Is AI Fixing Your Customer Journey, or Just Hiding What’s Broken?

When Resolution Hides the Problem

Imagine an organisation receiving thousands of contacts every month from customers asking why an application hasn’t progressed. Introduce an AI assistant capable of answering most of those questions and, from the contact centre’s perspective, the results could look impressive. More interactions are contained within self-service, fewer reach frontline teams and the cost of dealing with each enquiry falls, yet the process prompting customers to make contact hasn’t necessarily changed at all.

There is a further risk if those enquiries become less visible to the people running the operation. A recurring problem that was once obvious because agents were dealing with it every day could increasingly be absorbed by automation, making it easier to manage without necessarily making it easier to spot. AI hasn’t created the failure, but it may change how visible that failure is within the organisation.

Humans Have Been Hiding the Cracks Too

This isn’t entirely a new problem created by AI, because humans have been compensating for imperfect systems and processes for years. Garry captured this particularly well when talking about the role frontline teams already play:

// “You realise how good your agents are at papering over the cracks.”

Experienced employees know the workarounds, the systems that don’t quite behave as intended and the unofficial routes that get something resolved faster. They can interpret confusing information, work out which team really needs to receive a request and find a way through a process that may look very different on paper. To the customer, that can feel like excellent service, while to the organisation it can disguise just how much human effort is required to make the journey work.

AI introduces the possibility of doing something similar at much greater scale, with technology potentially able to find the information, navigate the process or resolve the resulting enquiry without the customer ever seeing what happened behind the scenes. Removing that effort could be genuinely useful, but making a broken process easier to navigate isn’t quite the same thing as fixing the process itself.

What Are We Actually Measuring?

That distinction becomes important when organisations start assessing whether AI is working. Containment might improve because fewer customers reach an agent, while cost to serve could fall because more interactions are resolved automatically. Customers might even report higher satisfaction because they received an immediate answer rather than waiting in a queue, giving the organisation several perfectly legitimate reasons to view the implementation as a success.

What those measures don’t necessarily reveal is whether the experience that caused the interaction has improved. An organisation could become increasingly efficient at answering “Where is my order?” while overlooking why so many customers don’t know where their orders are, or automate questions about delayed refunds without examining why those refunds are taking so long. As AI gets better at resolving these interactions, efficient recovery can start to resemble a good customer journey even when the underlying experience remains largely unchanged.

None of this means every customer contact represents a failure, nor does it suggest organisations should wait until every process is perfect before introducing automation. Customers will always have questions and there will always be value in making support quicker and easier to access. It does, however, suggest that alongside asking how successfully AI handled an interaction, organisations might also want to understand why that interaction was needed at all.

Fixing the Cause, Not Just the Contact

There may be a bigger opportunity for AI here if organisations look beyond the point where its impact is most visible and use what they learn from customer interactions to look further upstream. Repeated reasons for contact, patterns across conversations and employees repeatedly relying on the same workaround can all reveal where unnecessary demand is being created.

Used in that way, AI could help organisations understand the problems they’re currently becoming better at resolving. Instead of simply handling more contacts or making existing workarounds more efficient, the technology could provide greater visibility into where processes are failing and where the customer journey needs attention.

Perhaps that gives us a more useful way to think about AI transformation. An AI system that efficiently resolves thousands of avoidable problems can undoubtedly deliver value, particularly if customers receive faster and better support as a result. If the insight from those interactions can also help an organisation understand why the problems keep happening, however, the opportunity becomes much bigger than simply automating the contact.

Rather than using AI to become increasingly good at papering over the cracks, perhaps its greater value lies in helping us see where the cracks are in the first place.