Many organisations launch AI pilots or digital transformation programmes to improve customer experience, only to encounter structural barriers that limit scale, from fragmented data and ageing technology to disconnected operating models. In turn, progress often manifests in incremental gains, such as faster decisions, smoother operations and lower failure demand, rather than meaningful improvements in customer outcomes.
The pressure to get customer experience right is only increasing. Recent research from PA Consulting found that consumers view customer centricity and intelligent innovation as two of the strongest drivers of brand engagement. Despite this, many organisations struggle to translate investment into measurable impact.
This challenge is becoming more acute as AI spending accelerates. Those that close the gap are seeing tangible returns, with leading brands growing revenue 1.5 times faster and profits 2.2 times faster than their competitors.
For Clare Allum, global head of consumer and manufacturing at PA Consulting, the key to progress is to focus on the foundations. “You can build a really nice front end,” she says, “but data and infrastructure will determine whether it succeeds.”
“It’s only once these foundations are fixed that organisations can unlock the next phase of AI-enabled outcomes,” says Becky Noble, a public services expert at PA. “That means improved efficiency and resilience, and, ultimately, products, services and experiences that can anticipate needs and, in some cases, act autonomously.”
Fixing the foundations
The first requirement is deceptively simple: get the basics right.
Many organisations are still wrestling with legacy systems, fragmented data and operational complexity. Without addressing those issues, it becomes difficult to deliver the seamless experiences customers now expect.
It’s only once [the data] foundations are fixed that organisations can unlock the next phase of AI-enabled outcomes
BPP, a leading global education provider, illustrates the importance of investing in the foundations that sit behind the customer experience. Looking to accelerate its growth ambitions while delivering a better digital experience for students, BPP needed to address outdated platforms and fragmented user journeys without losing sight of the capabilities required to support future scale.
Working with PA, it launched an end-to-end transformation that put users at the heart of service design, established reliable data to underpin every student and staff interaction, and created the scalable digital platform needed for a more digital-first future. The result was a more seamless and personalised experience, greater visibility of student needs and a 50% reduction in account-related queries.
In the public sector, Noble says a similar principle applies to AI-enabled initiatives. The focus is two-fold: improving internal efficiency while exploring ways to improve citizen-facing services.
PA’s work with HM Courts and Tribunals Service (HMCTS) illustrates how those priorities can intersect. Facing rising demand and growing backlogs, HMCTS wanted to explore how AI could improve operational efficiency and service delivery. Staff had access to extensive guidance, but finding relevant information could be time-consuming.
Working with Microsoft, PA designed and piloted a GenAI knowledge-retrieval assistant that allows staff to ask questions in natural language and receive concise responses.
“AI now plays a pivotal role in helping HMCTS reduce court backlogs and provide a better service for citizens,” says Noble.
Once organisations have the data and infrastructure foundations in place, they can begin turning their attention to a more ambitious challenge: anticipating customer needs before issues occur. This is where many organisations believe the greatest long-term value lies.
PA’s work with Eurostar demonstrates how predictive capabilities can reshape customer experience at scale. The rail operator wanted to better understand passenger flows through Gare du Nord in Paris, Europe’s busiest station, where delays at check-in can create frustration for customers and operational pressure for staff.
To address the challenge, PA analysed data on customer check-ins and train performance to train a machine-learning model capable of predicting passenger flow through the station. The resulting solution, now moving into production, enables teams to anticipate congestion, adjust operations and proactively guide passengers on when best to check in.
The result is a more anticipatory experience, with the predictive system helping to reduce delays and pressure on frontline teams, while better aligning operations with customer needs.
When CX takes the lead
The most advanced organisations are moving beyond improving interactions to apply predictive capabilities in ways that help customers achieve what they’re trying to do, faster.
In financial services, this shift is particularly visible. Customers increasingly expect digital experiences that understand their circumstances, anticipate their needs and reduce the effort required to manage their finances.
Yet there remains a significant trust gap. Research conducted by PA among UK building society members found that while 74% already use digital banking channels at least monthly, only 35% feel comfortable with AI’s use in financial services and fewer than a third understand how it is being used.
The challenge for financial institutions is therefore not simply deploying more AI, but using it in ways that are transparent, trusted and clearly beneficial to customers.
“Many financial products serve very similar purposes,” says Donald Cameron, a financial services expert at PA. “Increasingly, differentiation and market share comes down to trust and credibility.”
Some organisations are already exploring what that next generation of customer experience could look like.
NatWest, for example, is using generative AI to evolve its digital assistant Cora+ from a reactive support tool into a more proactive financial companion. By analysing past behaviours and spending patterns, Cora+ brings the ability to anticipate customer needs without requiring the customer to think of what to ask.
The retail sector provides a further example. Amazon’s AI-powered shopping assistant, Alexa+, uses customer activity and conversational context to generate recommendations, answer questions and refine suggestions through dialogue.
“Amazon has always pushed the boundaries of convenience, and now it’s experimenting with using AI to enable conversational commerce,” says Allum. “Nobody wants to browse sixteen pages of options for a wedding outfit. I want the website to understand what I’m trying to achieve and help me get there faster.”
Outcomes, not interactions
As experiences become more intelligent, the organisations creating the greatest value are those that understand what customers are trying to achieve and remove the barriers standing in their way.
Competitive advantage has historically come from responding faster, serving better or adding more features. Increasingly, it will come from anticipating and acting on end users’ needs.
The next era of customer experience won’t belong to those that react most efficiently, but to those capable of making the right intervention at the right moment.
Many organisations launch AI pilots or digital transformation programmes to improve customer experience, only to encounter structural barriers that limit scale, from fragmented data and ageing technology to disconnected operating models. In turn, progress often manifests in incremental gains, such as faster decisions, smoother operations and lower failure demand, rather than meaningful improvements in customer outcomes.
The pressure to get customer experience right is only increasing. Recent research from PA Consulting found that consumers view customer centricity and intelligent innovation as two of the strongest drivers of brand engagement. Despite this, many organisations struggle to translate investment into measurable impact.
This challenge is becoming more acute as AI spending accelerates. Those that close the gap are seeing tangible returns, with leading brands growing revenue 1.5 times faster and profits 2.2 times faster than their competitors.
