CTO Jennifer Wimberly on Operationalizing AI in Aviation

In high-stakes industries like aviation, where every decision affects time, safety, and client experience, the role of AI extends far beyond automation. Today, intelligent systems are enabling these sectors to make faster, data-driven decisions across complex operational environments, from aircraft availability and dynamic pricing to compliance and logistics routing.

To explore this shift, we spoke with Jennifer Wimberly, Chief Technology Officer at Elevate Jet. In this conversation, she explains how her team is translating decades of aviation expertise into intelligent, AI-driven capabilities and what it takes to scale AI from experimentation to production. Wimberly also underscores the importance of grounding AI initiatives in clear business outcomes while avoiding hype-driven adoption.

Her insights provide a practical roadmap for leaders seeking to turn data, domain expertise, and emerging technologies into sustainable competitive advantage, making this interview a must-read for anyone shaping the future of AI-driven enterprises.

AI in Mission-Critical Workflows

Private aviation is a high-stakes environment. How do you define the boundary between automation and human oversight?

Wimberly: We are more focused on insight generation versus automation. The question we ask is not what can we automate, but how do we help the humans in this process, our clients, our colleagues, our operations teams, make decisions faster and with greater confidence. That is a fundamentally different design philosophy.

Automation requires real care in a highly regulated environment like aviation. The stakes are too high to remove human judgment from the equation. What we can do is make that judgment better informed and more precise by putting differentiated data and operational intelligence at its fingertips in real time.

Our commitment to operational safety, quality and service excellence is non-negotiable. It always has been. What the technology does is accelerate and sharpen that excellence, not replace it. The human touch is still at the center of everything we do at Elevate Jet, just now informed by intelligence that no competitor can replicate.

Moving from Strategy to Execution

Many enterprises sit on decades of operational data but struggle to activate it. What did you do to convert historical experience into structured, AI-ready intelligence?

Wimberly: Not all AI is the same, and that distinction matters enormously when you’re thinking about how to prepare your data. Generative AI thrives on unstructured data, text, documents, notes, transcripts. But the narrow AI and machine learning that powers Ruby requires something different. It needs clean, structured, consistent inputs. So, the work of making decades of operational data AI-ready was very hands-on, deliberate, and involved good old-fashioned data stewardship.

But there was a deeper shift that had to happen first, and that was learning to think about data differently, almost like a raw material. Not just a record of what happened, but an asset that could be refined and applied to serve clients in ways we had never been able to before.

That matters especially now because the Elevate Jet app represents a completely new way for us to interact with our clients and reach clients who may not have flown private before. The goal is accessibility, instant pricing, and real-time aircraft availability, all in one platform. To deliver that, we needed our historical intelligence to be structured so it could power those answers immediately and accurately. Decades of operational excellence had to become something Ruby could actually use, not just something we could point to.

Change management

How do you align AI ambition with realistic change management inside established organizations?

Wimberly: We actually try to avoid saying ‘AI change management‘ because the principles that make emerging technology adoption successful are not unique to AI. They never have been. What we have seen work and what we have seen fail across decades of technology transformation comes down to two things: do your teams deeply understand the business, and can they articulate business value?

Those are the differentiators. Not which tools they know or how technically proficient they are. The teams that build great solutions with emerging technology are the ones who can look at a business problem, understand it at a level that goes beyond the surface, and then communicate why solving it matters. That’s true whether you’re talking about AI, machine learning, quantum computing, or whatever comes next.

What we have seen time and again is that emerging technology adoption is deeply experiential. You cannot learn it in a classroom or a slide deck. You have to get your hands in it, in the context of your actual business, with your actual data and your actual clients. Organizations that try to shortcut that crawl phase almost always pay for it later.

At Elevate Jet, we invest in teams who are fundamentally curious problem solvers first. The technology is the enabler. The business outcome is the point. When your teams understand that, change management stops being something you manage and starts being something that happens naturally.

Governance Mechanisms

What governance mechanisms ensure AI recommendations remain safe, compliant, and commercially sound in high-stakes environments?

Wimberly: I have spent more time in rooms with legal, compliance, and ethics teams than I ever imagined I would as a technologist. But that experience shaped a design philosophy that I have carried directly into Elevate Jet, and it is this: before we design anything, we think about data use and ethics first.

