What is Generative AI and Why Does It Matter for Businesses?

What is generative AI, how does it work, and how will it impact businesses worldwide?

Generative AI is clearly the “hot topic” of the decade. Although AI has gone through a number of hype cycles over the years, Gen AI, and tools like ChatGPT marked a serious turning point in the rise of AI for business innovation. Unlike old-fashioned AI models generative AI tools seem to almost match human beings in terms of creativity and performance.

Plus, as the models powering generative AI grow more advanced, the opportunities offered by these technologies are evolving. Today, generative AI solutions are paving the way to cutting-edge autonomous agents, multimodal content generation tools, and so much more.

So, why does generative AI matter to virtually every business today? Why has it driven the adoption of almost double the number of AI tools among enterprises (according to McKinsey)? And how exactly will it shape our future? Here’s everything you need to know.

What is Generative AI? The Basics

Generative AI is a type of artificial intelligence capable of “generating” new content: such as text, images, videos, and even code. Gen AI tools are powered by advanced machine learning algorithms (like neural networks), large language models, and other advanced AI technology.

These tools can learn the patterns and structure of huge volumes of input training data, and generate new data with similar characteristics.

The rise of ChatGPT, one of the most popular and accessible generative AI chatbots, sparked a growing interest in generative AI technology. In fact, many analysts started predicting that generative AI could become a multi-trillion-dollar market based on ChatGPT’s success.

But generative AI isn’t completely new. Generative models have been used throughout the world for years for things like statistical analysis. However, the rise of deep learning, and neural networks made it possible to extend the capabilities of Gen AI tools.

Now, we have generative AI models that can analyze images with computer vision, like Dall-E, tools that can respond to speech and audio, with natural language processing, and Gen AI coding apps. Companies like Microsoft, Salesforce, and AWS are even using generative AI and automation tools to power autonomous agents.

The History and Evolution of Generative AI

Perhaps the first class of AI models to spark the generative AI revolution were variational autoencoders (VAEs). Introduced in 2013, these were the first deep-learning models widely used for creating images and speech. These tools work by encoding unlabeled data into a compressed version, then “decoding” the data back into its original format.

VAEs opened the door to AI systems that could generate novel data and content. Later, in 2014, researchers discovered the next stage in machine learning models: Generative Adversarial Networks (GANs). These are still used in generative AI landscape today, to create realistic images, and augment data.

As GANs evolved, so too did “transformer models”, following the release of Google Brain’s publication: “Attention is All You Need”. Transformers drew attention to how companies could combine the encoder-decoder architecture of VAEs, with a text processing mechanism (self-attention), to alter how language models were trained.

This led to the development of the large language models and foundational models that form a crucial part of how generative AI works today. With transformers, an encoder converts raw text into embeddings (representations), and the decoder takes those embeddings, alongside previous model outputs to successfully predict the next word in a sentence. Over time, the encoder learns how sentences and words relate to each other, without human labeling.

Several innovations contributed to this evolution. Transformers allowed users to process sentences all at once, rather than one word at a time. This differentiated transformers from recurrent neural networks, and other techniques like long short-term memory.

Transformers also learned the relationships between words, and eliminated the need for teams to define their tasks upfront, making it easier to pre-train models on vast amounts of raw text – leading to the development of large language models.

What is Generative AI? The Key Technologies

To better answer the question “what is generative AI”, and understand how Gen AI technologies work, we need to take a closer look at the core components of generative AI – specifically, large language models, foundational models, and neural networks.

Large Language Models: Redefining Language

Large language models (LLMs) are the linguistic engines of generative AI. They are trained on vast amounts of text data—books, articles, websites, and more—using billions, sometimes trillions, of parameters. Parameters are basically the guardrails that the model adjusts during training to improve its predictions and outputs.

By analyzing text and data at scale, LLMs become adept at predicting the next word in a sentence, solving complex queries, and generating coherent narratives.

