{"id":52733,"date":"2026-03-23T13:23:19","date_gmt":"2026-03-23T12:23:19","guid":{"rendered":"https:\/\/www.intelligentcio.com\/north-america\/2026\/03\/23\/the-new-fuel-how-data-and-ai-are-powering-virgin-atlantics-flight-path\/"},"modified":"2026-04-13T08:44:35","modified_gmt":"2026-04-13T07:44:35","slug":"the-new-fuel-how-data-and-ai-are-powering-virgin-atlantics-flight-path","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/north-america\/2026\/03\/23\/the-new-fuel-how-data-and-ai-are-powering-virgin-atlantics-flight-path\/","title":{"rendered":"The new fuel: How data and AI are powering Virgin Atlantic’s flight path"},"content":{"rendered":"\n

How Virgin Atlantic is leveraging data and AI to enhance decision-making, improve customer experience and drive profitability in an evolving aviation landscape<\/em>.<\/p>\n\n\n\n

The world is moving at breakneck speed, and few industries feel that pressure more than aviation.<\/p>\n\n\n\n

At the forefront of this transformation is Richard Masters, Vice President of Data and AI, Virgin Atlantic.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

From astrophysics to aviation<\/strong><\/p>\n\n\n\n

My role is Vice President of Data and AI at Virgin Atlantic. I\u2019m responsible for all analytical data across the organisation \u2013 anything that needs to be transformed or combined to support decision-making ultimately comes through my team,\u201d Richard said.<\/p>\n\n\n\n

His remit spans the entire business, from engineering and maintenance through to commercial and customer operations. Alongside this, he leads the airline\u2019s AI strategy: what to build, what to buy, who to partner with, and how to use AI responsibly.<\/p>\n\n\n\n

\u201cEssentially, if the term AI comes up, you come to me,\u201d he said. \u201cOur focus is on optimising decision support.\u201d<\/p>\n\n\n\n

Richard\u2019s career began in astrophysics, a background that still shapes how he approaches data today.<\/p>\n\n\n\n

\u201cThe analogy I always use is noise. As an astrophysicist, you\u2019re 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?\u201d<\/p>\n\n\n\n

After completing his PhD, Richard moved into systems and cyber intelligence, before joining Ernst & Young\u2019s forensics team, where he focused on compliance and behavioural analytics. He later joined Virgin Atlantic, working at the airline from 2018 to 2021 \u2013 a period defined by the COVID-19 crisis.<\/p>\n\n\n\n

\u201cWe had to do a lot of difficult but important work to keep the airline going,\u201d he reflects.<\/p>\n\n\n\n

Recognising AI\u2019s turning point<\/strong><\/p>\n\n\n\n

It was in the post-COVID landscape that Richard saw a shift.<\/p>\n\n\n\n

\u201cWhile aviation was focused on survival, other sectors were investing heavily in AI. But when generative AI emerged \u2013 particularly with ChatGPT \u2013 it became clear this wasn\u2019t just another tool. It was a catalyst.\u201d<\/p>\n\n\n\n

Initially, many organisations were cautious, particularly around using AI with sensitive data. But at Virgin Atlantic, leadership saw its broader potential.<\/p>\n\n\n\n

\u201cSpeaking with our CEO and CFO, we recognised this wasn\u2019t just a chatbot. It could support the entire chain of analysis, insight and decision-making.\u201d<\/p>\n\n\n\n

However, unlocking that value required a strong data foundation.<\/p>\n\n\n\n

\u201cWe knew our data needed to be in the right shape. That meant continuing the work we\u2019d started back in 2018 to centralise and structure our data through a unified platform.\u201d<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cWe could now bring all our data together from across different systems and do much more with it.\u201d<\/p>\n\n\n\n

