Turning aircraft engineering into an AI system
I led product discovery & definition, workflow design, and UX/UI for a major airline’s AI maintenance engineering platform. Shipped two modules that trained an AI to assist aircraft-level analyses, contributing to a projected ~$20M in-year savings target.
Duration
3 months
Role
Product Designer
A maintenance program that hardly questions itself
Uprise Airline manages hundreds of inspections and checks across its fleet.
When addressing or preventing aircraft failures, the aviation industry relied on defaulting to add tasks for years. With slow regulatory approval process for any changes, the industry favored overloading safety checks without analyzing the issue. There was no tool to make an informed case for prioritizing the right tasks.
I admit that we have a bias towards action and pick solution before the problem is well known.. That makes it difficult for Maintenance Programs to execute on multiple conflicting workcards.
— Director of Reliability Engineering
Getting from “this task might be ineffective” to a program change meant aligning three departments across different data, recurring meetings, and manual document creation for federal review.
Key departments in Aircraft Maintenance and Safety
I know which systems are causing problems. But I can’t escalate the problem and solutions fast enough to act on them before they become bigger issues.
Key responsibilities
Pain points and needs
Measurement of success
- Mechanical Reliability Index (MRI) trends
- Cost of operational events
- Aircraft-on-ground frequency
- Engineering project ROI
Fig 1. Key departments and pain points
We worked with 20+ engineers across the departments to define requirements for AI-assisted failure mode analysis and monitoring workflow. The new process could reduce unnecessary checks, surface real problems faster, and cut delays and cancellations.
Scaling the engineering decision to a machine-learning workflow
Discovery revealed a process that worked but couldn’t scale. The existing analysis flow was manual and delayed, and context easily got lost between departments.
Our mission was to enable collaborative analysis and capture observations as model training data. The analysis flow split into two paths based on journey stage and owner.
Types of Fleet Analysis Modules
Rank underperforming tasks, investigate the worst ones, and produce the approval package
Measurement of success
- Task removal or interval extension rate
- Time to complete a review cycle
- Approval package completion rate
- Cost saved per approved task change
Ownership
Maintenance Programs (Primary)
Fleet Engineering (Secondary)
Fig 2. Two analysis modules enabled in the tool
Future State Failure Investigation Workflow
- Support root cause investigation with historical data and failure modes
- Act as an expert for the fleet during failure analysis in Working Group sessions
- Lead the failure investigation process; cross-reference failure mode paretos, findings, aircraft data
- Validate issues from existing tasks — execution feasibility, chronic patterns
- Align on potential solutions in Working Group sessions alongside Reliability and Fleet
Fig 3. Redesigned failure investigation stages and description for each stage. the original workflow and information architecture.
Tying failure insights to maintenance tasks
Though analysis was the most critical stage of the workflow, it was not the centerpiece of the UX. It was the interaction for reviewing log entries, confirming or updating the AI-suggested failure classification, and improving the model’s ability to classify next time.
Task Details
Pressure Regulation Health Check
Applied to
A320-000
Function Code
0
Task Code
0001
Workcard
0000-0000
Sample Logs (5)
| Defect description | Date | Failure Mode | Functional Failure |
|---|---|---|---|
| #2 engine bleed-air valve — no indication | 05/30/2023 | Engine #2 bleed-air valve stuck open | Engine #2 bleed-air valve stuck open |
| #2 engine bleed-air temperature abnormal | 05/30/2023 | Bleed-air temperature regulator leaking | Bleed-air temperature regulator leaking |
| #1 engine bleed-air temperature abnormal | 05/30/2023 | Temperature regulator not controlling the fan-air valve | Temperature regulator not controlling the fan-air valve |
| #2 engine bleed-air pressure fluctuating | 05/30/2023 | Bleed-air temperature regulator leaking | Bleed-air temperature regulator leaking |
| #1 engine pre-cooler outlet temperature high | 05/30/2023 | Fan-air valve fails to open | Fan-air valve fails to open |
Fig 4. The task detail view after the AI model has populated failure type and failure mode columns. Engineers confirm correct suggestions and correct wrong ones. Every correction feeds back into the model.
Assessing task and failure performance
Task and failure classification, the two entry points for analysis workflow, were paired with a dedicated view to help engineers determine if a task or failure type was worth investigating.
Task Detail
Health Check — Pressure Regulation
Reference task ID 15-2020, last reviewed 2 months ago
TEI
30
LowEffectiveness
MRI
60%
Avg yield for task
Log rate
70%
70% Sample Pool
Flight cycles
88%
75% Sample Pool
Non-routine findings by Category
Logs (4)
| Defect | Date | Yield | Failure Mode |
|---|---|---|---|
| #2 Engine Surge Shutdown (Prev.) | 06/25/2023 | 9.5% | Air-Bleed Reroute Open |
| FM Cocked subject Ctrl Mtr C… | 06/08/2023 | 3.2% | TCT leakage |
| #2 engine came back and need… | 06/16/2023 | 34% | Temperature Control (TCT)… |
| APU/Airpack Nearby Refurbish… | 05/28/2023 | 56% | APU bleed check-valve fa… |
Fig 5. Task and failure detail views allow quick assessment of whether cross-departmental analysis is needed
From first task reviewed to MM in identified savings
A few weeks into the launch, analysis that previously took months of manual log review began producing actionable findings in days. We saw three significant impact cases from the tool:
1. $620K savings from compression check removal
A 11-year-old gas compression check was consuming 6K man hours annually and surfaced as a candidate for removal. The task is now reviewed and successfully removed without impact in passenger safety or delays, producing $620K in in-year labor savings.
2. $3.3M savings from yield optimization
Through aircraft-level analyses, yield optimization opportunity was identified across 64 aircraft for 2025, including task removals and execution interval extensions.
The AI classification layer the engineers had been actively reviewing and labeling reached ~80% labeling accuracy across 600K+ log entries by launch, and continued to improve as more functional failure tags were confirmed in the UI.
3. 80% accuracy rate and evolving
The AI classification engineers helped label reached ~80% accuracy across 600K+ log entries by launch and continues to evolve as more labels are validated and updated in the tool.
What I’d carry into my next projects
- Show uncertainty honestly. “The model flagged this as 62% likely” is more useful than a green check.
- Tie every disagreement to a measurable artifact (a tag change, an approved package). Feedback only compounds if someone can see it shift next week’s output.