How Virgin Atlantic is leveraging data and AI to enhance decision-making, improve customer experience and drive profitability in an evolving aviation landscape.
The world is moving at breakneck speed, and few industries feel that pressure more than aviation.
At the forefront of this transformation is Richard Masters, Vice President of Data and AI, Virgin Atlantic.
The airline has recently partnered with Databricks, a leading provider of a cloud-based platform that enables organisations to build, scale and govern data and AI, including generative AI and Machine Learning models.
Intelligent CIO spoke with Richard about his journey from astrophysics to aviation, how AI is reshaping Virgin Atlantic, and why a data-first approach is essential for navigating the future of flight.
From astrophysics to aviation
My role is Vice President of Data and AI at Virgin Atlantic. I’m responsible for all analytical data across the organisation – anything that needs to be transformed or combined to support decision-making ultimately comes through my team,” Richard said.
His remit spans the entire business, from engineering and maintenance through to commercial and customer operations. Alongside this, he leads the airline’s AI strategy: what to build, what to buy, who to partner with, and how to use AI responsibly.
“Essentially, if the term AI comes up, you come to me,” he said. “Our focus is on optimising decision support.”
Richard’s career began in astrophysics, a background that still shapes how he approaches data today.
“The analogy I always use is noise. As an astrophysicist, you’re working with very noisy signals from telescope observations. You use data reduction pipelines to remove that noise and reveal the signal the clear picture of a galaxy. That idea has guided my entire career: how do we extract a clear signal so people can make better decisions?”
After completing his PhD, Richard moved into systems and cyber intelligence, before joining Ernst & Young’s forensics team, where he focused on compliance and behavioural analytics. He later joined Virgin Atlantic, working at the airline from 2018 to 2021 – a period defined by the COVID-19 crisis.
“We had to do a lot of difficult but important work to keep the airline going,” he reflects.
Recognising AI’s turning point
It was in the post-COVID landscape that Richard saw a shift.
“While aviation was focused on survival, other sectors were investing heavily in AI. But when generative AI emerged – particularly with ChatGPT – it became clear this wasn’t just another tool. It was a catalyst.”
Initially, many organisations were cautious, particularly around using AI with sensitive data. But at Virgin Atlantic, leadership saw its broader potential.
“Speaking with our CEO and CFO, we recognised this wasn’t just a chatbot. It could support the entire chain of analysis, insight and decision-making.”
However, unlocking that value required a strong data foundation.
“We knew our data needed to be in the right shape. That meant continuing the work we’d started back in 2018 to centralise and structure our data through a unified platform.”
At the time, Richard was leading data science efforts within the airline. While the environment was already strong for Machine Learning, recent advances in data platforms and governance tools created new opportunities.
“We could now bring all our data together from across different systems and do much more with it.”
AI during the COVID crisis
The aviation industry faced an unprecedented shock during COVID-19, with many airlines collapsing entirely. For Virgin Atlantic, data and AI played a crucial supporting role.
“We were already using machine learning before the pandemic,” Richard said. “Broadly speaking, we used it in two ways: predictive analytics – what’s likely to happen – and prescriptive analytics – what happens if we change something.”
For example, pricing teams were already using data science to optimise flight occupancy and revenue.
“When COVID hit, that capability became even more valuable. We could estimate call centre volumes, predict travel patterns, and understand where demand was shifting. That helped us prepare for sudden spikes in customer queries and operational challenges.”
These insights were also critical during major strategic moments, including the attempted acquisition of Flybe and the airline’s recapitalisation efforts.
However, Richard points to 2022 as the real inflection point.
“That’s when generative AI introduced a completely new interface natural language. Suddenly, people could ask questions in a much more intuitive way and even ask for explanations repeatedly until they understood.”
This fundamentally changed how employees interacted with data.
“It augmented people’s abilities. Instead of relying solely on dashboards or static reports, they could explore information dynamically.”
For data teams, the implications were equally significant.
“These models are incredibly effective at classifying and categorising data something that used to be very challenging. They also accelerate development. Generative AI is particularly strong at coding, and we knew our partners and vendors would begin embedding it into their own tools.”
