Meet Ender, the AI Quality Assurance Specialist who will join your QA team and help it shine!
.svg){kind=small}
Author: Marina Samo
Date: 06.09.2025
Generative AI and AI, in general, are the only terms that have created chaos in people’s minds. If you ask ten people about what AI is, its applications, and the use cases of Generative AI, you will get more than fifteen different answers.
Consider this: Conversational AI, Conversation AI, and Conversational Intelligence. Can you tell the difference? Check for yourself with OpenAI, and you will be surprised. Google these terms, and you’ll be even more surprised.
And this is just for starters.
Here is your main dish from the customer service and sales conversations menu: AI Assistants, Speech Analytics, Summarization AI, Virtual Agent, Agent Assist, Knowledge AI, Voice bot, AI copilot, Conversational AI, Conversation AI, Conversational Intelligence, AI Sales Coaching, Revenue Intelligence, and AI what_comes_to_your_mind. Oh, Lord!
But this is okay. It is a new and extremely fast-developing technology, and terminology struggles to keep up. The terms are not yet established, and new ones constantly emerge.
Since we work with speech analytics, also known as conversation analytics, and are moving into conversation intelligence, we have classified AI tools according to their application in this area.
We selected one main idea for classification: AI is supposed to automate many processes in our work and private lives. Ideally, for good.
The dictionary says that automation is any technology that reduces human labor, especially for predictable or routine tasks.
The axis of our classification will be automation.
Let’s divide all the AI technological advancements for conversations improvement and reduction of human labor into four main groups:
This is a direct replacement of a human by a machine. The conversation is between a human and a machine. Bots became well-known in the early 2020s and gained some negative notoriety. We all remember SIRI and the jokes that came after:
Because of people’s negative perception of this technology, developers no longer want to call them chatbots or voice bots and have started using a new term – Conversational Intelligence.
You must consider that the 2024 generation of bots is much smarter and more efficient than their predecessors.
Let’s have a look at some modern bots:
Handle inquiries, provide information, resolve common issues, and troubleshoot technical problems.
Engage in sales conversations, qualify leads, set up further interactions, and conduct telemarketing calls to promote products or services.
Schedule, confirm, or cancel appointments, and assist with placing, tracking, and managing orders.
Remind customers about pending payments, collect overdue payments, provide billing information, and handle payment-related queries.
Conduct phone surveys to collect customer feedback and remind customers about upcoming events, appointments, or deadlines.
This group includes AI Assistants, Agent Assist, Knowledge AI, and AI copilot. This technology provides a human with the superpower of AI. The conversation is between two humans, but one has the additional support of Generative AI.
Let’s take a look at just one small group of AI assistants, as there are currently a great number of them arising every month:
This AI technology participates in the conversation between two humans (agent and customer) on the agent’s side. It helps the agent become more expert by providing crucial and relevant information in seconds.
This group provides automation for the most time-consuming and boring part of any communication: after-call or chat data processing, form fillings, CRM enrichment, analysis, follow-up processes, etc.
In this category, Generative AI provides humans with its intelligence for wrap-ups and post-conversation analytics (or real-time analytics). AI handles everything that happens after the call, so humans don’t waste their time on this painstaking work. In this case, AI allows us to scale any monitoring or analytics to 100%, which was previously impossible with human labor.
Here are some possible variations:
Traditionally, the content of conversations between company representatives and clients was a black box. Manual analysis of conversations like calls, chats, and emails is very expensive, so companies made a 1-5% random selection of the conversations to monitor for quality control and training. That’s it.
But the main value is not in improving the quality of service.
For companies in which customer-facing teams generate a significant share of revenue or operations losses, analyzing the content of the conversations can dramatically influence revenue and margins.
According to Ender Turing’s Conversation Intelligence in Healthcare Use Case, up to 60% of hospitals’ and clinics’ revenue is acquired through call centers. However, 10% of the demand is lost there every month. Conversation Intelligence provides detailed answers to all possible ‘Why’ questions and helps regain lost revenues, improve conversions, and make data-driven decisions significantly faster.
