CTO Jennifer Wimberly on Operationalizing AI in Aviation
AI / Machine Learning | 6 min read
In high-stakes industries like aviation, where every decision affects time, safety, and client experience, the role of AI extends far beyond automation. To explore how intelligent systems are being operationalised in mission-critical environments, CTO Magazine spoke with Jennifer Wimberly, Chief Technology Officer at Elevate Jet. In this conversation, Wimberly outlines how her team is translating decades of aviation operational expertise into AI-driven capabilities — and what it really takes to scale AI from experimentation to production in a heavily regulated industry.
Insight Over Automation: A Different Design Philosophy
When asked how Elevate Jet defines the boundary between automation and human oversight, Wimberly's answer challenges a common assumption in enterprise AI:
"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."
— Jennifer Wimberly, Chief Technology Officer, Elevate Jet
Activating Decades of Operational Data
Many enterprises sit on decades of operational history but struggle to activate it for AI. Wimberly describes the process of converting historical experience into structured, AI-ready intelligence as deeply hands-on — and draws an important distinction between the types of AI involved. While generative AI thrives on unstructured data (text, documents, notes, transcripts), the narrow AI and machine learning powering Ruby — Elevate Jet's AI platform — requires clean, structured, consistent inputs. Preparing that data demanded deliberate data stewardship and a fundamental shift in how the team thinks about data: not as a record of what happened, but as a raw material to be refined and applied.
The stakes are direct: Elevate Jet's client-facing app delivers instant pricing and real-time aircraft availability. For those answers to be accurate and immediate, decades of operational excellence had to be structured into something Ruby could use — not just something the organisation could point to as heritage.
Change Management Without the Jargon
"We actually try to avoid saying 'AI change management' because the principles that make emerging technology adoption successful are not unique to AI. What we have seen work comes down to two things: do your teams deeply understand the business, and can they articulate business value? Emerging technology adoption is deeply experiential. You cannot learn it in a classroom or a slide deck. Organizations that try to shortcut that crawl phase almost always pay for it later."
— Jennifer Wimberly, Chief Technology Officer, Elevate Jet
Governance as a Design Principle, Not a Checkbox
In a regulated, trust-driven industry, Wimberly frames governance as foundational rather than procedural. Before any design begins, the Elevate Jet team asks two questions: "Can we?" and "Should we?" — because being technically capable of using data to serve a client and being ethically right to do so are two different questions, both of which require a good answer. Data privacy is designed in from the beginning, not bolted on at the end. Client trust — built on decades of earned relationship — is non-negotiable in an industry where operator-client relationships are the product.
Measuring ROI: The Hard Side and the Soft Side
Wimberly argues that AI ROI has two sides — and both must be measured. The hard side is quantifiable: booking speed, pricing accuracy, conversion rates, and new client acquisition. At Elevate Jet, what previously took hours of back-and-forth now happens in seconds. The soft side — client confidence, friction removal, operations teams acting faster on better information — compounds over time into something equally powerful: trust. Her discipline: define what success looks like before you start, connect every technology investment to a specific client or business outcome, and let that discipline keep you honest about what the technology is actually delivering versus what you hoped it would.
Key Takeaways
- • Elevate Jet's AI philosophy centres on insight generation over automation — helping humans make faster, better-informed decisions rather than removing human judgment from high-stakes aviation workflows.
- • Activating decades of operational data required a key distinction: generative AI thrives on unstructured data, but Ruby (Elevate Jet's AI platform) requires clean, structured inputs — demanding hands-on data stewardship and treating data as a raw material rather than a historical record.
- • Change management success comes down to two things: teams that deeply understand the business and can articulate business value — not which tools they know. AI adoption is experiential; shortcutting the crawl phase always costs more later.
- • Governance is a design principle at Elevate Jet — the "can we" and "should we" framework ensures data use is both technically capable and ethically sound before any development begins. Client data privacy is built in from the start, not added at the end.
- • AI ROI has a hard side (booking speed, pricing accuracy, conversion rates) and a soft side (client confidence, friction removal, faster operations decisions) — both compound over time into the most durable return: trust.