AI during the COVID crisis<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cWe were already using machine learning before the pandemic,\u201d Richard said. \u201cBroadly speaking, we used it in two ways: predictive analytics \u2013 what\u2019s likely to happen \u2013 and prescriptive analytics \u2013 what happens if we change something.\u201d<\/p>\n\n\n\n

For example, pricing teams were already using data science to optimise flight occupancy and revenue.<\/p>\n\n\n\n

\u201cWhen 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.\u201d<\/p>\n\n\n\n

These insights were also critical during major strategic moments, including the attempted acquisition of Flybe and the airline\u2019s recapitalisation efforts.<\/p>\n\n\n\n

However, Richard points to 2022 as the real inflection point.<\/p>\n\n\n\n

\u201cThat\u2019s 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.\u201d<\/p>\n\n\n\n

This fundamentally changed how employees interacted with data.<\/p>\n\n\n\n

\u201cIt augmented people\u2019s abilities. Instead of relying solely on dashboards or static reports, they could explore information dynamically.\u201d<\/p>\n\n\n\n

For data teams, the implications were equally significant.<\/p>\n\n\n\n

\u201cThese 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.\u201d<\/p>\n\n\n\n

The result has been greater resilience and faster access to insights across the business.<\/p>\n\n\n\n

The challenge of centralisation<\/strong><\/p>\n\n\n\n

Despite the opportunities, implementing AI at scale is not without challenges.<\/p>\n\n\n\n

\u201cThe direction of travel for AI has actually been quite clear internally,\u201d Richard said. \u201cBut the difficulty comes from the sheer volume of AI solutions being marketed to teams.\u201d<\/p>\n\n\n\n

Different vendors often promote standalone tools, each requiring access to data.<\/p>\n\n\n\n

\u201cThat 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.\u201d<\/p>\n\n\n\n

To address this, Virgin Atlantic has focused on consolidating capabilities within its central data platform.<\/p>\n\n\n\n

\u201cWe can now tell teams: you don\u2019t 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.\u201d<\/p>\n\n\n\n

Another ongoing challenge is migrating legacy systems.<\/p>\n\n\n\n

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.\u201d<\/p>\n\n\n\n

A third challenge lies in aligning people and processes.<\/p>\n\n\n\n

\u201cYou might have multiple teams analysing the same data \u2013 like customer satisfaction scores \u2013 but using different methods or definitions. We need to bring those teams together and align their approaches.\u201d<\/p>\n\n\n\n

This involves both cultural and organisational change.<\/p>\n\n\n\n

\u201cWith 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.\u201d<\/p>\n\n\n\n

What customers experience<\/strong><\/p>\n\n\n\n

From a passenger\u2019s perspective, AI at Virgin Atlantic is largely invisible \u2013 and that\u2019s by design.<\/p>\n\n\n\n

\u201cWe haven\u2019t replaced our core operational systems with AI,\u201d Richard said. \u201cInstead, we use it to support decision-making.\u201d<\/p>\n\n\n\n

For instance, AI models can anticipate potential delays and suggest possible outcomes, but human teams remain in control.<\/p>\n\n\n\n

\u201cThe goal is augmentation, not automation.\u201d<\/p>\n\n\n\n

When implemented effectively, AI enhances the overall customer experience without being noticeable.<\/p>\n\n\n\n

\u201cIf we get it right, you won\u2019t see the AI \u2013 you\u2019ll just experience better communication and more proactive service.\u201d<\/p>\n\n\n\n

This is reflected in improvements to customer satisfaction metrics and overall business performance.<\/p>\n\n\n\n

Driving profitability<\/strong><\/p>\n\n\n\n

Virgin Atlantic has recently reported its first profitability since 2016 \u2013 and AI has played a supporting role.<\/p>\n\n\n\n

\u201cOne 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.\u201d<\/p>\n\n\n\n

Importantly, this is not about personalised pricing based on individual behaviour – it\u2019s about finding the most effective price points overall,\u201d said Richard.<\/p>\n\n\n\n

