In many industries, customer experience has become the primary competitive battleground. While products and services can be replicated, customers will remember how their experiences with companies made them feel. Was help easily accessible and truly beneficial? Were problems solved quickly? Did customer service interactions feel personal? It is these fundamental customer touches that separate a phenomenal experience for which customers will keep coming back from an average (or below average) interaction that causes no such reaction and is quite frankly forgettable?
From an enterprise perspective, artificial intelligence is becoming an important strategic investment for customer service because it can improve the brand experience at scale. This year, 98 percent of companies are increasing their AI communications budget, but on their own, bigger budgets don't directly equate to stronger results. When AI customer communications fail, customer service tends to take the biggest hit. Diagnosing and fixing the issue is really where organizations will sink or swim.
To truly benefit from AI in customer communications, organizations must first understand how to use it properly, building a proactive, personalized infrastructure that prioritizes customer loyalty and brand trust.
For leaders, this means that the more pertinent consideration should not be whether to use AI in customer service. Rather, it's whether the organization is prepared to manage AI as carefully as every other customer touchpoint.
Why AI Magnifies Every Customer Interaction
Every single customer interaction represents the company, and we can't lose sight of that core truth. When AI works well in customer service and its value is clear, consumers welcome it. But when AI produces errors, it's not the technology that's blamed, it's the company. And that's when customer trust takes a hit. Considering the scale of resolution at which AI agents in CX operate , one poor workflow can affect thousands of customers, even simultaneously.
Inside organizations, AI failures are often discussed in operational terms: root cause analysis, remediation plans, incident reviews. These conversations are necessary yet not reflective of how the failure is understood by the customer on the other side of the interaction.
A confusing answer, an inaccurate recommendation, or an interaction that goes in circles, though viewed as an operational failure to a company, can often be viewed as a brand reputation issue to a customer. Sinch research shows that 34 percent of companies see reputational damage as a primary consequence of an AI rollback. Reputational damage, unfortunately, does not have a clear resolution path. It takes time, authenticity, and effort to earn back trust: owning mistakes, being transparent, and proving that these issues won't happen twice.
If there is one thing that customer service leaders know to be true, it's that consumer expectations evolve constantly. Today, quality products and positive experiences are the top reasons people are loyal to a brand. Even in an inflationary economy consumers are, on average, willing to pay 25 percent more to stick with a brand they trust.
Since the nature of business is largely built around delivering what customers need, this can sometimes influence product changes, policy changes, communications changes, and regulation changes. Against this backdrop, it also means that AI requires stronger observability mechanisms in model changes. Regular reviews or fixes when things break isn't enough, especially when hallucination and off-brand responses account for 22 percent of AI failures. Companies need to be constantly monitoring inputs and outputs, as what happens during agent conversations might very well be a black box without effective guardrails in place.
AI agent failures split into two fundamentally different problems: infrastructure failures (like personally identifiable information exposure) that stem from data and access design and can be engineered away entirely, and model failures (like hallucination) that are inherently probabilistic and must instead be contained through narrow scoping, layered guardrails, and continuous feedback loops.
Prudent AI observability is the best way for organizations to diagnose problems before customers complain.
When an AI communications agent fails, most customer service leaders focus on the queue spike and customer complaints. Those are the visible costs. There is a third cost that receives far less attention: what failure does to the engineering team tasked with preventing the next one.
Aspects of service like identifying where conversations break down, recognizing repeated customer frustration, understanding why containment rates fall, spotting hallucinations early, and measuring customer sentiment aren't prioritized, when this is arguably where organizations would reap the most benefit.
The Sinch study also showed that 84 percent of AI communications engineering teams currently spend at least half their time building guardrails and safety controls. PII masking, rate limiting, retry logic, and compliance enforcement are the focus. As programs mature, this compounds since it often means there are more agents involved, more channels to manage, and more compliance requirements to meet. When these guardrails need to be custom engineered because they aren't provided at the platform level, each adds another layer that must be built, maintained, and rebuilt when something breaks.
From a process standpoint, if a company's best engineers spend the majority of their time rebuilding infrastructure, that leaves little time for actually improving the customer experience. Aspects of service like identifying where conversations break down, recognizing repeated customer frustration, understanding why containment rates fall, spotting hallucinations early, and measuring customer sentiment aren't prioritized, when this is arguably where organizations would reap the most benefits. In a lot of cases, this comes down to the tools and vendors that organizations use. The best platforms have underlying infrastructure that makes guardrails native. The extra time engineers have when they don't have to rebuild infrastructure from scratch for every deployment opens time for them to focus on what matters most for brand trust: the customer experience.
Customer service is one of the few places where technology decisions are immediately visible to businesses' most important stakeholders, the customers. As AI becomes the first point of contact for millions of customer interactions, organizations must stop thinking about implementation as the goal. The real competitive advantage lies in continuous improvement. Winning organizations will treat AI like any other customer touchpoint: meaningfully watched, systematically connected, meticulously consistent, and constantly improved. This kind of care will create the seamless experiences that drive long-lasting brand loyalty.
Heather Arrington is senior vice president of customer excellence at Sinch.