What’s more important to say is that all this was impossible until the tech evolved. The manual analysis would cost 20-30 times more than the potential gain, while AI analytics makes it very manageable for companies.
Match the software or service to your current needs. Don’t look at the trendy name – check out the business value you can get by implementing an AI tool into your business processes. What problem will it solve for you?
In 2024, you can expect industry-specific solutions for your business that can bring value faster and require less effort from your team – for example, Ender Turing Generative AI and Conversation Intelligence for Healthcare.
When you meet a vendor using our described terms, ask for a detailed explanation. There is still the possibility of getting lost in the translation. What do they offer exactly?
Generative AI is a type of artificial intelligence technology that generates content such as text, images, audio, and synthetic data upon human request. Generative artificial intelligence can spawn valuable pieces of information including simple code or tables, based on the input it receives.
Essentially, it produces the specific content a human asks for, using progressive algorithms and AI models to fulfill various creative and informational needs.
The advantage is that Artificial Intelligence can perform multiple tasks much faster than human intelligence.
Generative AI is a form of artificial intelligence that relies on machine learning and deep learning algorithms to generate texts, videos, images, or programming logic for different types of AI applications.
Question for you, as the saying goes.
Since we just discovered that generative AI is a technology, comparing it with OpenAI would be incorrect.
OpenAI is the name of an American research organization dedicated to developments in artificial intelligence.
OpenAI conducts research in many spheres, including natural language processing, computer vision, robotics, and the development of AI models like GPT series.
OpenAI is the creator of the well-known chatGPT, which has become an indispensable aid to thousands daily.
The acronym GPT stands for Generative Pre-trained Transformer. Let’s see what does it mean.
If you ask chatGPT what it is, the answer will be the following:
ChatGPT is an artificial intelligence chatbot based on generative AI LLM, developed by OpenAI, based on the Generative Pre-trained Transformer architecture.
A Generative Pre-trained Transformer is a type of large neural language model pioneered by the OpenAI company that is trained on large text datasets to understand and generate content that is human-like.
Basically, ChatGPT can engage in a variety of conversational tasks, including answering questions, providing information, generating text based on human prompts, and assisting with tasks such as writing, counting, and coding.
And yes, ChatGPT is a generative AI model. However, nowadays, many generative AI models have appeared to become chatGPT competitors or successors.
No doubt, when I am writing this paragraph, the name of chatGPT developed by OpenAI rings out on every corner.
Concurrently, numerous other generative artificial intelligence companies are actively contributing by creating diverse generative AI applications.
Let’s dive into various kinds of generative AI tools that have captivated users’ attention. We can divide them into 3 groups:
Copy.ai stands out as a prominent contender in the area of text generation.
The competitive landscape of generative AI is populated by big players to challenge OpenAI’s dominance: Antropic, Deepmind, Mistral, Cohere, and Stability.
Generative AI works as a system that can independently create new content across various mediums, such as text, images, music, and videos. It can also work with complex data as an AI system.
While individuals use generative AI to create content or obtain valuable insights through conversations, artificial intelligence also has a significant potential for implementation in enterprise settings. Implementing generative AI in large corporations can streamline the handling of massive volumes of enterprise data and enhance the scalability of diverse business processes.
In the realm of the IT industry, generative AI systems find application in:
Moreover, the technology underpins the development of chatbots and virtual assistants, relying on Natural Language Processing to optimize their effectiveness.
Generative AI models are AI platforms that create different outputs based on massive training datasets, neural networks, deep learning architecture, and user prompts.
Depending on the type of generative AI model you’re working with, you can generate images, translate text into image outputs, synthesize speech and audio, create original video content, and generate synthetic data.
It can be said that generative AI models are the champions of AI that are underappreciated.
Let’s go through the main types of generative AI models:
A bright example of an AI model that fits into multiple categories is chatGPT because it is made as a transformer-based model, large language model, and multimodal model.