The result has been greater resilience and faster access to insights across the business.
The challenge of centralisation
Despite the opportunities, implementing AI at scale is not without challenges.
“The direction of travel for AI has actually been quite clear internally,” Richard said. “But the difficulty comes from the sheer volume of AI solutions being marketed to teams.”
Different vendors often promote standalone tools, each requiring access to data.
“That leads to duplication and increased risk. Without a centralised strategy, you end up with data being copied into multiple systems, which becomes difficult to manage and govern.”
To address this, Virgin Atlantic has focused on consolidating capabilities within its central data platform.
“We can now tell teams: you don’t need to buy another solution we already have that capability. That builds trust and gives us much better visibility over how data is being used.”
Another ongoing challenge is migrating legacy systems.
Airlines have a lot of legacy infrastructure. Moving those data feeds into a modern, centralised platform takes time, but as more teams see the benefits, adoption increases.”
A third challenge lies in aligning people and processes.
“You might have multiple teams analysing the same data – like customer satisfaction scores – but using different methods or definitions. We need to bring those teams together and align their approaches.”
This involves both cultural and organisational change.
“With support from senior leadership, we can engage with different departments, understand their goals, and translate that into a shared framework. That helps teams collaborate more effectively and evolve their processes.”
What customers experience
From a passenger’s perspective, AI at Virgin Atlantic is largely invisible – and that’s by design.
“We haven’t replaced our core operational systems with AI,” Richard said. “Instead, we use it to support decision-making.”
For instance, AI models can anticipate potential delays and suggest possible outcomes, but human teams remain in control.
“The goal is augmentation, not automation.”
When implemented effectively, AI enhances the overall customer experience without being noticeable.
“If we get it right, you won’t see the AI – you’ll just experience better communication and more proactive service.”
This is reflected in improvements to customer satisfaction metrics and overall business performance.
Driving profitability
Virgin Atlantic has recently reported its first profitability since 2016 – and AI has played a supporting role.
“One example is our pricing engine, powered by machine learning. It helps us determine the right fare at the right time by optimising inventory and demand.”
Importantly, this is not about personalised pricing based on individual behaviour – it’s about finding the most effective price points overall,” said Richard.
AI has also contributed to cost optimisation.
“We’re using data to become more efficient and to make better investment decisions. Profitability comes from both revenue optimisation and cost control.”
However, Richard is clear that AI is not being used primarily to reduce headcount.
“We’re not automating entire functions to cut costs. It’s about supporting decisions and enabling us to move faster.”
For a relatively small airline operating around 45 aircraft, this agility is critical.
“It allows us to do more with what we have and fine-tune our operations,” said Richard.
What comes next
Looking ahead, Virgin Atlantic’s focus is on expanding capabilities within its centralised data platform.
“One key area is knowledge management,” Richard says. “We want to bring together all our internal and external policies – everything from baggage rules to safety procedures – and make them easily accessible.”
The aim is consistency across all customer touchpoints.
“Whether a customer contacts us or a crew member checks their app, they should receive the same information.”
AI plays a crucial role in this by enriching and structuring content.
“We’re also using AI for summarisation and categorisation. For example, customer messages can be sorted and prioritised more quickly, helping our teams respond faster.”
The same approach is being applied to operational areas such as health and safety reporting.
“We want to encourage more reporting, but that creates a volume challenge. AI helps us triage those reports, identifying what’s minor and what could become a serious issue.”
At a broader level, Richard sees a shift in how organisations approach AI.
“Instead of buying a separate solution for every problem, we’re building reusable capabilities on a core platform. The same AI engine can support customer care, safety reporting and many other areas.”
This approach improves efficiency and scalability.
“It’s about creating one coherent system with a set of tools that different teams can use.”
A data-first future
For Virgin Atlantic, the future of aviation is inseparable from data and AI.
Richard’s journey – from analysing distant galaxies to optimising airline operations – reflects a consistent theme: extracting clarity from complexity.
“The challenge is always the same,” he says. “How do we remove the noise and reveal the signal?”
In an industry defined by uncertainty and constant change, that capability may prove to be the most valuable fuel of all.
