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June 9, 2025

What is Generative AI? And what is AI in general?

Chief Executive Officer

Marina Samo

What is Generative AI? And what is AI in general?

Author: Marina Samo

Date: 06.09.2025

Generative AI tools

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.

Divide and conquer! For Generative AI and AI in the area of conversations.

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:

Group 1: Human-Machine Communication instead of Human-Human Communication

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:

Rule-based bots issues

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:

Customer Service and Support Bots:

Handle inquiries, provide information, resolve common issues, and troubleshoot technical problems.

Sales and Marketing Bots

Engage in sales conversations, qualify leads, set up further interactions, and conduct telemarketing calls to promote products or services.

Appointment and Order Management Bots

Schedule, confirm, or cancel appointments, and assist with placing, tracking, and managing orders.

Payment and Collection Bots

Remind customers about pending payments, collect overdue payments, provide billing information, and handle payment-related queries.

Survey and Reminder Bots

Conduct phone surveys to collect customer feedback and remind customers about upcoming events, appointments, or deadlines.

Group 2: Empowering Humans with AI Assistance in Human-Human Communication

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:

Agent Assist Tools

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.

  • Call Center Agent Assist
    • Real-Time Support: Provide live assistance to agents during calls, offering information and suggestions.
    • Sentiment Analysis: Analyze customer sentiment to help agents adjust their responses.
  • Sales Agent Assist
    • Customer Insights: Provide detailed insights and customer background to aid in sales pitches.
    • Sales Strategies: Offer real-time suggestions and follow-up reminders to sales representatives.
Generative AI use cases

Group 3: Post-Conversation Automation for both Human-Machine or Human-Human Communication

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:

  • Conversation Summarization
  • Conversation-Triggered RPA
  • Automated CRM Fill-Ins
  • Automated Quality Assurance for Customer-Facing Teams in Customer Service or Sales

Group 4: Conversation Analytics for both Human-Machine or Human-Human Communication

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.

Generative AI questions and answers

Conclusion

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?

  • Human-Machine Communication
  • AI-Assisted Human-to-Human Communication
  • AI Intelligence for In-Conversation or Post-Conversation Automation
  • Conversation Analytics for Calls, Chats, or Emails

Additional Material to Read

The Eight Paradigms of AI Application Startups by Angular Ventures

AI solutions for call center

What is a generative AI?

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.

What is the difference between OpenAI and generative AI?

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.

Is ChatGPT generative AI?

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.

What is the most famous generative AI?

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:

Image Generation and Editing:

  • DALL-E 2 (OpenAI)
  • Shutterstock’s AI Image Generator
  • StyleGan
  • NightCafe

Video Generation and Editing:

  • Synthesia
  • 14Descript

Text Generation:

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.

What is generative AI in simple terms?

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:

  • Content generation
  • Image creation and editing
  • Music creation
  • 3D modeling
  • Video creation and editing
  • Game development
  • Code generation
  • Art creation
  • Voice generation

Moreover, the technology underpins the development of chatbots and virtual assistants, relying on Natural Language Processing to optimize their effectiveness.

What is an example of a generative AI model?

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:

  • Generative adversarial networks (GANs)
  • Transformer-based models
  • Diffusion models
  • Variational autoencoders (VAEs)
  • Unimodal models
  • Multimodal models
  • LLMs
  • Neural radiance fields (NeRFs)

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.

What is conversation intelligence?

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.

How can I improve my conversational intelligence?

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.

What is the difference between conversational AI and conversation intelligence?

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.

What is the difference between conversation intelligence and revenue intelligence?

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.

