Front pagesponsored · Heedify
CX AI14m · 10:31 GMT · 4 min read
How a Teams-Native AI Receptionist Makes Automation Feel More Human
As Voice AI takes on a more active role in customer service, organizations must define where it can act independently, where employees add value, and how both can work together within Microsoft Teams to reduce customer effort without creating disconnected experiences
Organizations adopting Voice AI need a practical path from basic call handling to service automation that can act within established systems.
After addressing the operational challenges of connecting Voice AI to Microsoft Teams, they must now decipher what makes an interaction responsive and useful.
To ensure success with the technology, CX leaders should stop assessing its quality on autonomous resolution alone.
Mefteh Werghemmi, CEO & Co-founder ofHeedify, spoke to CX Today about the importance of setting clear boundaries for Voice AI automation.
“Every business has its own specific edge cases, and they are not always as rare as they may seem,” he said.
“If even 5% of customers are unhappy because an escalation is poor or happens at the wrong time, that represents a significant number of customers receiving a bad experience.”
The Value of Useful Dialogue
Today, voice AI is only as effective as its ability to resolve customer needs with minimal effort.
This includes understanding the request and retrieving relevant information whilst ensuring that decisions are made in accordance with defined business rules and that appropriate action is taken.
Defining clear boundaries allows AI to resolve appropriate requests independently, while requiring additional verification or human intervention when necessary.
For regulated organizations, authentication, data access, and AI agent actions require additional controls without limiting opportunities to automate routine services.
“By automation, we mean a voice agent that can take action in the system and solve the customer’s problem, not simply provide information,” Werghemmi explained.
The system must understand the request, retrieve relevant information, make decisions in accordance with defined business rules, and take appropriate action.
In fact, conversational LLMs can make these interactions more flexible by asking follow-up questions, seeking clarification, and responding to the context of the conversation.
“The voice agent can ask follow-up questions and respond naturally, rather than following a rigid, robotic script,” he noted.
These capabilities allow AI to complete tasks within existing business systems, reducing the need for human intervention in straightforward cases.
Rather than imitating employees, AI must ensure routine service feels clear, responsive, and capable of producing a useful outcome.
Transparency remains essential, however. As Werghemmi noted:
