What is Generative AI?

Generative AI (GenAI) is a type of artificial intelligence that creates new content, including text, images, audio, code, data, and videos, from descriptions in natural language. Generative artificial intelligence learns from vast amounts of data and generates new outputs that closely resemble content created by humans.

Generative AI models operate by learning statistical relationships within datasets. They analyze billions of examples during training, capturing language structure, visual patterns, or sound features. Once trained, generative AI models use this knowledge to create new content by predicting what should come next in any given sequence.

How Does Generative AI Work?

Generative AI models work by imitating a human brain's learning and decision-making capabilities. To achieve this, generative models rely on several key AI technologies that enable them to learn from training data, create content, and refine it.

Let’s break down how generative AI models work:

1. Learning from data

Before generative AI can create anything, it must learn from vast amounts of data. This process is similar to how humans learn: by reading, observing, and recognizing patterns. Artificial intelligence uses the following key technologies:

• Machine Learning Model (MLM) helps AI detect patterns and relationships within data.
• Neural Networks process information, identifying deeper connections in the data.
• Deep Learning uses neural networks to understand complex patterns in vast amounts of unstructured data, such as text, images, and videos.
• Natural Language Processing (NLP) enables AI to break down text, understand the meaning, and recognize intent.

2. Generating content

When users provide a prompt – a specific instruction of what they want AI to do – AI analyzes it, predicts the most likely and coherent response, and creates the output. Depending on the content type, it can use different technologies:

• LLMs (Large Language Models) predict the most relevant words and phrases in the text to form complete sentences and paragraphs, allowing AI to write reports, essays, or answer questions.
• Transformers power LLMs such as GPT-4 and Google Gemini to analyze words in relation to each other and generate text.
• Diffusion Models, such as DALL·E 3 or Stable Diffusion, make random changes to the initial data sample to create a new output. They are designed to generate images, videos, audio, and 3D models.

• Generative Adversarial Networks (GANs) work in tandem: one generates fake data samples, while the other tries to distinguish it from real data, providing feedback to improve quality. This back-and-forth process continues until the second neural network is no longer able to differentiate between fake and real data. GANs are used to create realistic visuals.

   

3. Refining content

Content created by generative AI models is not always perfect, so it needs refinement to improve accuracy and relevance. This involves techniques that help enhance the quality of AI-generated content:

• Fine-Tuning involves adapting pre-trained generative models for specific tasks or use cases by training them on a smaller dataset, for example, legal documents.
• Retrieval Augmented Generation (RAG) allows AI models to use relevant external sources outside of training data to generate more factual and up-to-date content.
• Reinforcement Learning from Human Feedback (RLHF) enables generative AI to learn from human feedback. As humans review and correct generative AI's outputs, the system adapts, improving its performance over time.

What Can Be Created with Generative AI?

Generative AI models can create various outputs, such as text, images, videos, code, sounds, synthetic data, and even artwork.

Text

Generative AI tools, such as ChatGPT (generative pre-trained transformer) and Gemini, can create human-like text based on natural language prompts. These tools can generate various types of text, including articles, documents, social media posts, website copy, reports, papers, and emails in multiple languages.

GenAI can even imitate creative writing skills to write poems, song lyrics, books, etc. Users can choose the tone, style, voice, etc. to write an article in Stephen King’s style, or a poem imitating Edgar Allan Poe’s storytelling.

Generative AI models can also automate repetitive writing tasks that don’t require creativity, such as generating meeting notes, writing follow-up emails, and summarizing reports and documents.

The text created by generative AI is unique, contextually relevant, and coherent, however, it might need some editing to adjust tone, structure, length, and word choice.

Images

Generative AI systems can create artistic and realistic images from scratch by analyzing descriptions provided by users. Tools like Midjourney and Dall-E can generate photorealistic images, digital paintings, illustrations, etc., offering multiple variations of the same image. They can also be useful in business to create brand logos, promotional brochures, visuals for ads, and social media posts.

