As AI becomes more deeply embedded in customer service and contact centers, many organizations frame the conversation around efficiency: faster resolution, lower costs, fewer agents.
But that framing misses both the real risk and the real opportunity facing brands that serve large, price-sensitive customer bases.
The question isn’t whether AI will transform customer service. It already has. The question is whether companies will use it simply to check a box or to build trust and advocacy in an environment where good service is becoming increasingly rare.
The Less Service Myth for Mass-Market Customers
Much has been written about the emergence of a K-shaped economy where the middle class is hollowing out, wealth is concentrating at the top, and most consumers fall into increasingly price-sensitive tiers.
That shift has influenced how many organizations think about customer service. High-value customers are seen as deserving high-touch experiences, while mass-market customers are routed toward speed and automation.
But customers don’t experience this as efficiency. Instead, they experience it as a difference in care.
There’s a common assumption that customers outside the top income tiers expect less when it comes to service. In reality, they want less effort. They’re perfectly comfortable solving simple issues on their own: as long as the experience is fast, clear, and reliable.
This is where AI excels. Straightforward requests, such as checking an order status, updating account information, or resolving basic issues, can and should be handled immediately through self-service.
In fact, Zendesk reported that 51% of consumers say they prefer interacting with bots over humans when they want immediate service.
When done well, automation respects the customer’s time and reduces friction across the system. But we see problems emerge when automation is treated as an endpoint rather than part of a broader service strategy.
When “Checking the Box” Breaks Trust
Everyone has a story about bad customer service, and those stories tend to stick. Importantly, 95% of consumers say that customer service has an impact on their brand loyalty.
And as AI becomes more widespread, truly good customer service may become even more scarce. It’s not because the technology fails, but because it’s implemented without judgment.
Too often, AI systems are designed to follow rules rather than produce desirable outcomes. They confirm requirements, adhere to policy, and complete workflows. Yet they can sometimes miss the broader context of what the customer actually needs.
Humans Must Own the Outcomes
One of AI’s greatest strengths is consistency. Well-designed systems can reduce routine errors, surface patterns in customer issues, and ensure that simple requests are handled accurately every time. But accountability for the customer experience (CX) doesn’t disappear just because a system is automated.
Ultimately, humans are still responsible for outcomes. That’s why AI must be designed with humility as well as intelligence.
An effective AI experience recognizes when it has reached its limits. There needs to be a point where the chatbot or other AI tool can say “I don’t have the answer, but I can get you to someone who does,” before turning the interaction over to a human.
That handoff is critical. Customers don’t just want resolution; they want reassurance that a real person is available when the situation becomes complex, emotional, or high-stakes.
When escalation is difficult or opaque, frustration rises. But when it’s seamless and intentional, automation becomes an asset instead of a barrier.
The Right Work for Automation
The most effective customer service models don’t ask whether AI or humans are better. They ask where each belongs.
Simple, repeatable interactions should be automated. More complex moments, which are those involving judgment, exceptions, or trust, should escalate to people quickly and cleanly. The mistake many organizations make is treating escalation as failure rather than as a core feature of good service design.
This is especially important at scale. When customers feel trapped in automated loops with no clear path to a human, dissatisfaction grows rapidly. But when escalation is designed thoughtfully, AI helps manage volume while people focus on the moments that matter most.
...problems emerge when automation is treated as an endpoint rather than part of a broader service strategy.
Leaders need to look closely at their data. Examining call resolution rates, repeat contacts, and escalation drivers can help make more deliberate decisions about what to automate and what to protect as human-led work.
Why People Still Win the Retention Game
As AI becomes more accepted across industries, it will play an increasingly visible role in customer care and contact centers.
Over time, certain interactions may be handled end-to-end by intelligent systems. However, in the foreseeable future, people still win the customer retention game. Especially when they’re placed intentionally and supported properly.
The next evolution of customer service won’t be defined by how much is automated, but by how well automation and human judgment are orchestrated.
High-volume, low-complexity interactions will continue to move toward self-service. Moments that involve trust, exceptions, or emotion will demand faster escalation to empowered people.
At the same time, AI will increasingly operate behind the scenes. Quality assurance (QA), coaching, and performance feedback will become continuous and data-driven. Instead of replacing agents, technology will help develop them, raising the overall standard of service across contact centers.
Looking ahead, the brands that stand out won’t be the ones that deploy AI the fastest. They’ll be the ones that design customer care intentionally, using automation to remove friction, humans to build trust, and data to ensure the system learns over time.
In a marketplace where customers quickly forget average service and never forget bad service, the ability to combine efficiency with accountability may become the defining advantage.