Why Most Businesses Should Think Twice Before Using AI for Customer Service

As the founder of a voice AI company, I often find myself advising potential clients against adopting our technology. This strategy might sound counterintuitive, especially to my sales team, but my experiences with numerous organizations have taught me a valuable lesson: placing AI in an unsuitable context can lead to more complications than solutions.

A Case in Point: Law Firm Client Intake

Recently, a law firm approached us with the intention of deploying AI to manage client intake calls. After reviewing their call recordings, I reached a clear conclusion — they weren’t ready for such a transition. Their intake process involved intricate legal questions, clients sharing emotional and often traumatic experiences, and complex eligibility assessments. Handing these responsibilities over to AI would likely result in disaster.

Surprisingly, situations like this occur more frequently than one might expect. The AI hype has led many businesses to believe they must implement AI immediately, yet the truth is that AI excels in specific contexts but can fail miserably in others.

Three Essential Criteria Before Considering Voice AI

Before embarking on an AI journey, your business should evaluate these three critical factors:

1. Predictable Call Patterns

In my analysis of over 10,000 call transcripts across various sectors, I discovered that for some businesses, upwards of 80% of calls revolve around just a handful of conversations — scheduling appointments, answering FAQs, offering status updates, and basic troubleshooting. These predictable patterns are ideal for AI.

However, if each call tends to be unique, it is wise to reconsider. For instance, a mental health clinic I reviewed had no two calls that resembled one another; each client presented complex, personal circumstances that necessitated empathy and attentive listening. In this case, AI would do more harm than good.

To assist businesses, we developed a pattern analysis tool that evaluates call transcripts. If less than 70% of calls feature recognizable patterns, it may be best to hold off on implementing AI. A home services provider found that 85% of their calls were indeed appointment bookings, marking them as excellent candidates, while a B2B software firm revealed that only 30% of their calls followed a pattern, indicating they required human intervention.

2. Clear Escalation Triggers

AI can only navigate failures gracefully if there’s a clear definition of what constitutes a failure. I once observed a company that rolled out a chatbot without any escalation protocols. As a result, the bot attempted to assist increasingly