We call it the ‘can we’ and ‘should we’ of our designs. Because often times you absolutely can use data to do something powerful and valuable for a client. But you have to stop and ask whether it is ethical and right to do so. Those are two very different questions and both of them have to have a good answer before we move forward.

Data privacy is something we take extremely seriously at Elevate Jet. Our clients trust us with their information, their preferences, their travel patterns. That trust is sacred. So we design with transparency and accountability built in from the very beginning, not bolted on at the end. Governance is not a checkbox for us. It is a design principle. And in a heavily regulated industry like aviation, where the relationship between operator and client is built on decades of earned trust, that approach is not optional. It is foundational.

Measuring ROI

How do you measure ROI when AI improves speed and efficiency but also reshapes decision-making workflows?

Wimberly: ROI in AI has two sides and you have to measure both. The hard side is quantifiable. Booking speed, pricing accuracy, conversion rates, new client acquisition. At Elevate Jet, what used to take hours of back and forth now happens in seconds. That is a measurable, defensible return that shows up in client behavior and business performance.

But the soft side is equally important and often undervalued. Does the client feel confident making a booking decision? Has friction been removed from their experience? And can our operations teams act faster on better information? Those outcomes do not always show up immediately on a balance sheet but they compound over time into something very powerful, which is trust.

The key is to define what success looks like before you start, not after. If you cannot articulate what you are trying to solve and how you will know when you have solved it, you are not ready to measure ROI. At Elevate Jet we connect every technology investment back to a specific client or business outcome. That discipline is what keeps us honest about what the technology is actually delivering versus what we hoped it would deliver.

Technical Debt

How do you prevent AI debt -technical, operational, or ethical – as adoption increases?

Wimberly: It starts with good sound enterprise architecture principles. One of the most important habits I have developed over my career is delaying ‘one way door’ decisions for as long as possible. In a landscape where AI is moving as fast as it is right now, locking yourself into irreversible architectural choices too early is one of the fastest ways to accumulate debt you cannot easily pay back.

Beyond that it requires disciplined application lifecycle management. And honestly this is an area where our entire industry is still forming its point of view, including us. We are all asking the same questions right now. Should AI models be inventoried and managed like applications? What does the lifecycle of an AI model look like versus a traditional application and how do those differ? When do you retire a model? When do you retrain versus replace?

These are not rhetorical questions. They are operational realities that every technology leader needs to answer and answer soon. We are fortunate that while our AI footprint at Elevate Jet is still manageable, I have access to great colleagues from diverse industries who are working through the same challenges. That cross-industry perspective is invaluable because no single industry has all the answers yet.

Reskilling Teams While Operationalizing AI in the Aviation

Has AI changed the role of your operations team – and if so, how are you reskilling them?

Wimberly: At Elevate Jet we have a relatively small, talented, and deeply tenured IT operations team and honestly their understanding of this business is gold. You cannot replicate that kind of domain knowledge overnight and we never take it for granted.

So our focus is less on reskilling and more on expanding. Our IT operations team connects and protects the core business and when they do that well we have the freedom to be our most flexible and innovative in everything we build on top of that foundation. That stability is what makes bold moves possible.

What we are investing in is the deliberate blending of that deep domain knowledge with new team members who bring cutting edge techniques and fresh perspectives from outside aviation. That combination, the institutional wisdom of teams who know this business inside and out paired with team members who know what is possible with emerging technology, is what makes for the most competent and impactful team. It is not one or the other. It is both, working together.

Ruby Platform

Is Ruby a single-use AI agent, or is it the foundation for a broader AI operating model across Elevate Jet?

Wimberly: Ruby is not an agent. Ruby is one platform component in a broader AI operating model that we are actively building at Elevate Jet. And that distinction matters because the vision was never to solve a single problem. The vision was to make 30 years of private aviation expertise instantly usable for our clients, and colleagues and then keep building from there.

Right now, Ruby powers our dynamic pricing and feasibility intelligence, giving clients instant, accurate answers about aircraft availability and pricing in real time. But the operating model expands from there through continuous model training and optimization, expanding the datasets available to Ruby, and integrating more services into the app so that the client experience becomes truly frictionless from the moment they open the app to the moment they land and even beyond.

Because here’s what we know about our clients. Whether they are seasoned private flyers who have built trust with us over decades, or first-time clients who are discovering private aviation through the app for the first time, what they all want is the same thing.

