AI Readiness & Maturity Assessment

This assessment provides an AI-assisted advisory snapshot of your organisation’s readiness to adopt and benefit from AI-based predictive analytics and equipment monitoring systems in operations and maintenance.

About You

Name Work email Role Organisation / Site

Rate each statement from 1 (strongly disagree) to 4 (strongly agree) based on your current reality.

1. Perceived Business Value of AI

Senior leaders in our organisation see AI-based predictive analytics and monitoring as important for improving operational performance (production, reliability, safety or cost). There are clear examples or business cases internally that show how AI could realistically benefit our operations or maintenance. When AI-based initiatives are discussed, they are linked to measurable outcomes (e.g. deferment reduction, cost, reliability, safety) rather than only “innovation”. If you have an example (or a missed opportunity) where AI could have added value, please describe it briefly. (Optional)

2. Ease of Use & User Understanding

The user interfaces for AI-based tools (dashboards, alerts, reports) are intuitive for operations and maintenance staff. Key users have received sufficient training to navigate and use AI-based tools confidently in their day-to-day work. Users understand, at a high level, how AI-based insights are generated (for example from sensor data, events and history), even if they are not data scientists. What is the most common complaint or difficulty users have when using AI-based tools or dashboards? (Optional)

3. Trust & Confidence in AI Outputs

When AI-based alerts or recommendations are issued, users generally trust them enough to investigate or consider action. We have had situations where AI-based predictions or recommendations proved accurate and increased user confidence. Users feel comfortable challenging or overriding AI recommendations when they appear incorrect or misaligned with context. Briefly describe an incident where AI-based output was trusted (or not trusted) and what happened. (Optional)

4. Data & Systems Readiness

The operational data required for AI (sensor readings, events, work orders, failures) is generally complete and of acceptable quality. Different systems (for example SCADA/DCS, historian, CMMS, condition monitoring tools) are integrated sufficiently to support useful AI insights. When we receive AI-based insights, it is possible (at least for some users) to trace them back to underlying data or assumptions if needed. What is the biggest data-related obstacle to using AI effectively in your organisation? (Optional)

5. People, Skills & Way of Working

Key roles (for example engineers, planners, controllers) know how to incorporate AI-based insights into existing workflows and decision processes. We have enough internal capability (or access to external expertise) to manage and continuously improve AI-based solutions. Lessons learned from using AI in operations are captured and shared (for example through reviews, communities of practice, or training). Which roles or teams most need further development to work effectively with AI in your context? (Optional)

6. Leadership, Governance & Alignment of Expectations

Expectations about what AI can and cannot do in our operations are realistic and clearly communicated. Senior leaders actively support AI-related initiatives and follow through with resources and decisions. There is a clear governance structure (sponsors, owners, decision-makers) for AI initiatives in operations and maintenance. If AI projects have stalled or failed in the past, what was the main reason from your perspective? (Optional)

Overall View

In one sentence, how would you describe your organisation’s overall readiness to use AI in operations and maintenance? What is the single most important improvement that would increase your confidence in using AI-based predictive analytics or monitoring?

By submitting, you agree that your responses may be used to generate an AI-assisted advisory summary. This is not a performance guarantee and should be interpreted using your own professional judgement.