Conversational AI has changed the game.
Because while people may say they don’t want to talk to a machine, their behaviour with AI says otherwise.
Consider that:
ChatGPT averages 123.5 million daily active users.
They process over one billion queries a day, according to their most recent reports.
87% of consumers say their chatbot interactions overall have been positive or neutral.
And 62% say they prefer talking to AI if they’d have to wait for a person.
There have been numerous reports, from The New York Times to the Washington Post to Wired to the MIT Technology Review all highlighting how people say they’ve even fallen in love with chatbots, and while these may be extreme cases, the bottom line is this: conversational AI works.
Through the combination of enormous datasets, sophisticated autocompletion, and advances in affective computing, these systems keep getting better at understanding and anticipating what people want.
Pair that with multimodal capacities for audio and video, and it’s easy to see why AI automation has changed the way businesses handle calling.
It’s not right to say a revolution is happening with AI in customer service in 2025, because the truth is: it’s already happened.
In this article, we go back to the beginning, covering what AI-powered calling is, how it works, and how it’s redefined communication for businesses in 2025.
Reaping the Benefits of AI-Powered Calling
“AI-powered automated calling” is a catch-all for conversational AI solutions to business telephonic communication problems, whether inbound or outbound.
These platforms make or field calls entirely without your intervention, combining conversational AI with advanced data processing.
They allow AI to share information, upsell and re-connect, gather customer insights, pre-screen candidates or potential clients, and even do administrative work like scheduling.
They’ve already been deployed across businesses and departments to do the heavy-lifting, freeing the human beings to focus on things they’re really needed for.
AI-powered calling is bringing companies greater:
Efficiency: Exponential cost reductions and freeing up staff at the same time.
Scalability: Making hundreds to thousands of calls simultaneously, with each one tracked and logged.
Personalization: Tailoring calls with business data and making adjustments on the fly to improve mood and understanding.
AI calling automation is already giving these benefits:
Conversational AI Technology: The Mind Behind the Call
Okay, fine, but what is conversational AI?
Let’s look at how it works and why people find it so engaging.
How It Works
Conversational AI reduces language into tiny pieces that can be transformed, analyzed and processed by computers.
And from here, it can also build it back up in the reverse.
AI-calling relies on several things working together, like:
Powerful Data Processing: It all begins with those massive datasets of human language. This is processed so queries and responses are understood with context, intent, and sentiment. That allows on-the-fly adjustments to emulate human communication.
Natural Language Understanding (NLU): This is part of Natural Language Processing (NLP) and how AI parses speech. It identifies keywords, context, and even emotional undertones, to assist with interpretation. For example, if a customer says, “I need help with my bill,” the AI recognizes “help” as intent and “bill” as subject.
Natural Language Generation (NLG): With meaning registered, the system is ready to make the response. Unlike old calling automation systems that needed pre-recorded messages, NLG makes customized dialog that fits business goals and the context of the conversation.
Voice Synthesis: With the words you’re ready for voice, so here AI converts it to audio, giving it a natural-sounding tone and demeanor. Advanced systems go further, using neural voice synthesis, to mimic human inflection and tone even more effectively.
Machine Learning Algorithms: This is all fine and well, but it’s not much good without improvement. What makes AI calling systems work is the capacity to adapt and keep adapting from each interaction. Machine learning refines accuracy and enables this learning to each new scenario. In other words: the more they’re used, the better they become.
Here’s an example of a typical flow:
Conversational AI and IVR
I’d be very surprised if you don’t hate those old IVR (interactive voice response) systems, because pretty much everyone does.
They waste our time by making us wait through long menus, don’t hear us correctly the first time (maybe ever), and never budge from their programming.
And even after you do all that waiting and cringe through misheard choices, you typically still end up on hold to get to a person who can actually do what you called to get done in the first place.
Maybe you’ve had similar experiences with Siri and Alexa too (pre-AI).
Conversational AI has changed this game entirely.
Instead of rigid channels, it brings natural exchange. Instead of listening to options that aren’t quite right, you get your need met or even exceeded on the first go.
Conversational AI allows for:
Natural Conversational Interactions: Just talk to it and tell it what you need. It’s that easy.
Contextual Understanding: Maybe you don’t know what you want, and they can handle open-ended queries, too. That means switching seamlessly between topics, adjusting speed and even, in the best solutions, directly transferring calls to human agents when needed.
Dynamic Responses: The AI gives personalized dialog every time, in real time. You’ve never had this call before.
Affective Computing: Adding AI Empathy in Customer Interactions
Affective computing techniques take this all to the next level. As a field of study that merges computer science, cognitive science, and psychology, it helps give machines the ability to interpret and adapt to human emotions.
By analysing tone of voice, word choice, pace, volume, and context, these approaches empower systems to adapt their responses to a user’s emotional state.
Why Empathy Matters
Just like in life, between real people, empathy aids trust and improves communication. And no, AI doesn’t have real empathy, but the best conversational AI systems successfully emulate it, by:
Using a calm, reassuring tone to de-escalate frustrated callers.
Using positive, enthusiastic language to enhance sales calls.
Adjusting call endings based on need.
Speeding up or slowing down and clarifying based on perceived understanding.
Pete & Gabi: AI-Powered Automated Calling
Now we bring it all together.
Pete & Gabi takes all this, the best from conversational AI and affective computing techniques, and joins it with innovations we’ve made to take it to the next level.
With more than four years and hundreds of thousands of hours spent in development, it’s been put through the paces, learned by doing, and fine-tuned through interactions with thousands of users.
We added things that we found lacking in the industry, like live call transfers, the capacity to work across a multitude of use cases, easy integration with existing systems, and all within a single, scalable platform.
Enhanced Customer Experience
By combining conversational AI with affective computing techniques, Pete & Gabi is already giving customers great experiences, by better understanding what they need and how.
Seamless Human Integration
Pete & Gabi couldn’t be easier and gets on its feet in just days. Working off scripts and even optional voices that fit your needs, it gives you the ability to engage, pre-screen, qualify, and then transfer right to your closer to get deals done.
Conclusion
We opened by saying conversational AI has already changed the game, and it has. Just ask our customers who are already using it to make massive strides in sales, reduce churn, seize on leads, and empower their engagement.
In the recruiting field, it’s a fixture for firms that pre-qualify candidates, confirm, schedule and exchange needed details, all automatically and at the time that’s best for candidates.
Building on the innovations of merging NLU and NLG, bringing proprietary data into the fold, using affective computing techniques and machine learning algorithms, conversational AI systems have already given companies a safe, scalable, and effective entry into the AI revolution.
So, are you not yet using AI calling automation?
Take a look at Pete & Gabi, before you get left behind.