Other examples: Stable Diffusion, LaMDA, PaLM, AlphaCode, BLOOM, LLaMA.
First and foremost, conversation intelligence is a technology that leverages artificial intelligence to analyze vast data of speech or text from customer conversations (e.g., human feedback) to derive useful insights from them.
Many software products, AI systems, have already been created based on this technology to help analyze conversations with customers in various businesses.
The conversation data from these generative AI systems is distributed to other platforms, such as CRMs, advertising platforms, data analytics, and digital experience platforms, to take action on the data in real time.
The main goal is to help revenue teams improve customer experience and stimulate customer engagement in a product or service.
Let’s examine the difference between the term we learned from the previous paragraph (conversation intelligence) and conversational intelligence.
While conversation intelligence is a new technology based on AI power, conversational intelligence is something different.
For us as humans, our own conversational intelligence is the intelligence hardwired into every human being to enable us to navigate successfully with others.
However, the AI revolution did not bypass this term, and some technologies were created that are now called AI conversational intelligence.
AI conversational intelligence offers real-time voice or text assistance for people, creating chatbots and virtual assistants. Amazon’s Alexa is a prime example of conversational AI in action.
If you want to improve your conversational intelligence as a human, that’s the right choice to make, and it’s never a waste. There are lots of approaches to this, starting with dedicating your time to listening to various people more than talking and ending with special training programs in colleges.
In simple words, conversational AI represents different variations of real-time voice or text AI assistants for people, such as SIRI at Apple, Alexa at Amazon, and Kaia at Outreach.io.
Meanwhile, conversation intelligence analyzes various conversations, including calls, chats, web forms, and emails. It aims to discover insights and trends for improving future interactions.
Let’s look at some examples of conversation intelligence platforms:
Fathom is a free app that instantly records, transcribes, and summarizes your Zoom, Google Meet, or Microsoft Teams meetings so you can focus on the conversation instead of taking notes.
Gong is a tool designed to record and analyze sales calls, providing insights and summaries to help improve performance and customer interactions.
Chorus is a call and demo recording software that transcribes calls, tracks keywords in the conversations, and provides analytics.
Ender Turing analyses customer service and sales calls, chats, and emails. It collects data across the channels, performs automatic Quality Assurance, and provides deep analytics for sales teams, service teams, operations, and marketing.
There is a significant difference between these two terms.
Conversation intelligence software uses artificial intelligence and machine learning to capture unstructured data in voice and text-based interactions in human-to-human or machine-to-human conversations.
In seconds, the software matches the unstructured data with structured metadata about the interaction and combines it with sentiment and emotion analysis. This provides deep insights into the meaning of words and the drivers of behavior.
Revenue intelligence is the process of gathering, analyzing, and interpreting sales data to make informed business decisions. Dutzends of revenue intelligence platforms are on the market, and many are empowered with AI technology.
When applied to sales communication, conversation intelligence can become part of revenue intelligence. Conversation intelligence solutions could be one of the links in a complex chain of revenue intelligence or work alongside a revenue intelligence platform.
LLMs and generative AI models are related but distinct concepts within the field of artificial intelligence. Here’s a detailed comparison to clarify their differences and overlaps:
Key Differences
Overlap
In summary, while all LLMs can be viewed as generative AI models when used for text generation, not all generative AI models are LLMs, as they may focus on generating other types of media.
No, machine learning (ML) models are not an outdated technology. In fact, they are more relevant and widely used than ever. Here are several reasons why machine learning continues to be an essential and evolving field:
Continuous Evolution and Improvement
Using generative AI models can provide huge value to all kinds of businesses. Generative ai work delivers support in various business processes: customer service, sales, marketing, product development, automated machinery, autopilots, copilot. We can definitely say that the generative AI system is a part of our current and future.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas ac velit pellentesque, feugiat justo sed, aliquet felis.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas ac velit pellentesque, feugiat justo sed, aliquet felis.
