Agentic AI

Getty Images - Ekaterina Goncharova

Enterprises are investing heavily in artificial intelligence to modernize customer service. The promise is clear: faster responses, lower costs, and more consistent experiences at scale. Yet for many customers, the experience still feels fragmented, slow, and impersonal.

The gap between what AI promises and what customers actually experience is becoming more visible. As companies deploy AI to support customer service operations, expectations are rising just as quickly. The result is a growing disconnect between how customer service is designed and how it is perceived.

Niraj Ranjan, CEO of Hiver, an AI-powered customer service platform, says the issue is not the technology itself but how it is being applied.

“AI is everywhere in customer service conversations, but most companies are still figuring out how to make it effective in real workflows,” says Ranjan. “It is easy to bolt AI onto a platform, but it is much harder to make it work in a way that actually improves outcomes for both customers and support teams.”

Customers Expect More, But Trust Remains Fragile

According to a recent Prosper Insights & Analytics survey, 40.6% of consumers say AI needs human oversight, while 40.1% say AI can provide wrong information. These concerns point to a clear trust gap that has not kept pace with adoption.

That gap becomes more pronounced when it comes to privacy. The same survey found that 33.5% of consumers are extremely concerned about how AI uses their data, and another 26.5% are very concerned. Together, this represents a majority of consumers who remain uneasy about how AI operates behind the scenes.

Customers are not rejecting AI outright. They are raising the bar. Speed and convenience are expected, but not at the expense of accuracy, transparency, or accountability.

When It Matters, Customers Still Choose Humans

This tension becomes most visible in real interactions. While companies are scaling AI-driven support, customers continue to show a strong preference for human involvement, especially in higher-stakes situations.

According to a recent Prosper Insights & Analytics survey, 82.7% of consumers prefer to speak with a live person for banking-related support, compared to 17.3% who prefer an AI chat program. A similar pattern appears in healthcare, where 83.7% prefer human interaction.

Even in lower-stakes scenarios such as online shopping, 69.2% of consumers still prefer a live person over AI.

These patterns are consistent. Customers are open to automation, but not when it compromises clarity, trust, or resolution quality.

The Real Problem Is Not AI. It Is The System Around It

Many of the challenges companies face today stem from how customer service systems were originally designed. Legacy tools were built for a different era when communication volumes were lower, and expectations were easier to manage.

As businesses scale, these systems often become fragmented. Teams rely on multiple tools to manage conversations across channels. Context is scattered, ownership is unclear, and workflows remain manual in critical areas.

Nitesh Nandy, Co-founder at Hiver, has seen this pattern across support organizations.

“In most environments, AI is being layered on top of systems that were never designed for the level of speed and complexity we see today,” says Nandy. “When the underlying workflow is fragmented, adding AI does not fix the problem. It often makes the gaps more visible.”

This fragmentation creates operational friction. Conversations are routed inconsistently. Teams struggle to track ownership. Important context is buried across systems. As volumes increase, these inefficiencies compound, leading to slower responses and inconsistent experiences.

The momentum behind AI is undeniable, but so is the need for balance. Hiver’s recent report found that 43% of organizations are already investing in or evaluating AI for customer support, even as teams continue to prioritize human oversight.

The momentum behind AI is undeniable, but so is the gap between adoption and impact. McKinsey’s State of AI research shows that while AI adoption is widespread, only a minority of organizations are realizing meaningful business outcomes from it. Much of the challenge lies in execution—most companies struggle to scale AI beyond isolated use cases or embed it into real workflows. Research from IBM’s Institute for Business Value reinforces this trend, finding that many organizations are still stuck moving AI from pilot to production.

AI Adoption Is Outpacing Understanding

Another challenge is that consumer understanding of newer AI models remains limited. According to Prosper, 68.8% of consumers say they have not heard of agentic AI, one of the most discussed emerging trends in the space.

Even after being introduced to the concept, sentiment remains cautious. Only 17.5% say agentic AI is a good idea, while 40.9% say it is not, and 41.6% remain unsure.

AI innovation is moving faster than both consumer trust and comprehension. Without clear communication and thoughtful implementation, new capabilities risk adding confusion rather than value.

The Shift Toward Human Plus AI Service Models

Organizations that are seeing meaningful improvements in customer service are taking a more balanced approach. Instead of pursuing full automation, they are designing systems where AI and humans work together.

In these models, AI handles repetitive tasks such as triage, routing, and drafting responses. This allows human agents to focus on interactions that require context, judgment, and empathy.

Ranjan describes this as a more practical approach to AI adoption.

“The goal is not to replace people. It is to remove the friction that slows them down,” he says. “When AI takes care of the busywork, teams can focus on delivering better outcomes for customers.”

This model aligns more closely with customer expectations. People are open to faster service, but they still expect human involvement when it matters.

Turning AI Potential Into Real Performance

As AI continues to evolve, its role in customer service will expand. But its effectiveness will depend on how well it is integrated into real workflows.

Companies that focus on operational clarity, unified systems, and human oversight are better positioned to realize the benefits of AI. Those that rely on fragmented tools and isolated automation are more likely to encounter the same challenges that have limited progress so far.

The future of customer service is not defined by AI alone. It is shaped by how effectively organizations combine AI with human expertise to deliver consistent, reliable, and meaningful experiences.

For customers, the expectation is simple. They want fast, accurate, and trustworthy support. Meeting that expectation requires more than adopting AI. It requires building systems that reflect how customer service actually works today.

Disclosure: The consumer sentiment study referenced above was conducted by my company, Prosper Insights & Analytics. This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Bloomberg, and the London Stock Exchange Group for economic benchmarking.