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Artificial Intelligence in use today within various parts of the aviation industry

By Anthony Kioussis
President, Asset Insight

AI applications in the aviation industry minimize aircraft down time by anticipating maintenance issues based on collected data.
Artificial Intelligence (AI) is often associated with robots able to mimic human actions and some level of cognitive reasoning. It is true that ad vanced robotics utilize various forms of AI, but what might surprise you is that you’re probably benefiting from the use of AI without knowing it.

As most of my colleagues at Asset Insight will tell you, I’m not the brightest bulb on the company’s informa tion technology chandelier. That being the case, I need things explained to me in layperson terms, offering me the ability (I hope) to explain how AI can enhance – and is already enhancing – planning and decision-making for business aviation managers.

Bear with me through the techno-speak that is about to follow and you’ll under stand its importance shortly (and it might even allow you to communicate with a millennial).

AI programming

AI utilizes programming that is able to combine vast amounts of data with high-speed computer processing and advanced algorithms (a set of instructions that are re peated in a sequence a specified number of times or until a condition is met) that are adjusted by the AI, allowing the software to learn automatically from patterns in the data and move closer to “reality” over time.

How has this helped you on a day-to-day basis? When you use Google, you’ll notice that your computer is offering you suggestions as you type. These suggestions are not random, but rather based on information that you, and others who have demonstrated similar interests, have found useful in the past.

Google is actually utilizing a search engine algorithm that learns what people want as they use it and then attempts to “guess” what the users are seeking. These days, if you notice many leading companies, they tend to make use of a customer engagement platform to get customized results for their businesses. It is like a data extraction process. Historically, the process used to establish, and allow for, data transference between computers has been accomplished through “REST APIs.” API stands for “Application Programming Interface.”

It is a set of rules that allow programs to talk to each other. The capability is created on a computer server to permit communication with other computers. REST, which stands for “Represen tational State Transfer,” is a set of rules that developers fol low when they create their API.

One of these rules states that you should be able to obtain a specific piece of data when you link to a specific web address.

Safran’s Nacelles, used in the production of the Airbus 330neo, uses VR to facili tate the immersion of engineers, technicians and operators. Wearing 3D viewing goggles, users can see full-size parts designed with CAD soft ware, or work on ergonomics and positions using virtual mannequins.

Aid in searching

What does all this mean in layperson terms? Let’s say you’re trying to find videos about Hawaii on YouTube. You open up YouTube, type “Hawaii” into the search area, hit enter, and you see a list of videos about Hawaii. A REST API works in a similar way.

You search for something, and you get a list of results back from the service you are com municating with. REST APIs have worked well. However, as people’s reli ance on technology has increased, the load on computing power has as well – in some cases exponentially, hence the reason certain websites seem to “crawl” (if not crash) when too many queries attempt to utilize the REST API si multaneously.

Other issues are that the form and content of information returned to a user’s query is predefined by the responder, and any changes to the API structure by that responder (the data path to the information the user is seeking to follow) can lead to applications not working.


To provide a place where any application can go to obtain whatever data it needs (read: standardization), and to deliver what any requestor is seeking, as opposed to fitting a requestor’s query into an existing bucket, a data query language called GraphQL was developed that has been used by Facebook since 2012 and was released to the public in 2015.

GraphQL is a powerful new API language and is somewhat similar to NodeJS SQLite (which you can learn more about on the Linode site). It can help optimize data storage and database management. But how does that translate to planning and decision-making advances for business aviation? Say that you’re managing a maintenance facility and the completion of a maintenance event is running ahead of schedule.

You could have your sales group search various databases for an aircraft requiring maintenance that could fit within the potential new slot. Alternatively, through the use of AI, your company’s program management system could have already communicated the potential capacity increase to Asset Insight.

Rather than waiting for the facility’s personnel to ask for the information, Asset Insight’s AI capability, and implementation of GraphQL and Asset Insight’s proprietary technology would automatically investigate which aircraft had upcoming maintenance requirements that could fit the available timeframe (based on the facility’s capabilities), and recommend the facility consider pursuing specific aircraft serial numbers to create additional revenue.