Large Language Models available on the market

  • GPT-4 – Developed by OpenAI, GPT-4 is one of the most advanced models available, known for its ability to process and generate both text and images. It’s widely used in applications like customer service, content creation, and coding.
  • Claude 3 – Created by Anthropic, the Claude 3 family includes models like Haiku, Sonnet, and Opus, designed for enterprise use with features such as multilingual capabilities and enhanced performance.
  • LLaMA 3 – Meta’s latest iteration in their generative ai LLaMA series, available in 8 billion, 70 billion, and a forthcoming 400 billion parameter versions. It’s open-source and used extensively for various AI applications.
  • PaLM 2 – Google’s generative ai model, known for its strong performance in commonsense reasoning, formal logic, and multilingual understanding. It comes in different sizes, with the largest model having 540 billion parameters.
  • Gemini 1.5 – From Google DeepMind, generative ai Gemini 1.5 offers advanced capabilities with a massive context window and multimodal support, handling text, images, and audio data natively​​.
  • Falcon 180B – Developed by the Technology Innovation Institute (TII), Falcon 180B is a powerful generative ai model with 180 billion parameters, excelling in reasoning and coding tasks.
  • Stable LM 2 – Stability AI’s model available in versions with 1.6 billion and 12 billion parameters, known for its efficient performance and smaller size compared to other LLMs.
  • Mistral 7B – A model from Mistral generative AI, featuring 7.3 billion parameters and optimized for faster inference and longer sequences, available under an open-source license.
  • Cohere Command – Cohere’s generative ai models focus on instruction-based tasks and retrieval-augmented generation, providing robust performance for enterprise applications.
  • Orca – A smaller generative ai model developed by Microsoft, leveraging progressive learning to build upon GPT-4’s capabilities, suitable for reasoning and complex text generation tasks.

Generative ai models or just generative models

  • DALL.E 3 – Developed by OpenAI, DALL.E 3 excels in generating coherent and detailed images from textual descriptions, offering significant improvements in visual fidelity and concept understanding.
  • Stable Diffusion XL (SDXL) Base 1.0 – From Stability AI, this model is known for producing high-quality, diverse images, suitable for applications in media, advertising, and personal projects.
  • Gen2 – Genetative ai created by RunwayML, Gen2 is a versatile text-to-video generation tool that allows for extensive customization, making it ideal for producing ads, demos, and educational videos.
  • Veo – Google’s latest generative ai video model, capable of producing high-quality 1080p videos in various cinematic styles. Veo offers advanced creative control for filmmakers and content creators.
  • Imagen 3 – Another model from Google, Imagen 3 is their highest quality text-to-image generative ai model, generating photorealistic images with high detail and fewer visual artifacts compared to previous models.
  • PanGu-Coder2 – Developed by Guizhou Hongbo Communication Technology Co., Ltd., this generative ai model excels in code generation and debugging across multiple programming languages, enhancing coding productivity.
  • Deepseek Coder – Generative ai from Deepseek AI Technologies, this model specializes in generating and optimizing code, with a focus on performance and readability.
  • Code Llama – Meta’s generative ai model for coding tasks, based on the Llama 2 model, supports code generation, debugging, and code completion in various programming languages.
  • StarCoder – HuggingFace’s generative ai model designed to assist software developers by generating efficient and clean code. It supports various coding tasks and programming languages.

What is the difference between Large language models and generative ai models?

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:

Large Language Models (LLMs)

  • Definition: LLMs are a type of generative AI model specifically designed to understand, generate, and manipulate human language. They are trained on vast amounts of text data to predict and generate coherent and contextually appropriate text.
  • Examples: GPT-4, GPT-3.5, BERT, T5, LLaMA.
  • Primary Function: The primary function of LLMs as a generative ai is to handle natural language processing tasks such as text generation, translation, summarization, sentiment analysis, and more.
  • Capabilities: LLMs as an example of generative ai excel at generating human-like text, answering questions, writing essays, and even coding based on natural language prompts. They are not limited to text generation but are also adept at tasks requiring understanding and processing text.
  • Training Data: They are trained on a wide variety of text data sourced from books, articles, websites, and other text-rich media.
  • Applications: Chatbots, virtual assistants, content creation tools, language translation services, and automated text analysis tools.

