AI in Customer Experience: Building Products That Work for Customers

alexwelsh6
1 day ago
6 min read

At what point do internal AI improvements translate into real customer value?

Many organisations are exploring what AI can offer, while others are still working out where it fits in their priorities, but wherever you sit on that spectrum, one pattern keeps surfacing: AI typically improves internal team workflows (e.g., automating data analysis, process flows, etc) faster than it improves the customer-facing experience.

This gap is worth paying attention to because it reveals something important about how real transformation unfolds. We hear this echoed in conversations with product, CX, and strategy teams as they navigate early adoption - many want to understand where AI can make a visible difference, how internal processes influence that timeline, and when customers should actually feel the results.

Why Does This Matter?

Internal efficiency creates the conditions for better products: Faster decisions and cleaner workflows matter most when they support clearer journeys and more dependable outcomes for customers. This means that it's important to also determine the change in customer experience when you deploy AI for internal processes.

Progress that stays internal is harder to translate into customer outcomes: The impact of AI becomes clearer when improvements shape the experience customers actually interact with. This is the real measurable value of a business - your customers love your products.

Customer value is shaped only if the service is founded on behaviours: Trust, retention, and confidence grow when products work reliably from the customer’s point of view.

This gap sits with product ownership, not the technology itself: Closing it depends on how internal AI progress is reflected in product decisions, prioritisation, and design. Most organisations approach AI from a tech perspective only - this reductionist approach doesn't deliver the continuous impact that a business can achieve.

According to recent data from IBM, '51% of all mature AI adopters and 71% of AI optimisers say that using AI in customer service has helped them grow revenue.'

Customers value outcomes, not labels

Customer experience research shows consistent preferences across sectors. People want clarity, ease, and predictable outcomes. They rarely ask for AI directly. They respond more strongly to journeys that feel smoother, faster, and less strenuous.

Recent studies point to a stark disconnect between organisational enthusiasm and customer sentiment. According to HubSpot & SurveyMonkey, 75% of marketing leaders say AI is more important to their strategies this year, while just 19% of consumers say they’re excited about it.

This matters because early AI adoption often focuses on operational tasks. Data preparation, document handling, risk checks, and workflow support all influence experience quality without changing the interface.

Customers may not see a new feature, yet they still feel improvements in speed and transparency.

'We worked with our banking client to develop an AI decision-making platform and started with customers first. Along with our clients, we spoke to over 80 people to understand how they make decisions, what’s important to them, what they do once the decision is made etc’ to develop journeys and features that spoke to their needs and expectations.’

AI in customer experience is shaped by internal systems

The structure of internal systems plays a substantial role in determining how quickly customer experiences can evolve.

Leaders in financial services often cite data quality, legacy platforms, and unclear ownership as consistent barriers. AI can support the improvement of these foundations, but the work takes time and must be handled carefully.

As a Chief Technology Officer told us at our recent roundtable event:

'AI can only take an organisation so far. It needs dependable data, strong governance, and clear support from senior leaders, otherwise data owners have no reason to prioritise the use cases that create value.’

As internal systems stabilise, teams gain the confidence to revisit journeys that previously felt constrained by operational reality.

They can remove steps that caused friction. They can rethink flows that were built around technical limitations rather than customer needs.

AI becomes an enabler for this work, not a replacement for thoughtful design.

When Internal Systems Stabilise, Customer Experience Improves

When internal foundations begin to stabilise, customer experience design gains room to move. Teams can shift their attention from technical constraints to what customers genuinely need.

They can simplify sequences that once felt fixed. They can create journeys that guide people through important moments with greater confidence.

This is where AI supports design in a practical way. It improves the conditions in which decisions are made. It reduces noise, strengthens consistency, and supports clearer flows.

Customers do not need to see the underlying technology to feel the benefit. They recognise steady progress in the ease of the journey.

Here are some of the common points shared by the most successful product teams we’ve seen this year:

They identify tasks that drain time or attention within the customer journey, then focus on improving the supporting process rather than adding new features at the surface.
They look closely at the moments where customers lose momentum and trace the cause back to design, process, or underlying systems. This helps them understand where AI can support a more dependable flow.
They bring design, engineering, and CX together early to ensure that internal progress translates into external value in a coherent and predictable way.
They maintain a simple review cycle that captures qualitative feedback from customers and frontline teams, helping them judge whether internal improvements are genuinely felt.
They use AI to support consistent decisions in high-volume or high-stress parts of the journey, creating experiences that feel steadier and more reliable for customers.
They develop data flows as part of their customer journeys - determining where data is captured, how it's captured, and how it's utilised to deliver the ideal experience.

Gaps expose the work that matters most

The gap between internal progress and customer impact is becoming a reliable signal for teams working through early AI adoption.

It highlights where transformation genuinely begins, long before new features appear at the surface. The most effective teams use this space to understand which operational steps hold the greatest influence over the journey.

They strengthen the data, decisions, and processes that shape reliability, then assess how early AI capability can support these foundations.

As the internal structure improves, the path to customer value becomes clearer, more predictable, and far easier to deliver with confidence.

Understanding AI Maturity

AI maturity is often discussed but rarely defined. A simple maturity model helps teams understand where they are today and what progress looks like. For example, this might look like:

Level 1 - Exploration

Teams test ideas, run small pilots, and explore what AI can do for their product. Work is early stage, often experimental, and usually contained within specific teams and functions.

Level 2 - Workflow Integration

AI begins to support defined tasks within existing workflows. Teams start adapting how they work to include AI as part of day-to-day delivery, and early patterns for collaboration between product, design, engineering, and data begin to form.

Level 3 - Orchestration

AI connects multiple steps in a process, creating more coherent end-to-end flows. Human oversight and model behaviour are managed together, and roles and responsibilities become clearer as teams formalise how decisions are made and monitored.

Level 4 - Adaptive Intelligence

AI shapes how the product responds in real conditions, drawing on data, feedback, and context. Teams have strong data foundations and the confidence to interrogate, refine, and continuously improve model behaviour as part of their product practice.

Three Things You Can Do Right Now

If you’re trying to strengthen AI in customer experience without jumping straight to new features, these are three practical places to start:

Start with friction, not features. Identify where customers slow down or lose confidence, then trace those moments back to the internal processes behind them.

Use AI to strengthen decisions before changing the interface. Let internal AI capability improve reliability, consistency, and flow, rather than rushing to surface it in the product.

Check whether customers feel the difference. Pair internal performance metrics with simple feedback on clarity, effort, or confidence in the journey.

This is exactly the kind of work Sorai supports teams with, helping internal AI capability shape product decisions that improve customer experience. Visit our services page to learn more about what this looks like in action: https://www.soraiglobal.com/services

Looking Ahead

The AI Productisation gap is not a sign of slow progress. It reflects the reality that meaningful transformation begins inside the organisation before it reaches the customer.

As teams continue to strengthen their data, workflows, and governance, the space between internal capability and customer experience starts to narrow.
Journeys become cleaner, decisions become more reliable, and the service gains a steadier rhythm that customers can trust.
AI then settles into its most effective role. It supports the experience quietly, shaping outcomes without needing to occupy the spotlight. Organisations that focus on these foundations will be well placed to deliver customer value with confidence.

Sorai: Where Product-Led AI Meets Customer Experience

If you’re exploring how internal AI capability can translate into better product decisions and more reliable customer experiences, Sorai supports teams working through that transition in a practical, product-led way.

Contact the team directly to discover what this could look like for your business: → david@soraiglobal.com