From optional to imperative: why AI is the next mandate in MRO

AI dominates the conversation in aviation circles. But it’s worth asking: where did it truly begin as a breakthrough? It didn’t start with marketing hype or startup pitch decks. The real catalyst was the aircraft themselves.

Modern aircraft generate massive volumes of operational data and have evolved with increasingly automated, efficient systems. But as information collection scaled up, so did operational complexity. Flight decks and dashboards became rich with insights, but overwhelming to decode. What was needed wasn’t more data, it was clarity. That’s where AI entered the picture, to make sense of the information overload.

In the early 2010s, engineers at GE and Rolls-Royce began embedding sensors and building algorithms; not to replace the seasoned judgment of maintenance crews, but to enhance it. The goal wasn’t automation, it was foresight. These systems could catch what the human eye might miss: a shift in vibration here, a thermal inconsistency there.

Initially, the aviation industry was cautious with AI. Industry regulation and the relentless focus on safety naturally urged caution. This was especially true with business aviation. Smaller fleets, high-touch service models, and conservative investment strategies made AI feel like an outsider’s tool. But the industry continued to advance.

Now, MRO managers are increasingly vocal about the challenge of managing data-rich environments. As systems generate more inputs and dashboards grow in complexity, there’s a clear desire for tools to cut through that noise. AI is seen not just as a means to process information, but as a way to elevate decision-making by surfacing what truly matters, quickly, clearly, and reliably. It has now become a matter of operational necessity.

There’s a point in every innovation cycle when the question shifts from “should we explore this?“ to “can we afford not to?“

Just like GPS, once a novelty and now a necessity in every cockpit, AI is quickly reaching that threshold in MRO. The shift is no longer theoretical.

Across the sector, MROs are facing a convergence of pressures. Firstly, the problem of an ageing workforce. As veteran technicians retire, proactively digitising their expertise helps preserve essential knowledge. When combined with AI, it can play a pivotal role in guiding and training the next generation of technicians—supporting decision-making through contextual insights derived from experiential knowledge.

The tightening of regulations is another factor. Digital compliance is no longer optional; it’s expected. For MROs still reliant on fragmented or paper-heavy workflows, the risk of audit failure increases every year. AI helps standardise compliance tasks and streamline data traceability to minimise error and audit risk.

Another area to consider is a shift in customer expectations. Today’s clients demand real-time updates, instant answers, and seamless service experiences. AI enables instant access to service status, powers digital logs, and streamlines communication for faster, more transparent responses.

Lastly, the not so small matter of financial pressures from downtime, overtime, inventory waste, and rework erode margins. AI could reduce these risks through smarter scheduling and resource use, while integrating with existing systems.

Despite the momentum, not all MROs are moving at the same pace.

Larger MROs, particularly those backed by airlines or OEMs, have the infrastructure and risk tolerance to pilot AI tools, and many already are.

But smaller, independent shops, with tighter budgets and fewer staff, face tougher trade-offs. Ironically, these are the very operations that stand to benefit the most from AI’s promise: less paperwork, better planning, fewer errors.

Without clear, accessible on-ramps to adoption, we risk creating a two-speed industry, not because one side is more capable, but because the other lacks timely access to the resources and guidance required to act. Fortunately, many ERP vendors are now building modern, bespoke AI tools optimised to work with smaller, private datasets, without requiring data sharing across companies. Built-in safeguards ensure data remains siloed and secure, making these AI solutions more accessible to all.

Even when the benefits of AI are clear, resistance can persist. It’s often rooted not in awareness, but in identity.

Many in business aviation take pride in deep knowledge, personal relationships, and doing things the right way. The fear isn’t that AI won’t work, it’s that it might dilute the human element.

But here’s a different lens. AI functions more like a trusted assistant, quietly supporting the expert rather than replacing them. Just as spell checkers and grammar tools flag issues without changing the author’s intent, AI helps technicians and managers see potential issues and improve outcomes while preserving control and judgment. In doing so, it enhances speed and accuracy while reducing errors.

Holding off on AI might feel like preserving tradition. But the real risk is letting tradition drift out of alignment with the needs of modern operations. Success isn’t just about adopting technology, it’s about aligning people, processes, and leadership expectations to support it.

Delaying AI adoption doesn’t just hinder day-to-day operations: it’s a strategic risk. Each missed optimisation reflects more than inefficiency; it signals hesitation. And in today’s digital-first ecosystem, hesitation quickly begins to look like decline. Clients and OEM partners increasingly interpret digital maturity as a proxy for operational readiness. An MRO that struggles with visibility or traceability may not just lose a bid, it can erode confidence in long-term reliability. In a field built on trust, that kind of doubt is hard to recover from.

Early adopters are already shaping the standard. They’re demonstrating to clients, auditors, and partners that they are stable, scalable, and future-ready. So, when does AI stop being optional? When not adopting it says more than adopting ever could.

AI’s entry into business aviation has been steady, evolving through practical use cases like predictive diagnostics and maintenance scheduling. The shops using it aren’t chasing trends. They’re leaning on tools that simplify the messiest parts of their day, from quoting to compliance, with greater precision and speed.

Delays may feel minor in isolation, but their cumulative effect shows up in missed opportunities, slower response times, and shrinking trust. Institutional hesitance doesn’t just slow adoption; it undermines competitive readiness.

By the time the shift feels safe, AI may already be standard. And as AI continues to advance, it’s likely to integrate with broader systems like predictive supply chains and fleet-wide learning platforms, reshaping how MROs plan, not just respond.

As leaders, we have a responsibility not just to respond to change, but to guide it. In an era defined by visibility and velocity, AI enables the kind of scalable discipline today’s aviation business demands.