AI has also contributed to cost optimisation.<\/p>\n\n\n\n

\u201cWe\u2019re using data to become more efficient and to make better investment decisions. Profitability comes from both revenue optimisation and cost control.\u201d<\/p>\n\n\n\n

However, Richard is clear that AI is not being used primarily to reduce headcount.<\/p>\n\n\n\n

\u201cWe\u2019re not automating entire functions to cut costs. It\u2019s about supporting decisions and enabling us to move faster.\u201d<\/p>\n\n\n\n

For a relatively small airline operating around 45 aircraft, this agility is critical.<\/p>\n\n\n\n

\u201cIt allows us to do more with what we have and fine-tune our operations,\u201d said Richard.<\/p>\n\n\n\n

What comes next<\/strong><\/p>\n\n\n\n

Looking ahead, Virgin Atlantic\u2019s focus is on expanding capabilities within its centralised data platform.<\/p>\n\n\n\n

\u201cOne key area is knowledge management,\u201d Richard says. \u201cWe want to bring together all our internal and external policies \u2013 everything from baggage rules to safety procedures \u2013 and make them easily accessible.\u201d<\/p>\n\n\n\n

The aim is consistency across all customer touchpoints.<\/p>\n\n\n\n

\u201cWhether a customer contacts us or a crew member checks their app, they should receive the same information.\u201d<\/p>\n\n\n\n

AI plays a crucial role in this by enriching and structuring content.<\/p>\n\n\n\n

\u201cWe\u2019re also using AI for summarisation and categorisation. For example, customer messages can be sorted and prioritised more quickly, helping our teams respond faster.\u201d<\/p>\n\n\n\n

The same approach is being applied to operational areas such as health and safety reporting.<\/p>\n\n\n\n

\u201cWe want to encourage more reporting, but that creates a volume challenge. AI helps us triage those reports, identifying what\u2019s minor and what could become a serious issue.\u201d<\/p>\n\n\n\n

At a broader level, Richard sees a shift in how organisations approach AI.<\/p>\n\n\n\n

\u201cInstead of buying a separate solution for every problem, we\u2019re building reusable capabilities on a core platform. The same AI engine can support customer care, safety reporting and many other areas.\u201d<\/p>\n\n\n\n

This approach improves efficiency and scalability.<\/p>\n\n\n\n

\u201cIt\u2019s about creating one coherent system with a set of tools that different teams can use.\u201d<\/p>\n\n\n\n

A data-first future<\/strong><\/p>\n\n\n\n

For Virgin Atlantic, the future of aviation is inseparable from data and AI.<\/p>\n\n\n\n

Richard\u2019s journey \u2013 from analysing distant galaxies to optimising airline operations \u2013 reflects a consistent theme: extracting clarity from complexity.<\/p>\n\n\n\n

\u201cThe challenge is always the same,\u201d he says. \u201cHow do we remove the noise and reveal the signal?\u201d<\/p>\n\n\n\n

In an industry defined by uncertainty and constant change, that capability may prove to be the most valuable fuel of all.<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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. […]<\/p>\n","protected":false},"author":58,"featured_media":52734,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[4,11,10695,1645,34,43],"tags":[9798,9806,11133,5487,5910,221,4369,158,6243],"class_list":["post-52733","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aviation","category-cloud","category-feature","category-insights","category-more-news","category-top-stories","tag-airline-innovation","tag-aviation-technology","tag-data-and-ai","tag-data-strategy","tag-databricks","tag-digital-transformation","tag-generative-ai","tag-machine-learning","tag-virgin-atlantic"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/posts\/52733","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/comments?post=52733"}],"version-history":[{"count":1,"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/posts\/52733\/revisions"}],"predecessor-version":[{"id":52828,"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/posts\/52733\/revisions\/52828"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/media\/52734"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/media?parent=52733"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/categories?post=52733"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/north-america\/wp-json\/wp\/v2\/tags?post=52733"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}