capabilities are positioned as supporting the asset lifecycle strategy — not replacing the need for one. |
Section 3 Score: _____ / 10
| Score | Interpretation |
|---|---|
| 7–10 | Use cases are well-defined and teams are prepared to act on insights. |
| 4–6 | Use case definition needs sharpening. Clarify decision ownership before scaling. |
| 0–3 | Use case work is the priority. Without clear decisions to support, AI generates noise, not value. |
Overall Score Summary
| Section | Score | Max |
|---|---|---|
| Section 1 — Data Trust | 10 | |
| Section 2 — Integration Architecture | 10 | |
| Section 3 — Use Case & Decision Readiness | 10 | |
| Total | 30 |
| Total Score | Interpretation |
|---|---|
| 22–30 | Ready to scale. Your foundations support meaningful AI deployment. Focus on prioritizing use cases by business impact and executing with governance. |
| 14–21 | Proceed with intention. You have real capability but identifiable gaps. Run foundation improvements in parallel with a focused, limited AI pilot. Don't wait for perfection — but don't scale prematurely either. |
| 0–13 | Foundation first. AI investment at this stage will underperform. The highest-ROI work right now is data quality, integration stability, and use case clarity. Get those right and AI becomes significantly more powerful. |
Priority Action Planner
Use your lowest-scoring section to identify where to focus first.
| Priority | Area | Action | Owner | Target Date |
|---|---|---|---|---|
| 1 | ||||
| 2 | ||||
| 3 |
The Maven Perspective
AI is not a shortcut. It is a multiplier. The organizations that will benefit most are not those chasing every new feature — they are those investing in strong foundations, clear architecture, and disciplined execution.
The three questions in this assessment are not obstacles to AI adoption. They are the conditions that make AI adoption worth the investment.
Maven partners with organizations at every stage of this journey, from data foundation remediation and MAS implementation to AI use-case design and governance. If your scores reveal gaps you want to address, reach out to the Maven team.
→ Read the full article: AI in Asset Management