For small businesses, the allure of artificial intelligence can be hard to resist. AI’s capacity for automation can be a powerful multiplier, giving a business the ability to compete with larger organizations. From content generation to workflow automation, AI can unlock next-level efficiency, allowing businesses to do more with less.
But transferring tasks from human workers to AI agents requires some careful consideration, especially when those tasks involve customer conversations. Deploying AI in customer service can cut costs considerably while also making care more accessible. If not deployed correctly, however, AI can cause customer service nightmares that are very difficult to recover from.
Unleashing AI Too Quickly Can Lead to Customer Frustration and Churn
Perhaps you’re considering bringing in AI voice agents to manage your customer care because you saw an impressive demo that handled questions quickly, confidently, and with a level of competence you wish you could get consistently from your human agents. But keep in mind that every demo runs on the 20 questions the vendor already knows the answer to. Your callers ask the other 400.
And when you’re considering how customers will react when your agent can’t meet their needs, remember that a bad interaction costs a small business far more than it does a large one. Your brand is usually your owner’s name, which means brand reputation has higher stakes.
One of the biggest risks small businesses face when they bring on AI is not noticing failures. You’ll most likely be adopting AI because you’re struggling to keep up with your current tasks. Adding “Diligent AI Oversight” to your to-do list probably isn’t your goal.
But AI failure is silent. Nobody complains to the AI, which would otherwise trigger a message that gets passed up the chain of command. Customers who don’t like the care experience you’re providing simply hang up and go elsewhere.
The mistakes AI tends to make can also add to your costs. The risk isn’t rudeness, but confident wrongness. An agent that invents a delivery date has made a promise you now have to honor or take back.
AI Platforms Should Be Tailored to Your Company and Your Customers
As you evaluate AI voice agents, the key consideration is their capability to work with your customer base. The right platform will provide speech recognition tuned to your customers’ accents and your vocabulary, including personal names, street names, or product names. Generic automated speech recognition mishears exactly the words that matter.
Tuning the platform correctly requires making sure answers come from a real source of truth, like brand glossaries, SOPs, or compliance scripts. Proper training also requires proper guardrails that define what an AI agent can promise, what it can never discuss, and when it should trigger a transfer. And regardless of your guardrails, the agent should have the permission to say, “I don’t know. Let me get someone else who can help you.”
From a technical performance standpoint, businesses should demand platforms with barge-in capabilities and proper endpointing. Those are the things that make a call feel normal. Sub-second responses are also important. To make sure the platform can provide them, check the 95th percentile, not the average.
Transcripts and recordings are another technical must-have. To be valuable, they should be searchable. If you can’t read what it said to Mrs. Smith on Tuesday, you don’t have a system.
Be Strategic About The Customer Calls You Automate First
The best place to begin with voice AI is with calls that are high volume, low variance, reversible, and verifiable. Those calls should also involve low emotional stakes.
The calls you’re missing altogether, such as those that come in after hours or when all of your lines are busy, can be your starting point. With those, you aren’t competing with a great human, but with a voicemail, which makes them high-return and low-risk.
Allowing AI to manage calls about order status, opening hours, stock, pricing, and parking is also a good starting point. These are high-volume calls that require almost no judgment.
Bookings, reschedules, confirmations, and reminders are structured and reversible. If you use AI in this area to reduce no-shows, you’ll create an opportunity to improve your revenue.
Keep humans on any calls that might involve dealing with a caller who’s already angry when the phone connects, such as billing disputes, cancellations, complaints, and anything clinical or legal. When payment is involved, put the customer in human hands.
Look Beyond Containment to Evaluate Effectiveness of Voice AI
Containment, which is a bot’s success at managing an interaction on its own, is often cited as a key metric for voice AI platforms. In reality, however, it’s a vanity metric. A bot that refuses to transfer scores brilliantly on containment, but isn’t necessarily helping customers.
One of the best metrics to measure, and one that’s easy to calculate, is repeat contact from a customer within 72 hours. If they call back about the same thing, then it’s safe to assume their issue wasn’t resolved.
Escalation is also important to measure, but you should value escalation quality over escalation rate. The system works when the human agent taking over gets the context that was shared with the bot. A bad handoff, in which the customer needs to start from square one, is worse than no automation.
If you want a preview of how escalation is handled, say “agent” mid-sentence and see what happens. Does the transcript travel? You also want to know what happens at 11 p.m. when there’s no human to transfer to, as well as what happens when the model is slow or down. Never forget that the people who hit the failure path are the ones who write the reviews.
Answer rate should be tracked, but you want to compare it against a real baseline that includes the calls you used to miss. This is usually where the whole business case lives.
Bookings, no-shows, conversion, and revenue per answered call are also meaningful metrics. If those move, the rest is noise.
Be Aware of These Signs That You Aren’t Ready to Integrate Voice AI
The following checklist can help you determine whether you’re ready to leverage AI for customer care. If you admit to any of the following, it’s a good sign you aren’t ready:
- Your answers aren’t written down anywhere. If you can’t train a new hire from a document, you can’t brief an AI either.
- You can’t describe what a good call looks like.
- Bookings are in a diary, orders are in WhatsApp, or everything is stuck on one laptop. If this describes your operations, you don’t have anything to effectively integrate with.
- Your volume is low, but you’re not missing calls.
- The phone isn’t ringing enough. That’s a demand problem, not a call-handling problem.
- You’re doing it because a competitor announced it.
- Prices change weekly, but the docs don’t.
- Leadership expects zero errors. If the tolerance is genuinely zero, don’t start.
It’s also critical that someone in the business owns your AI efforts. Spending two hours per week on this is typically enough. This oversight should include listening to recordings of 30 calls a week and tagging why each went wrong. Four or five problems detected this way will usually explain any issues you’re experiencing.
Recent statistics show that most small businesses are now investing in AI. But they also show that small business leaders are worried about the reputational damage that can result from unleashing AI in the wrong places.
If you want to avoid that kind of damage, don’t be too quick to allow your customers to engage with AI. Before going live, make sure your agents know your business, are properly monitored, and are deployed strategically. Those steps will keep your AI investment from becoming a liability.
Abhi Kumar is the Co-Founder of Voicing AI, an enterprise voice AI company focused on helping organizations deploy reliable AI solutions in regulated industries. His background spans enterprise software and venture capital, including roles at Oracle, Microsoft, and M12, Microsoft’s venture arm, where he evaluated and invested in enterprise technology companies globally.
Through his experience assessing which technologies achieved enterprise adoption, Kumar developed a deep understanding of the challenges organizations face when implementing AI at scale. Today, he focuses on bridging the gap between AI innovation and production reality by building voice AI solutions designed for real-world enterprise environments, including banking, healthcare, aviation, and telecommunications.
Photo courtesy Mariia Berezovsky for Unsplash+
