Artificial Experience: How AI can help uncover the hidden gaps in customer experience
The art of customer experience goes beyond simply listening to what customers say. It requires understanding the signals beneath the surface – the subtle behaviours, unmet needs, and unspoken expectations that often reveal the most valuable insights. It is exactly in this space where AI can create significant new value, writes Wareef Alowerdi, Associate Consultant at Elm.
Most of what goes wrong in a complex customer experience (CX) never shows up as a complaint across public services, B2B ecosystems, and experience-led sectors. The most serious gaps surface earlier and far more quietly: a hesitation before submission, a requirement read a few times before it makes sense, a handoff between departments that no one really owns, or the uncertainty about a decision that was never shown to the customer.
Often the beneficiary just closes the tab without a word, and the organization finds out far too late if it ever finds out at all.
This matters more now that the foundation of digitization is firmly in place. Saudi Arabia is a good example with its 2025 Digital Experience Maturity Index for government services reaching an advanced 86% across dozens of platforms. When getting services online is no longer the differentiator, the real edge shifts to something harder: understanding what really happens inside the journey and whether the organization is built to respond to what the customer never said out loud.
Artificial Experience
This is where ‘Artificial Experience’ begins. The term is a deliberate blend: the Artificial of artificial intelligence joined to the experience of Customer experience, naming the exact point where the two meet.
Artificial Experience (AX) is the design of how AI models, predictive workflows, and connected data work behind the scenes, alongside physical and digital touchpoints in customer experience, to make the journey proactive, contextual, and alive. Put simply, it reads what the customer never says.
In complex public sector and B2B environments, customer experience is rarely a single clean interaction. It is a long journey that runs through multiple regulations, compliance checks, and internal approvals and the person moving through it is not a casual shopper. They might be a citizen reaching for a public service, an investor launching a new enterprise, a business working its way through corporate licensing, or a fan whose night out depends on multiple steps lining up: the ticket, the entry gate, the seat, the queue, the way back out.
The common mistake is to treat AI in this space as little more than a faster helpdesk, a slicker chatbot, or a quicker email reply. The real problem is rarely missing support; it is that the journey only reacts waiting for the customer to get confused, stall, call, or quit before anyone realizes a bottleneck exists. AX flips that logic, sensing friction as it forms instead of patching the journey after it has already broken.
Uncovering the hidden gaps
There is a deeper reason this is so hard to catch. When you sit a customer down and map their journey, they tell you about the things that annoyed them, the long wait, the unhelpful answer, the page that would not load. They rarely tell you about the hidden gaps, the unclear requirement, or the handoff that quietly stalled because they never saw those parts and often did not realize they were the real cause.
In other words, the feedback you collect captures the noise of frustration far better than its source. This is exactly where AI holds the advantage and where AX earns its place because it does not wait to be told. It reads the behavior the customer leaves behind and surfaces the gaps no one thought to mention.
That changes how services are designed in the first place. It moves organizations away from static journey mapping which only shows how a service was meant to work on paper toward something closer to journey sensing. Sensing watches the service while it is live so it can point to the exact requirement that is causing friction, the cross-entity handoff that is dropping context, and the place where the journey is failing the customer in real time. It might catch for instance that applicants of one business type all stall at the same upload field and then quietly drop off, the kind of single sticking point no complaint log would ever surface.
Designing the CX with AX
Most journeys are not designed badly; they are designed in pieces. Each department owns its step, and no one owns the space between them, which is exactly where the experience quietly falls apart. The maps we draw assume one scenario at one moment, the typical user on a typical day, so the unusual case and the person who does not fit the template slip through unseen.
What AX introduces is ownership of those gaps. And this matters most for empathy: AX does not manufacture it, but widens the circle of who receives it, reaching journeys and people human attention could never notice on its own.
There is a real line of course between an experience that anticipates and one that simply watches. Because AX acts on what the customer never said out loud, it must be governed with care and in public services, that means respecting privacy and consent from the very first day. The point is for these predictive workflows to support human judgment and lift administrative weight, never to quietly decide who qualifies for what on their own.
Ultimately, the advantage will not belong to whoever owns the most digital touchpoints, but to whoever can make a complex, multi-entity journey feel effortless. The best Artificial Experience is the one nobody notices: the requirement made clear before confusion sets in, the data retrieved before it is asked for twice, and a journey that simply works because someone understood the need before it was ever spoken.