Generative AI can also enhance and modify existing images, allowing users to change backgrounds, add or remove objects, improve image quality, and transform photos into artistic styles like paintings or sketches.

Videos

Generative AI can create short clips and animations from text descriptions or existing footage. AI-powered tools can generate realistic or stylized video content, automate edits, and enhance visual effects.

These tools are widely used in marketing, content creation, and filmmaking to produce engaging promotional videos, social media clips, and presentations. Generative AI can also edit and refine existing videos, enabling users to enhance resolution, apply color correction, generate missing frames, and add special effects.

Audio & music

Generative AI can produce realistic speech, background music, and sound effects based on text prompts or input audio. Tools like ElevenLabs convert text into natural-sounding speech (Text-to-Speech technology) to generate lifelike voices for audiobooks, virtual assistants, and customer service bots, while AI-powered music models create jingles, and ambient sounds.

Generative AI can also modify existing audio, allowing users to clone voices and translate speech into different languages. These capabilities are widely used in podcasting, video production, game development, and marketing.

Synthetic data

Generative AI is not only used for text, images, and audio, it can also create synthetic data, which mimics real-world data. This is particularly valuable in heavily regulated industries with strict privacy laws, like finance and healthcare.

GenAI models can create realistic, anonymized datasets to test applications, conduct simulations, make predictions, and generate insights without exposing sensitive customer data. For example, in the banking industry, synthetic data can be used to train fraud detection models, test AI-powered credit scoring and loan approval tools, simulate market risk scenarios, etc.

Code

Generative AI models can support IT specialists in writing, debugging, and optimizing code, making software development faster and more efficient. Tools like GitHub Copilot help developers by suggesting code snippets, automating repetitive tasks, and finding errors.

In Creatio, non-technical users can build new applications from scratch, thanks to generative capabilities of Creatio AI. By providing a description in natural language of what the goal of the application should be, users can quickly develop new business apps, even if they don't have coding experience.

Art

This is one of the most controversial uses of generative AI technology. Some people would argue that AI is not capable of creating art as it lacks real creativity and it’s only mimicking human’s work. Nonetheless, GenAI models can create paintings, generate music, and write plays. Whether it’s real art or not, it’s up for discussion.

Generative AI Use Cases for Businesses

In this section, we’re going to describe some of the generative AI use cases for business, including industries such as banking and healthcare, and business departments like sales, customer service, and marketing. We’re also going to explore how GenAI technology can support software engineering and R&D.

Banking

Generative AI for banking institutions can help increase safety, improve customer experience, and reduce costs:

• Credit scoring and loan approvals - generative AI can create accurate credit scoring models, assess credit risk, and help finance professionals make better loan decisions.
• Fraud detection - generative AI tracks transactions and flags anomalies to detect money laundering and fraud.
• Personalized financial advice - AI-powered financial coach offers personalized financial advice, helping customers manage their budgets, investments, and savings.
• Synthetic credit data generation - GenAI produces synthetic credit data with the same properties as original data to help banks simulate customer behaviors, test credit risk models, etc.
• Regulatory compliance and reporting - generative AI automates the preparation of anti-money laundering reports, audits, and compliance documents, ensuring they are compliant with industry regulations.

Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 to $340 billion.

Healthcare

Healthcare institutions can use generative AI tools to enhance diagnostics, improve patient care, boost medical research, and develop new drugs and treatments:

• Updating medical documentation - with generative AI tools taking care of creating and updating medical documentation, healthcare professionals can focus on providing patient care.
• Diagnostics - GenAI tools can create diagnostic tests to detect various conditions and identify early signs of diseases from medical images much sooner than healthcare professionals.
• Personalized healthcare advice - generative AI technology can quickly create healthcare advice, tailored to individual patients, based on patient data, such as medical history and lifestyle.
• Synthetic patient data generation- GenAI models can create synthetic patient data for medical training, clinical trials, and research purposes.

Marketing

Marketing professionals can use generative AI to create personalized marketing materials, enhance marketing automation, and test different ad visuals and copy at scale.