Rather than researching the correct aircraft operators to contact and allowing time to narrow the decision-making window for the facility and the potential maintenance client, the facility’s sales team now has targeted prospects to pursue and more time is available for both sides to plan and decide, improving efficiency and (potentially) increasing revenue. Moreover, aircraft companies may have highly trained employees (perhaps with the help of a VR Training Platform) in sales and other operations that may have a better mode of helping customers as well as skills to pursue potential clients to opt for their services.

Rather than forwarding a user’s request to a source that can only respond with the information it has available, GraphQL, a powerful new computer language, allows the user to access data from countless sources to match the detected information request and respond to a user’s query with an swers that are not constrained by the information available through any single data bucket.

Planning production or utilization

Let’s follow this thought process a bit further. Suppose your company manufactures aircraft components utilized in various maintenance events, and the need for such parts is based on aircraft flight hours and/or cycles. You could plan your production on general industry utiliza tion figures.

You could contact individual shops to learn what consumption levels they foresee for the next year. Or you could harness the power provided by AI’s ability to recognize the pattern of component use and allow it to recommend production level revisions based on changes in anticipated demand and your company’s inventory and existing production schedule. This can also enforce more efficient inventory management, where the number of products stored could be minimized to the bare minimum, based on the AI’s forecast of the need.

Utilizing REST API technology, thousands of connec tions between the API and data sources would need to be actively managed to ensure the communication stability necessary to provide an accurate and consistent response.

However, by virtue of GraphQL’s stability, the new API language, in conjunction with Asset Insight’s proprietary technology, allows for data monitoring directly from the database itself, then permits the data source to commu nicate directly with the user. Updates to the database do not inhibit the connection and accurate information flow between the user and data source is assured.

Think this is futuristic? OEMs today have the ability to monitor the engines and systems on many newer produc tion aircraft while they are airborne, allowing for parts to be prepositioned based on actual or anticipated compo nent failure.

Airlines are logistically able to take advan tage of such AI capabilities, and it won’t be long before business aviation finds ways to do so as well. What if you’re a frequent user of charter flights. You can use many capable systems to search and book a charter flight today.

But how much could your personal efficiency increase, and your travel expenses decrease, if your com puter’s AI, knowing your schedule, continuously moni tored charter deals available through countless sources and recommended bookings to you based on your needs.

Suppose the AI also told you that a better alternative ex isted, such as rail transportation – information you never requested and an option you never even considered.

DARPA’s Aircrew Labor In-cockpit Automation System is a tailorable, drop-in, removable kit that promotes the addition of high levels of auto mation into existing aircraft, enabling operation with reduced onboard crew. The system intends to reduce pilot workload, augment mission performance and improve aircraft safety. It aims to support execution of an entire mission from takeoff to landing, even in the face of contin gency events such as aircraft system failures.

AI in aircraft transactions

When it comes to aircraft transactions, entities taking advantage of this technology will benefit substantially at the expense of non-users.

For example, a large number of factors are employed by Asset Insight’s AI platform to de rive current trends and long-term Residual Values ranging from the economic outlook and market momentum to the projected future cost for specific metals.

The AI expands and refines these factors and proactively models (quite lit erally) millions of calculations involving parameters that aircraft owners, buyers and sellers have never considered. The ability to think in such complex terms is only some of the value created by AI.

GraphQL assists the AI process by acting as something akin to a combination traffic flow monitor, data compiler and information desk. Rather than forwarding a user’s request to a source that can only re spond with the information it has available, API systems utilize GraphQL to access data from countless sources to match the information request/flow patterns they detect, and they respond to a user’s query with answers that are not constrained by the information available through any single data bucket.

Also, as was mentioned earlier, chang es made at the data source do not break the API connec tion between the query originator and the responder. Although it will take time for GraphQL to demonstrate its full value to the business aviation industry, there are many reasons why entities will gravitate to it.

Asset Insight is already applying GraphQL technology in the API avail able to our partners, and we see great potential in how it can help our clients and our partners’ customers in the years to come.

Anthony Kioussis is President of Asset Insight, which offers aircraft valuation and aviation consulting services. His 40+ years of experience in aviation includes GE Capital Corporate Aircraft Finance, Jet Aviation, and JSSI.