5 Things People Wish They Knew Before Using Voice AI Agents
Thinking about adding voice AI to your contact center? This webinar breaks down the biggest “wish we’d known that earlier” lessons from real deployments. We’ll cover what actually works in production, how to integrate them cleanly into enterprise systems, and how to build an experience that’s reliable, compliant, and scalable.
What contact center leaders are really worried about
At Balto, we’ve done our research. These are the most common complaints and questions that contact center leaders have about voice AI agents.
- Customers noticing the handoff. The transition from AI to human has to feel completely natural or it breaks trust instantly.
- Generic bots that don’t fit the business. Leaders want AI built around how their operation actually works, not a one-size-fits-all solution.
- Disrupting existing workflows. The goal is AI that works alongside their people, not something that breaks what’s already working.
- Compliance risk. Voice AI agents need to be held to the same standards as human agents, not treated as a workaround that creates new liability.
- Lack of visibility. Nobody wants AI running in the background with no way to track, measure, or improve it over time.
Tips for a Smooth Voice AI Implementation
1. When evaluating a vendor, visibility and handoffs are a great first step
The best contact centers hold their AI agents to the same coaching standards, QA benchmarks, and performance metrics as their human agents. If your AI is running calls with no visibility or feedback loops, that’s a recipe for problems down the road.
Handoffs are also where a lot of voice AI falls apart, and it’s one of the first things you should pressure-test in any vendor evaluation.
2. Rely on vendors that review your data before recommending where to begin
Understanding where voice AI actually makes sense requires looking at your real call data. What matters is your call volumes, automation complexity, handoff triggers, and the integrations required for a clean transfer.
The output of that analysis should be a clear picture of which automation opportunities make sense for your business and what those flows should look like. If a vendor is asking you to pick use cases off a menu without looking at your data first, you’re just guessing.
3. Look for code-built agents, not workflow builders
There are two broad approaches in the market right now: self-service workflow builders where you design the conversation flows yourself, and code-built agents where a dedicated vendor team builds, tests, and maintains the agent for you.
The self-service route sounds appealing with full control, fast iteration. But in practice, you’re picking from a set of pre-determined use cases that might not fit your business and rigid templates break down fast when callers go off script.
At Balto, we don’t expect you to be an expert in AI as well as your own contact center. Let our team of linguists, prompt engineers, and machine learning experts take care of the rest.
4. Bring your required systems to your vendor so you can prioritize integration
Your human agents use multiple tools on each call, from querying your CRM, pulling from your knowledge base, checking scheduling systems in real time, and sending follow ups. Your voice AI agent needs the same ability. Without those integrations, you won’t be saving time or money.
Bring your full tech stack to the vendor conversation early. Ask how many platforms they’ve connected to, what their timeline is for new integrations, and what happens if you’re on something they haven’t worked with before.
What one customer learned after 18 months in production
Corey Dyben, Marketing and Sales Operations Manager at American Medical Sales and Rentals, implemented voice AI in his contact center about 18 months ago as an alternative to a near-shore after-hours contact center.
What went better than expected: Given the demographic he serves, Corey braced for pushback. He didn’t get much. Most customers accepted the AI agent without complaint, and once early calibration issues were QA’ed, the experience came together better than he anticipated.
What he wishes he’d known:
- Building the knowledge base takes real effort. Getting the AI to understand the business the way a trained human agent would took more time than expected. It’s not a setup task you can rush. In many ways it is like hiring a new team member.
- QA shifts, but it doesn’t go away. Corey’s team spends about as much time QA’ing the AI as they did their human agents. It is still a cost savings, but didn’t save quite as much time as he had expected.
- Integration planning shouldn’t be an afterthought. Moving fast to deploy meant missing opportunities to connect the voice agent to other systems. Looking back, he would have mapped out integrations much more deliberately from the start.
- Internal change management matters. Getting buy-in from the existing call center team required a clearer, more thoughtful story. The framing that landed: AI handles what you wouldn’t ask a human to do. Humans handle what AI can’t, like genuinely empathizing with a frustrated customer. They’re not competing; they’re complementing each other.
What to take with you
- Hold your AI agent to the same standards as your human agents, with visibility and feedback loops from day one.
- Don’t choose use cases without looking at your actual call data first.
- Be skeptical of self-service workflow builders. Code-built agents perform better in real-world conditions.
- Bring your tech stack into the vendor conversation early.
- Make time for QA and internal change management. They don’t disappear after implementation.
