FEATURE
When generative AI emerged – particularly with ChatGPT – it became clear this wasn’ t just another tool. It was a catalyst.
“ 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.”
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INTELLIGENT CIO EUROPE www. intelligentcio. com