The intelligent experience: designing CX for trust, growth and efficiency

The last era of commerce and public services was built around people interacting directly with brands, platforms and institutions. AI has now inserted itself between them.

AI is increasingly acting as a mediator for how consumers and citizens discover, evaluate, and access products, services, and support – often shaping how decisions are made before an organisation knows that a journey has begun.

This represents more than the arrival of another digital channel. It fundamentally changes how organisations are discovered, evaluated and chosen.

“The assumption that customers will engage directly with your brand is already outdated,” says Clare Allum, global head of consumer and manufacturing at PA Consulting, a global innovation consultancy. “AI intermediaries now decide what gets seen, compared and chosen. If you’re not designing for that layer, you’re not competing and you’re being filtered out. The winners will be those that design for machine judgement while keeping human trust.”

This shift presents a challenge for both public and private sector organisations, but also an opportunity to respond more intelligently. Becky Noble, a public services expert at PA, says: “In public services, we’re seeing the opportunity for AI to equip organisations with early insight to anticipate needs before they are explicitly expressed.”

From the attention economy to the recommendation economy

“For decades, organisations competed for attention. Increasingly, they’ll compete for recommendation,” says Allum. “It’s no longer enough to be visible; you need to be understandable and credible to the systems helping customers make decisions.”

Marketing budgets, search strategies and customer experience investments were designed to ensure a brand appeared at the right moment in a customer’s journey. Success depended on memorability and persuasion.

AI is changing that equation. Recommendation engines may compare products using pricing, performance or other criteria such as reviews or consumer sentiment, which surface strengths and weaknesses beyond the scope and control of traditional marketing.

As a result, firms can no longer assume they control how information is interpreted. The challenge is no longer getting discovered by customers. It is how to be understood, evaluated and recommended by digital intermediaries.

“We’re shifting from a world where information was designed primarily for human consumption to one where it also needs to be interpreted by AI systems,” says Donald Cameron, financial services expert at PA.

In areas such as consumer-facing financial services, where products are complex and trust is critical, the implications are significant. Information once used to persuade now needs to be structured for AI systems that compare products, evaluate evidence and recommend options.

“The ability to be found and recommended by AI tools requires more structured information than traditional marketing content,” says Cameron. “This means organisations need more data, more depth and more detail to influence the decisions customers are making.”

The same principle applies to public bodies, says Noble. “For public bodies, trust is not a brand attribute; it is legitimacy,” she adds. “Citizens need to know when AI is being used, what it has done, what it has not done, and how a human can challenge the outcome. This last part, the human-in-the-loop, is crucial. And we’ve seen such principles applied to good effect already in AI implementations; such as in our work with HMCTS.”

Trust in a world of recommendation and delegation

If recommendation is becoming the new battleground, then the advantage will go to those able to deploy trust as a differentiator. When customers are seeing recommendations provided by a large language model, trust is not limited to the information being correct, but also the source of that information and the brand behind it.

One surprising trend emerging in financial services is that the locus of trust is also shifting, with some customers reporting feeling more comfortable discussing sensitive financial topics with AI than with another person.

“Some consumers feel less judgement when asking certain questions or disclosing sensitive information to an AI agent,” says Cameron. “And that has intriguing implications. Consumers still want to feel like their financial services provider is looking out for them and providing support before problems arise. Trust, empathy and clarity remain essential.”

This trend opens up new ways to support customers. But it also raises questions about governance, accountability and accuracy. And the importance of trust is only likely to increase as AI moves beyond recommendation and into action, especially as payment providers look to deliver more progressive initiatives like AI agents completing transactions on a user’s behalf.

Visa’s Intelligent Commerce initiative, alongside similar efforts from Mastercard, PayPal, Amazon, OpenAI and Google, points to a future in which consumers delegate parts of the purchasing process to intelligent agents operating within defined parameters.

Fully autonomous purchasing is not yet mainstream, but the direction of travel is becoming clearer. We’re moving from AI recommendation towards AI delegation.

As AI begins to act on users’ behalf, what will end-users ask these agents to prioritise?