• AI-powered marketing automation - generative AI automates repetitive marketing tasks, personalizes marketing outreach at scale, and optimizes marketing campaigns in real-time.
• Personalized content creation - GenAI tools can help marketers prepare engaging marketing materials, such as newsletters, ads, social media posts, articles, and marketing visuals, tailored to different channels, platforms, customer segments, and individual customer preferences.
• A/B testing - marketers can use GenAI tools to quickly create multiple variations of the same content to test which option works the best with specific audience segments and channels.
• Product recommendations - generative AI tools can analyze customer data, such as demographic information, past interactions, historical sales, website behavior, etc., to provide personalized product and service recommendations.

We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. - Mckinsey

Sales

Sales professionals can use generative AI for sales to personalize outreach communication at scale, generate tailored offers, and obtain tailored suggestions for upselling and cross-selling:

• Personalized outreach - generative AI sales tools automate nearly the whole outreach process, from initial scouting for high-value prospects to gathering information about them and preparing tailored messages.
• Cross-selling and upselling recommendations - sales agents can use GenAI to analyze customer data and generate personalized upselling and cross-selling recommendations that will help them increase the deal value.
• Sales content generation - generative AI tools can help sales agents generate various types of sales content, including personalized sales offers, sales scripts, playbooks, presentations, etc.
• Meeting preparation - sales professionals can use GenAI to prepare for a sales meeting by summarizing key prospect data and obtaining recommendations on the best approach to win a deal.

Customer service

Businesses can use generative AI for customer service to provide timely communication in multiple languages, automate case routing, and improve the case resolution times and accuracy:

• Chatbots - chatbots powered by AI's natural language processing capabilities can answer customer questions, providing timely and accurate customer support 24/7. They can also escalate more complicated cases that require human touch to customer service agents.
• Case routing - GenAI tools can analyze the customer case and automatically route it to the best fitting agent based on their qualifications, experience, locations, etc., which supports timely case resolution.
• Case support agent - generative AI can serve as a support agent, providing case summaries and next-best-step recommendations to successfully resolve customer issues.
• Knowledge management - customer support agents can use GenAI to analyze frequently asked questions and prepare FAQ pages, guidebooks, and other materials that help customers find answers to their questions.

According to McKinsey, implementing generative AI in customer service functions can potentially increase productivity at a value between 30 to 45% of current function costs.

Software engineering

Generative AI is transforming software development by augmenting coding, finding and fixing bugs, and helping engineers build better applications:

• Code generation - generative AI tools can generate initial code drafts, suggest entire functions, and complete code snippets, which helps developers write code faster.
• Automated bug detection and fixing - GenAI can analyze code to detect bugs and suggest fixes.
• Legacy code modernization - generative AI assists in translating old code into modern programming languages, helping companies update legacy systems efficiently.

Research and development (R&D)

Generative AI supports research and product development across various industries, by enabling generative design, improving existing designs, and accelerating knowledge discovery:

• Generative design - generative AI can create candidate molecules and design new protein sequences to accelerate the process of developing new drugs and materials. For example, a 2024 Nobel Prize in Chemistry was awarded for using AI to generate new protein structures used in drugs and vaccines.
• Enhanced design - GenAI can improve existing designs by selecting better materials and suggesting improvements to enhance durability and reduce material waste.
• Literature review and knowledge discovery - generative AI can analyze and summarize vast amounts of research papers, patents, and scientific literature, helping researchers stay updated on the latest discoveries.

The use of generative AI technology in R&D could deliver productivity with a value between 10 to 15% of R&D costs.

Benefits of Generative AI

According to McKisney, the total economic benefits of GenAI amount to between $6.1 trillion and $7.9 trillion annually, as companies across industries leverage AI to boost productivity, reduce costs, and enhance innovation.

Here’s how businesses can benefit from implementing this powerful technology:

Efficient and effective content creation

Businesses can use AI-powered tools to produce marketing copy, sales offers, blog articles, ad visuals, and social media content in a fraction of time. These tools can be used to generate first drafts, which can be further improved by humans, significantly reducing the time and effort required to create new content.