They want their time protected and their experience to be seamless. They want intelligence delivered so effortlessly that the system itself fades into the background. That is what the broader AI operating model is designed to deliver. Ruby is where it starts. It is not where it ends.

What role did data quality, taxonomy standardization, and knowledge capture play in the success of Ruby?

Wimberly: A critical one. You cannot build intelligent systems on messy data. Full stop. The quality of what goes in determines the quality of what comes out, and when your clients are making instant booking decisions for often high stakes travel, there is no room for garbage in, garbage out. The work of standardizing, structuring, and capturing decades of operational knowledge was not glamorous, but it was the foundation that makes everything else possible. The model is only as good as what you feed it.

And it is not a one-time job. Good data governance is essential to the ongoing viability of the platform. That means humans in the loop, continuously curating, validating, and improving the data that feeds the model. The intelligence gets better over time, but only if the governance around it is treated with the same rigor as the technology itself.

Enterprise AI Strategy and Mindset

What’s the most common mistake CTOs or business leaders make when trying to operationalize AI at scale?

Wimberly: Chasing AI instead of chasing a business outcome. It sounds simple, but it’s surprisingly easy to fall into, especially right now when the technology is genuinely exciting and moving fast. As technologists, we can get caught up in what’s possible and lose sight of what’s purposeful. Everything we enable must be grounded in the strategy of the business. That’s not just good governance; it’s how you prioritize what is inevitably a very long and exciting list of ideas coming from every corner of the organization.

The second tendency I see just as often is getting stuck in the analysis phase. Teams spend so much time architecting the perfect AI solution that they never actually put anything in front of a client. At Elevate Jet, our philosophy has been to use the right tool for the job and get something meaningful into our clients’ hands quickly, so we can be responsive to their actual needs rather than something we dreamed up on a whiteboard. Our models that already benefit from 30 years of operational excellence will also learn and grow over time, incorporating more aggregated and differentiated data to make recommendations increasingly tailored to each client. But that has to start from somewhere that is real.

Future Focused

How do you envision AI in the next 5-10 years?

Wimberly: AI is here. It is not coming. The leaders who are still treating it as a future problem, or expecting that more analysis will get them to a place where they feel more in control before they act, are already behind.

What I see ahead is emerging technology, not just AI, becoming a baseline expectation in every industry. Our clients expect us to leverage all of it, appropriately and responsibly, in service of their experience. The differentiator will be how well you manage it, govern it, and evolve it. And that requires something no model can provide, human judgment, human accountability, and human leadership. At Elevate Jet we are building for exactly that future, where the intelligence deepens over time and our clients and our teams are more empowered because of it, not in spite of it. And I promise you, the robots are not taking over.

If you had to give one piece of advice to future tech and business leaders, what would that be?

Wimberly: Fall deeply in love with your business. Not the technology. The business.

Sit with the accountants. Go on a ride along with your sales team. Get out of your office and if you are working remotely, find your way into the office. The more intimately you understand how a business actually works, what drives it, what breaks it, what delights its clients, the better technologist you are going to be. I genuinely believe that.

And give your teams the same gift. I am a big believer in data scientists sitting alongside the business teams they serve. Developers co-located with the colleagues they are building solutions for. That proximity is not a nice to have. It is where the best ideas come from. It is where technology stops being something that is done to a business and starts being something that moves with it.

The technology will keep changing. It always has. But the leaders who win will always be the ones who understood the business deeply enough to know exactly what to do with it. And if you can, find a founder to work for who has that same passion. Someone like Greg Raiff, who has spent decades obsessing over how to remove every point of friction from private aviation and never stopped pushing to make the experience better for every client. That kind of founder passion is contagious. It makes you a better leader. And it reminds you every day why the work matters.

The Leadership Lesson

As AI becomes a core capability across industries, the real differentiator will not be the technology itself but how effectively organizations apply it to real business challenges. As Jennifer Wimberly emphasizes, successful AI adoption begins with a deep understanding of the business, disciplined data foundations, and a clear focus on outcomes rather than hype. For technology leaders, the key takeaway is simple: when AI is grounded in real operational expertise and aligned with measurable goals, it becomes a powerful enabler of smarter decisions, greater efficiency, and lasting competitive advantage.