PA’s annual Brand Impact Index – a nationally representative survey of US consumers – shows that consumers are leaning towards brands that deliver dependably, and are focused on making life easier and better.

Allum adds: “They’re looking to organisations that reduce friction, solve problems, and earn trust. Brand leaders have a responsibility not just to deliver that but to ensure that AI engines know and surface this information.”

Crucially, organisations cannot become so focused on AI optimisation that they lose sight of the substance behind the experience. Adds Cameron: “The quality of products and services, and the people behind them, need to withstand scrutiny,” says Cameron. “Especially as AI evaluates offerings based on evidence, outcomes and customer value, not just messaging.”

As intelligent systems increasingly compare products based on evidence, outcomes and customer value, weak propositions may become harder to disguise behind strong marketing alone.

From reaction to anticipation

With AI moving from recommendation into delegation, it also creates the conditions for a more fundamental shift in how services are delivered, enabling organisations to anticipate needs, not just respond to them.

By combining AI, analytics and connected data, organisations can identify signals earlier and respond before customers or citizens actively seek support.

The opportunity is particularly pointed in the public sector, says Noble. Advances in technology allow for data gaps between siloed departments to be bridged, enabling a shift towards early intervention rather than reaction.

Noble gives the example of an elderly person living alone. “They might be missing GP appointments, repeatedly calling the council because they’ve muddled collection days, or not drawing money from their pension,” she explains. “There are lots of little breadcrumbs that might suggest something isn’t quite right.”

Individually, these signals may appear insignificant. Together, they can indicate that intervention is needed and trigger support mechanisms earlier.

“People expect the services they use to work together around their needs, not organisational boundaries, because from their perspective it’s all part of the same life event,” says Noble. “Technology can play a key role in improving anticipatory services, ensuring citizens get the support they need in the moments that matter.”

Singapore’s LifeSG platform demonstrates how this can work in practice. Designed around major life events such as childbirth, retirement or job loss, it brings together services across government and surfaces relevant information, actions and support before citizens need to search for them individually.

Similar thinking is now appearing across many sectors. Among consumer products, Unilever has used AI to help its teams analyse data faster and identify stronger product ideas, claims and attributes earlier in the innovation process, enabling quicker, more evidence-based responses to consumer needs.

In financial services, the Commonwealth Bank of Australia’s Customer Engagement Engine uses AI and behavioural analytics to identify emerging risk states and intervene before customer harm occurs. Digital-native banks such as Monzo are also exploring ways to help customers anticipate and manage future financial strain.

Delivering these services consistently depends on connected data, interoperable systems and a unified view of customers or citizens.

“Very few traditional organisations have that single view,” says Cameron. “That’s where some newer entrants to industries like retail and finance are ahead of the game.”

Focus on the moments that matter

Becoming anticipatory does not mean trying to predict everything. One of the mistakes organisations can make is assuming that AI should be applied equally across every interaction. The greater opportunity lies in identifying the touchpoints that deliver the most value; where timely intervention can prevent harm, build trust and improve lives.

“Not every touchpoint carries the same weight,” says Allum. “A small number of moments shape how customers judge you, determining whether they convert in that interaction and whether they choose to come back again, building loyalty over time.”

Yet the focus on these moments should never come at the expense of the customer experience investments of the past decade, warns Allum. “Frictionless journeys, intuitive interfaces and strong service design remain essential, but they are now table stakes because we’re all getting less patient and more likely to switch,” she adds.

Brands that succeed in an AI-mediated economy will build on those foundations rather than replace them. They will become machine-legible, ensuring intelligent systems can accurately understand and evaluate what they offer. They will become trusted, maintaining transparency, empathy and credibility even as AI takes a larger role in decision-making. And they will become anticipatory, identifying needs earlier and acting before problems emerge.

The challenge is no longer simply to attract attention. As AI becomes the new front door to commerce and public services (and, in time, begins to act on users’ behalf), competitive advantage will belong to organisations that are easiest to understand, easiest to trust and best placed to anticipate and act when it matters most.

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, 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 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.

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.