MRO-PRO founder Scott Wells took part as a guest panellist at The Economist’s Keeping Europe Flying event in London this month, where more than 50 senior leaders from across the UK aerospace industry gathered to discuss the future of aviation maintenance, resilience, and operational performance.
Hosted by The Economist’s Industry Editor, Simon Wright, the session addressed one of aviation’s most pressing challenges: how to maintain operational resilience in the face of growing demand, constrained capacity, and increasing complexity.
Scott brought a unique and practical perspective to the panel, as a former manager of MROs, now an AI trailblazer in MRO, his expert insight challenged conventional narratives around capacity and highlighted a more fundamental issue: how the industry plans, learns, and collaborates.
Scott also made the case for keeping the conversation grounded in practical outcomes rather than broad AI themes. He argued that the most valuable application of AI in MRO is helping organisations predict what is likely to happen before an aircraft arrives, including probable defects, parts demand, tooling needs, and emerging workload.
This blog captures the key themes from his contribution and what they mean for the future of MRO.
Moving Beyond the “Capacity Problem”
A recurring theme across the discussion was the widely cited issue of MRO backlogs.
Rather than viewing backlogs purely as a capacity constraint, Scott highlighted that many delays are actually driven by planning inefficiencies and gaps in maintenance intelligence.
In practice, delays are often caused by:
- Incomplete or inconsistent work pack forecasting
- Late availability of parts, tooling, or services
- Unanticipated defect emergence
- Repeated resetting of planning assumptions across operators
The implication is clear:
The industry does not just need more capacity; it needs better predictability.
The Real Bottleneck: Before the Aircraft Enters the Hangar
One of the most impactful insights shared was that inefficiencies in base maintenance are typically determined well before execution begins.
If critical elements such as parts availability, tooling, access equipment, and probable defect scenarios are not accurately forecast ahead of induction, then even the most skilled technicians cannot compensate for the disruption.
This shifts the focus from hangar productivity to pre-induction planning quality.
Improving work pack completeness and accuracy represents one of the most immediate and scalable opportunities to:
- Reduce turnaround times
- Improve technician productivity
- Unlock latent capacity within existing infrastructure
The Industry Learning Problem
Looking more structurally at the MRO ecosystem, Scott highlighted the issue that
the industry is not learning effectively at scale.
Today, valuable maintenance knowledge is often:
- Locked within individual planners and engineers
- Tied to specific customer AMP (Aircraft Maintenance Programme) structures
- Fragmented across organisational silos
As a result:
- Knowledge is lost when individuals move on
- MROs frequently start again when onboarding new operators
- Planning remains heavily dependent on individual experience
To address this, he emphasised the need to align maintenance activity more closely with OEM MPD (Maintenance Planning Document) intent, enabling knowledge to become reusable, scalable and systematically embedded.
Federated Learning: A New Model for Industry Collaboration
A standout concept from the panel was the idea of federated learning across MROs.
Today, MRO organisations effectively solve the same problems on the same aircraft types, with similar defect patterns but do so in isolation.
Federated learning introduces the possibility of:
- Sharing anonymised operational intelligence
- Aggregating defect and execution insights across organisations
- Improving forecasting accuracy at an industry-wide level
Crucially, Scott emphasised that this can and should be achieved without exposing commercially sensitive data.
The potential benefits are significant:
- Faster onboarding of new aircraft types
- Reduced planning uncertainty
- Improved resilience across the entire ecosystem
By 2030, the panel agreed that competitive advantage may shift toward those MROs that learn fastest, not necessarily those with the largest capacity.
From Reactive to Predictive: The Role of AI in MRO
While AI is often discussed in abstract terms, Scott grounded its value firmly in operational reality. The most impactful application of AI in MRO is not automation for its own sake but predictive planning.
Key capabilities within MRO-PRO’s bespoke MILE AI-driven platform include:
- Forecasting probable defects before aircraft induction
- Anticipating parts, tooling, and service requirements
- Identifying emerging workload patterns earlier
- Improving work pack completeness
This represents a fundamental shift from reactive maintenance planning to probability-driven decision-making. The result is not just efficiency gains, but a step change in turnaround time reliability, capacity utilisation and operational confidence
Building a More Resilient MRO Ecosystem
The discussion at “Keeping Europe Flying” made one thing clear:
the future of MRO will not be defined solely by physical capacity expansion.
Instead, resilience will be built through:
- Better planning intelligence
- Stronger cross-industry collaboration
- Scalable learning models
- Practical, outcome-driven AI adoption
For MRO-PRO and the wider industry, this represents both a challenge and an opportunity. Those MROs that can connect planning, learning, and execution into a unified system will be best positioned to support the next decade of aviation growth.
In his closing remarks, Scott highlighted how better forecasting, predictive maintenance, and shared operational intelligence can unlock hidden capacity and improved efficiencies even before an aircraft enters base maintenance.