Generative AI Models

  • Definition: Generative AI models encompass a broader category of AI systems designed to create new content across various modalities, including text, images, audio, and video. They generate new data samples based on the patterns learned from the training data.
  • Examples: DALL.E and Stable Diffusion – image generation, Gen2 (video generation), Jukedeck (music generation), and Deepfake models (video and audio synthesis).
  • Primary Function: The primary function of generative AI models is to create new, original content. This can include generating realistic images from text descriptions, composing music, creating videos from textual inputs, and synthesizing speech.
  • Capabilities: These models are capable of generating high-quality images, videos, music, and other forms of media. They are often multimodal, meaning they can process and generate multiple types of data.
  • Training Data: They are trained on datasets specific to their output type. For instance, image generation models are trained on large datasets of images, while music generation models are trained on extensive collections of music.
  • Applications: Digital art creation, video content production, music composition, virtual environment creation for games and simulations, and realistic voice synthesis for virtual assistants.

Key Differences

  • Scope: LLMs are a subset of generative AI models focused specifically on natural language. Generative AI models cover a wider range of modalities, including text, images, audio, and video.
  • Output Type: LLMs primarily generate text, whereas generative AI models can produce a variety of outputs, including images (e.g., DALL.E), music (e.g., Jukedeck), and videos (e.g., Gen2).
  • Use Cases: LLMs are often used for text-based applications such as chatbots and text analysis. Generative AI models are used in more diverse applications, including art, entertainment, and media production.

Overlap

  • Some LLMs can be considered generative AI models when their primary function involves generating text, a form of content creation. For instance, GPT-4 can be used to create articles, stories, and other textual content, placing it within the realm of generative AI.

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.

Are machine learning models an outdated technology?

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

  • Advanced Algorithms: Machine learning algorithms are continually being improved and refined. Innovations such as transformer models (e.g., BERT, GPT-4) and reinforcement learning techniques (e.g., AlphaGo) demonstrate the ongoing advancements in the field.
  • Hybrid Approaches: New approaches that combine machine learning with other technologies, such as quantum computing and neuromorphic computing, are emerging, offering enhanced capabilities and efficiency.

2. Widespread Applications of

  • Industry Adoption: Machine learning is widely used across various industries, including healthcare, finance, automotive, retail, and entertainment. Applications range from predictive analytics, fraud detection, and recommendation systems to autonomous vehicles and personalized medicine.
  • Emerging Technologies: Machine learning is a cornerstone of emerging technologies such as the Internet of Things (IoT), edge computing, and smart cities. These technologies rely on ML models for data analysis, decision-making, and automation.

3. Integration with Generative AI and Large Language Models

  • Generative AI: Machine learning models are integral to generative AI systems, which create new content such as text, images, and videos. Models like GPT-4, DALL.E, and Stable Diffusion are built on advanced ML techniques.
  • LLMs: Large language models (LLMs) such as GPT-3, BERT, and T5, which are used for natural language processing tasks, are based on sophisticated machine learning architectures. These models have transformed how we interact with generative ai technology and process information.

4. Scalability and Efficiency

  • Scalability: Modern ML models are highly scalable and can handle vast amounts of data, making them suitable for large-scale applications. Cloud-based ML platforms like AWS SageMaker, Google AI Platform, and Azure ML allow businesses to deploy and scale ML models efficiently.
  • Efficiency: Advances in hardware (e.g., GPUs, TPUs) and software optimizations have significantly improved the efficiency of ML model training and inference, making it feasible to deploy complex models in real-time generative ai applications.

5. Research and Development

  • Ongoing Research: Significant research efforts continue to focus on improving machine learning algorithms, addressing challenges such as interpretability, robustness, and ethical considerations. Conferences like NeurIPS, ICML, and CVPR showcase the latest research and breakthroughs in the field of generative AI.
  • Educational Growth: The field of machine learning is growing academically, with more courses, degrees, and certifications available than ever before. This educational growth ensures a steady influx of skilled professionals who can contribute to the advancement of ML technologies.

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.

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