Aircraft MRO operations supported by digital technology and data | DXC Technology

Article | September 22, 2026

Quote the MRO risk before the aircraft enters the hangar

Costly mistakes can occur before a mechanic even touches an aircraft, such as when an aircraft MRO quote underestimates the risk.

Maintenance, repair and overhaul (MRO) proposals are often treated as commercial documents. In reality, they’re operating plans, capacity forecasts and margin commitments.

Added to which, estimators invariably find themselves between a rock and a hard place. Price too high, and the work flies south. Price too low, and the business inherits months of cost leakage and customer tension.

Getting the balance right is critical because the current market has less room for error

Oliver Wyman calculated that global MRO demand reached $136 billion in 2025 and could approach $193 billion by the end of the decade, driven by older fleets, higher utilization parts and labor pressures, flagging potential bottlenecks and pricing volatility.

Consequently, the boardroom agenda is changing. It’s no longer about whether AI can make maintenance smarter in the abstract. Instead, executives must work out where better prediction can protect revenue and improve confidence before the aircraft arrives.

Margin is an issue before maintenance begins

A heavy maintenance visit is full of uncertainty. Records may be incomplete. Aircraft of the same type can carry very different histories: whereas one may have spent years on short-haul cycles in harsh conditions, another may have flown longer sectors in less punishing environments.

That's why aviation MRO quoting risk is becoming a strategic capability. Oliver Wyman’s 2026 analysis says that the previous year’s material costs exceeded forecasts by 100 to 200 basis points, engine shop visits are taking 200 days or more for many narrowbody operators and two-thirds of MRO consultants struggle to find qualified technicians.

Experienced teams still matter enormously, but they're being asked to make calls from larger data sets, with tighter capacity and more volatile input costs. AI's premier value lies in predictive analytics that give experts a stronger evidentiary base for the commercial decisions that shape the work — and for the risk management those decisions demand.

Better quotes start with better risk signals

The most useful AI applications in MRO aircraft maintenance don't always look spectacular; some sit quietly inside pricing, planning and contract initiation. And that's exactly why executives should pay close attention.

A better quote does three things:

1.Catch defects earlier

Identifies likely defects earlier.

2.Sharpen estimates

Improves estimates for labor, tools, assets and turnaround time.

3.Clarify opportunity value

Gives the business a clearer view of which opportunities are attractive, risky or need different terms.

Real-world benefits

DXC’s work with a leading aircraft maintenance company clearly demonstrates this opportunity. The Germany-based company provides worldwide MRO services to its parent company and other international airlines. The client wanted to test whether AI models could predict potential defects for sales quotes, using historical MRO events, previous proposals and other sources. The result was a cloud-based sales quote application used by employees to prepare proposals and improve the planning basis at the start of contracts.

The outcome is important because the benefit isn’t a model score; it’s a better decision. The application helps the company develop more accurate proposals, present more competitive bids, improve the probability of winning contracts and plan the work more effectively at the outset.

Why this is an operating model decision

It’s tempting for the C-suite to view this as a sales tool or a maintenance analytics project, but that’s too narrow.

AI-enabled aviation MRO quoting changes an aircraft MRO business. Sales, engineering, finance, supply chain and operations need to share a common view of risk management. The quote should carry assumptions that can be tested as more data arrives. Then, when work begins, the business should compare predicted defects with those that materialize and feed those insights back into the next bid.

That loop shifts AI from experiment to performance

DXC Xponential is relevant here because it frames AI as a repeatable operating approach, that connects people, processes and technology, enabling organizations to move from isolated pilots to outcomes at scale. We support this with 50,000 full-stack engineers and AI-first facilities across six continents.

DXC OASIS tackles the same executive issue from an operations perspective. Mission-critical enterprises need a live view across systems, signals and decisions. For MRO leaders, that matters because quoting, capacity, parts availability, customer commitments and IT resilience remain connected in practice, even when they operate in different systems. It’s tempting for the C-suite to view this as a sales tool or a maintenance analytics project, but that’s too narrow.

Aerospace examples point to a broader pattern

The aircraft MRO use case is part of a wider aerospace shift: using AI and data to improve decisions with operational consequences. DXC helped American Airlines apply AI, machine learning and data analytics to improve prediction accuracy for aircraft touchdown times and runway arrivals.

It also supported Textron with AI-powered chatbots, self-service and automation aimed at reducing service desk tickets by 20% across a global employee base.

The management learnings are consistent. Aerospace companies gain value when AI is applied to decisions that constrain performance (e.g., when will an aircraft arrive, how can employees get support faster and what maintenance risk is embedded in this bid?)

Put this knowledge to work

MRO leaders should begin by identifying where uncertainty most often turns into margin erosion. In many organizations, that will be the quote-to-contract handoff: the moment when sales ambition becomes an operational obligation.

The next step is to assemble existing data: maintenance events, defect histories, proposal records, aircraft usage patterns, parts availability, labor assumptions and turnaround performance. Then apply AI to a focused business question, prove whether prediction quality is good enough to change decisions and build governance around how people use the insight.

Demand is growing as downsides deepen

So, the real game-changer is to treat AI-powered aviation MRO quoting as a strategic tool for margin control and cost optimization, not just another tech trial.

The fact is, MRO providers that understand risk earlier, price with precision and map out the work before the aircraft even enters the hangar will always have the edge.