Additionally, GenAI improves consistency, enabling companies to maintain uniform brand voice and style across different pieces of content and multiple platforms.

Personalization at scale

Generative AI enables businesses to deliver highly personalized experiences to customers at an unprecedented scale. By analyzing customer data, preferences, and behaviors, AI can generate tailored marketing content, product recommendations, sales offers, and communication that resonate with individual users.

GenAI technology helps companies enhance personalization across multiple channels, from emails and chatbots to websites and ads, ensuring that every interaction feels relevant and engaging. Unlike traditional methods, which require extensive manual effort, generative AI can adapt content in real-time for millions of customers, improving engagement, conversion rates, and customer satisfaction.

Increased productivity

Generative AI has the potential to augment employees' capabilities by automating routine tasks that consume 60-70% of employees’ time today. From low-complexity activities such as data entry to highly complex ones like making strategic decisions, generative AI systems can revolutionize human productivity.

According to McKinsey, GenAI’s impact on productivity can add to the global economy between $2.6 trillion to $4.4 trillion annually.

In industries like finance, healthcare, software engineering, and R&D, generative AI streamlines operations by automating data analysis, document generation, coding, synthetic data creation, and report summarization.

Reduced costs and increased revenue

Generative AI helps businesses cut costs by automating tasks that traditionally take much time and effort. By streamlining content creation, customer support, data analysis, and software development, companies can reduce the costs of labor while improving productivity. AI automation also minimizes errors, lowering expenses related to corrections and rework.

At the same time, generative AI contributes to increased revenue by enhancing customer satisfaction, personalizing marketing efforts, and improving sales outreach. Businesses can leverage AI-generated insights to make data-driven decisions regarding their operations, strategies, and resource allocation.

Accelerated time to market

Generative artificial intelligence helps businesses release products and content faster by automating research, design, and production. For example, the development of new drugs traditionally takes 10 to 15 years. With generative AI models, pharmaceutical companies can fast-track key stages, such as identifying the most effective molecule for a new drug, reducing what typically takes several months to just a few weeks.

Enhanced creativity and innovation

Generative AI supports creative processes by helping brainstorm new ideas, design multiple variations, and experiment with new styles and formats. GenAI-powered tools can also help fight creative blocks by delivering first drafts of content. Whether it's creating unique artwork, composing music, writing scripts for advertisement, or designing new products, generative AI expands creative possibilities while enhancing human creativity rather than replacing it.

Challenges and Risks of Generative AI

While generative AI offers numerous benefits, it also comes with challenges and risks that users and businesses must address. These include concerns related to accuracy, bias, security, and lack of quality training data.

Here are some of the key challenges associated with generative AI:

1. Inaccuracy and hallucinations - GenAI models can generate false or misleading information that seems plausible but is incorrect. This happens because the model doesn’t have access to the desired data and tries to fill out the blank. This can be reduced with guardrails that restrict generative AI tools to only use relevant data sources. For example, LLM Notebook by Google uses only sources provided by the user.
2. Bias and ethical concerns - AI models learn from existing data, which may contain biases, leading to unfair or discriminatory outputs. To ensure fairness and inclusivity, GenAI models should be trained on diverse data and monitored by humans.
3. Security and data privacy risks - AI-generated content can be used for fraud, misinformation, and deepfakes, raising cybersecurity concerns. Additionally, users and businesses need to be careful not to share their private or customer-sensitive data with publicly available models that could use them to train and share with other users.
4. Regulatory and legal issues - generative AI may use publicly available data protected by intellectual property rights and copyright laws to provide input. That’s why it’s crucial to provide models with clear guidelines and always check the output they produce.
5. Lack of quality data - generative AI models depend on high-quality data to train and learn to produce accurate results. Many businesses have poor or incomplete data, which reduces the effectiveness of the models. Before implementing generative AI tools, organizations should first make sure their training data is clean, complete, and free of duplications.
6. Lack of explainability and metrics - many generative AI models are often viewed as "black boxes" because they don’t provide clear explanations on how they make decisions. This lack of explainability can pose challenges in gaining stakeholder trust and ensuring accountability.
7. Lack of skills - generative AI is a relatively new technology, only recently available to the general public. Many people don't have the skills required to effectively work with GenAI tools and might be reluctant to learn them.

