The most critical requirement for scaling Voice AI in contact centers is implementing CX observability before deployment – not after failures occur. In our conversation with Franco Trimboli, Chief Product Officer at Operata, he explains why observability protects brand reputation and what specific failure points organizations encounter when deploying Voice AI without proper visibility.
Why Organizations Are Rushing to Deploy Voice AI Now
Three forces are driving Voice AI adoption in contact centers today: CCaaS incumbents like Genesys, NICE, and Amazon Connect are converting their IVR systems into generative AI voice replacements; startups like PolyAI are positioning Voice AI as the new front door to customer experience; and developer platforms like LiveKit and Pipecat are enabling experimentation with minimal setup.
Business leaders feel pressure from multiple directions: cost reduction through automation, the opportunity to serve experiences that were never economically viable with human agents, and the promise of hyper-personalized customer interactions. As Franco explained:
“Voice AI brings this generational leapfrog in capability. It can do more than ever, across more modalities, more cost-effectively and in real time. But with that promise comes urgency – and urgency without visibility creates risk.”
Operata’s Maestro platform supports over 50 CX platforms including Amazon Connect, Genesys Cloud, NICE CXone, and Voice AI providers like PolyAI, LiveKit, and Pipecat, giving organizations unified observability across their entire Voice AI stack.
Related: Inside Operata: The Rise of CX Observability for AI-Powered Contact Centers
Where Scaling Voice AI Beyond Pilots Breaks Down
Early Voice AI pilots typically succeed because they target small, low-risk use cases with controlled conditions. These wins signal that Voice AI is ready to scale, but that assumption creates three critical pressure points.
Pilots use tightly scoped scenarios that rarely reflect the full complexity of real customer journeys. High-stakes interactions, account disputes, technical troubleshooting, compliance-sensitive transactions, require Voice AI to handle ambiguity and multi-step processes that controlled tests don’t expose.
“Pilots are controlled scenarios. They rarely map cleanly across the entire customer experience. The real question is: how do you balance business risk while deploying non-deterministic technology across interactions that are higher stakes?”
The Voice AI stack includes authentication systems, payment gateways, CRM lookups, knowledge bases, and handoff logic. Each dependency introduces potential failure points that multiply as deployment expands.
Successful Voice AI deployment also requires cross-functional strategy involving service designers, product managers, compliance teams, and operations leaders, not just developers. Organizations that treat Voice AI as a plug-and-play feature miss requirements around safety, customer outcome measurement, and regulatory compliance.
What Breaks When Voice AI Meets Live Customers
Voice AI failures fall into two categories: behavioral performance issues and technical performance issues. Both erode customer trust in live interactions.
Behavioral Performance: AI Acts Unpredictably
Unlike deterministic software, AI is non-deterministic, it can respond in ways testing environments never predicted. This creates business risk in regulated industries like finance, healthcare, and banking, where compliance violations or PII mishandling can trigger regulatory consequences.
“This is especially important in highly regulated markets that require compliance reporting—finance, banking, healthcare. Testing can catch certain scenarios, but it can’t predict every real-world interaction.”
Technical Performance: New Failure Points Appear
Adding Voice AI introduces three common technical failure modes that Operata customers report in production:
Latency-derived silence: When Voice AI queries an external knowledge base or CRM system, processing delays create awkward silence. Customers can’t tell if the system is thinking or if the call dropped.
Model fallback degradation: When the primary AI model becomes unavailable, systems fall back to a less capable backup model. Customers immediately notice the drop in conversation quality.
Network quality impact on WebRTC audio: Voice AI services run over WebRTC, meaning real-time network conditions—jitter, packet loss, ISP routing issues, directly affect audio quality. Traditional CCaaS monitoring doesn’t capture these signals.
“These aren’t edge cases. These are the realities of deploying Voice AI in multi-vendor, multi-platform CX environments. And if you can’t see them happening, you can’t fix them.”
Why CX Observability Must Be Deployed Before Voice AI, Not After
Traditional monitoring tools provide high-level metrics, containment rates, average handle time, CSAT scores, that mask what’s actually happening in customer interactions. CX observability captures the continuous state of every interaction at every moment, providing the granular, contextual data needed to diagnose Voice AI issues.
“I fundamentally believe that CX observability is the safety net protecting your entire brand reputation. Without it, you’re flying blind to customer frustration in real time.”
CX observability captures every interaction event, turn, and state change in real time, along with supporting voice, network, and telephony data. This enables teams to understand not just that something failed, but why it failed and what upstream or downstream factors contributed.
Voice AI services depend on multiple external systems: authentication, payment processing, knowledge bases, CRM data, and handoff logic. A failure in any dependency can cascade into the Voice AI experience. If observability only monitors the AI layer, root causes remain invisible.
Operata’s Maestro platform enables teams to start with a high-level metric, like containment rate and drill down into specific failed interactions to see what actually happened second-by-second across the entire stack.
“You can start with a high-level metric, drill down into specific failures, and see what actually happened on a second-by-second basis. You might see a containment issue and trace it back to a failed API call, a network problem in your own environment, or a latency spike in a RAG lookup. That’s the difference CX observability makes.”
What to Monitor When Deploying Voice AI
Organizations deploying Voice AI should instrument these areas from day one:
Measure A/B experiments rigorously in early production. Track what triggers failures and human handoffs to uncover gaps in contextual understanding and latency issues that lab environments miss.
Measure interaction quality across the entire customer journey. Observability must span telephony, IVR, Voice AI, agent handoffs, and backend systems, not treat Voice AI as an isolated component.
Analyze the 20% of calls that don’t succeed. Failures reveal where the system is brittle, whether from AI limitations, technical failures, or process gaps.
Set and track KPIs for AI agents and vendors. Track task success, resolution quality, and customer sentiment, not just containment and handle time.
Implement observability before deployment. If you can’t measure Voice AI behavior and performance, you can’t fix problems when they occur or scale safely.
Four Critical Dimensions for Voice AI Observability
- Safety and compliance: Can you prove the Voice AI system handles PII correctly, adheres to required scripts, and escalates appropriately when policies are violated?
- Customer outcome measurement: Does the Voice AI actually resolve customer needs? Track task success, first-contact resolution, and downstream behaviors, not just generic CSAT scores.
- Model quality and consistency: Monitor for model drift, performance degradation, and variance between primary and fallback models.
- System reliability across dependencies: System-level observability correlates Voice AI behavior with the health of upstream and downstream services, revealing root causes that AI-only monitoring cannot see.
“Without system reliability, you can’t serve customers. Without safety, you can’t scale. Without great customer experiences, you don’t have confidence to bring Voice AI to new areas of your business. And without model quality visibility, you don’t know if it’s good one day or bad the next.”
Start with Observability, Then Scale Voice AI
The most important decision organizations can make when deploying Voice AI is implementing CX observability first, before scaling, before production deployment, and before customer trust is at risk.
“You can’t fix what you can’t measure. Observability is about capturing the state of the system to examine and uncover its performance. It’s the safety net protecting your brand reputation. Without it, you’re flying blind – and the last thing you want is customers experiencing frustration in real time.”
The Voice AI capability leap is real. The business case is compelling. But without CX observability, Voice AI will break under the complexity of multi-vendor, multi-platform contact center environments and take customer trust with it.
Organizations that instrument Voice AI with observability from the start can experiment confidently, scale safely, and deliver the ROI that Voice AI promises.
Learn more: Inside Operata: The Rise of CX Observability for AI-Powered Contact Centers