Short History of Generative AI

Generative AI has evolved significantly over the years, driven by advancements in computing and algorithms. Here’s a brief timeline of its development:

Early foundations (1950s)

Generative AI’s roots lie in the early AI research of the 1950s, with Alan Turing proposing the Turing Test, which tested a machine's ability to exhibit intelligent behaviour similar to that of a human.

As we know, generative AI models rely on machine learning technology and neural networks. The first machine learning algorithm was introduced in 1952, by Arthur Samuel, for playing checkers, and a prototype of a neural network was developed by Frank Rosenblatt in 1957.

Machine learning era (1990s - 2000s)

In the 1990s, machine learning took center stage, moving away from rule-based models to algorithms that learned from data. At that time, the gaming industry played a crucial role in advancing generative AI. The development of 3D graphics cards, designed to enhance motion graphics in video games, laid the foundation for graphics processing units (GPUs) - now essential for training and running generative AI models.

Hidden Markov Models and Gaussian Mixture Models were used for generative tasks like speech recognition and image generation. In 20024, Google introduced the first autocomplete feature, which generated potential words and phrases as users entered their search terms, based on the Markov model.

Deep learning breakthrough (2010s)

The 2010s saw the rise of deep learning supported by neural networks, which enabled significant improvements in image and language processing. In 2011, Apple introduced the first virtual assistant Siri, which was capable of recognizing speech and answering users' questions.

This era laid the foundation for more advanced generative models.

Generative models (2014 - 2017)

In 2014, Generative Adversarial Networks (GANs) were introduced, marking a breakthrough in generating realistic data.

In 2017, Google published a paper explaining the principle of transformer models, which enable the development of advanced generative AI tools.

Transformers and Large Language Models (2019-2020)

The introduction of Transformer models revolutionized text generation. Models like GPT-2 and GPT-3 showed unprecedented capabilities in generating human-like text. This led to the rise of AI tools for writing and coding.

Generative AI today

In 2022, OpenAI introduced ChatGPT, a generative AI combined with large language models, capable of generating complex, coherent and contextual sentences and long-form content in response to user prompts. This was the first AI-powered tool available to the general public. Generative AI now creates text, images, audio, video, and more.

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Creatio is an AI-native CRM and workflow automation platform that seamlessly integrates generative AI to help businesses automate workflows, generate content, analyze data, make data-driven decisions, and personalize customer interactions with ease. Creatio AI is an integral part of the platform and not a separate add-on, which means it can seamlessly access every object and workflow within the system.

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Creatio.ai empowers sales teams to work more efficiently and strategically by leveraging AI skills to automatically generate tailored outreach messages to engage high-value prospects, obtain cross and upselling recommendations to increase deal value, generate meeting summaries, and create opportunity summaries and sales analytics reports.

Creatio.ai redefines how marketers design and execute campaigns by integrating generative AI to brainstorm new campaign ideas, generate content tailored to specific customer segments, write newsletters and emails with product recommendations, and generate performance reports with insights on how to improve the ROI of marketing campaigns.

Customer service teams can use Creatio.ai to enhance their ability to deliver exceptional service with intelligent assistance for faster and more accurate case resolution. Creatio.ai can summarize cases and recommend the best way to resolve a specific issue, write drafts of customer communication to speed up the response time, analyze customer sentiment to find common problems, and update the knowledge base.

Additionally, thanks to the no-code approach paired with generative AI technology, Creatio enables users to design and deploy AI-driven solutions tailored to their unique business needs.

With its AI-native approach, Creatio empowers organizations to benefit from powerful generative AI capabilities across all functions and workflows, enhancing operational efficiency and maximizing AI adoption.