Examples of cutting-edge LLMs include: OpenAI’s GPT-4o, Gemini from Google, and Meta’s Llama models. How generative AI works in this context involves a technique called transformers, which use a self-attention mechanism. This allows the model to weigh the importance of different words in a sequence, capturing contextual nuances. Transformers typically form into 3 categories:

• Encoder-only models: Encoder models, such as customer-service chatbots like Watson Assistant, are used for non-generative tasks, like understanding customer feedback, or pulling data from large documents.
• Decoder-only models: Decoder models, like the GPT models, are trained to predict what should come next in a sequence or sentence, without an encoded representation. For instance, ChatGPT doesn’t need to know exactly what you will say to respond to your question.
• Encoder-decoder models: Encoder-Decoder models leverage both encoder and decoder functionality. They can accomplish many of the tasks of decoder-only models, and are usually faster and cheaper to create, because they’re more compact in size.

Foundation Models: Enabling Versatility

Foundation models are the adaptable core of generative AI. Think of them as general-purpose platforms designed to be fine-tuned for specific applications. Unlike traditional AI systems that are built for single-use tasks, foundation models allow generative AI technology for enterprises to adapt to a range of different use cases. Examples of foundation models include:

• Variational Autoencoders: Variational Autoencoders were some of the earliest models introduced into the generative AI world. These VAEs learn from a compact collection of data known as “latent space”. VAEs use their two neural networks (the encoder and decoder), to process data, and excel at image reconstruction and anomaly detection.
• Transformer-Based Models: These are the backbone of most modern LLMs. They build on the capabilities of traditional VAEs, with their encoder and decoder networks. These tools add more layers to the encoder network, and use a self-attention mechanism to weigh the value of each part of a sequence during processing, making them ideal for text-based tasks like translation.
• Diffusion models: Known for their ability to create realistic images, diffusion models create new content or data by making controlled changes to a data sample. Basically, they add and remove “noise” from content, to create something new. A good example is the DALL-E 3 model, which uses diffusion to create novel images.

Neural Networks: The Brain Behind Generative AI

Neural networks are the computational framework that enables generative AI to learn, adapt, and create. Modeled after the human brain, these systems consist of interconnected nodes (neurons) that process data in layers. Their ability to identify patterns, learn from vast datasets, and generate new content is at the heart of how generative AI works.

There are various types of neural networks, suited to different tasks, such as:

• Convolutional Neural Networks (CNNs): These specialize in image and video processing. CNNs are widely used in generative AI for creating photorealistic images or analyzing medical images for diagnosis.
• Recurrent Neural Networks (RNNs): RNNs are designed for sequential data, like time series or text input. While they’ve been largely surpassed by transformers in NLP tasks, RNNs remain valuable for certain niche applications.
• Transformer Networks: These are the foundation of modern LLMs, excelling at understanding sequential and contextual data. They introduced the attention mechanism, enabling models to focus on the most relevant parts of input data.
• Generative Adversarial Networks (GANs): GANs build on the concept of the diffusion model, training two neural networks in competition. The “generator” creates fake data samples by adding random noise to existing data. The “discriminator” tries to determine what data is real. Over time, the generator constantly creates more realistic data, while the discriminator gets better at spotting fakes.

How Generative AI Works: The Basics

The most common question after “What is Generative AI?” is “How does it work?” You probably already know how to use a generative AI solution like ChatGPT.

You input a prompt (in the form of a text, image, video, or code), and the AI responds accordingly, creating new content or answering questions. However, understanding how generative AI works (behind the scenes) is a bit more complex.

Typically, generative AI operates in 3 phases:

Training Generative AI

Training generative AI solutions is how developers teach them to perform in a specific way, or complete certain tasks. The training process starts with a foundation model, such as a large language model, for text generation applications, or a transformer model.

To create these models, developers train a deep learning algorithm on huge amounts of unlabeled, and unstructured data. For instance, many LLMs are trained with terabytes of information from the web. During the training process, the algorithm evaluates various “fill in the blank” tasks, predicting the next element in a sequence, and adjusting itself to optimize accuracy.

The result of all this training is a neural network of encoded representations of patterns, relationships, and entities in data. The network can generate content automatically, in response to inputs, such as prompts. It might sound simple, but this training requires huge amounts of data, and a lot of computing power, which is why so many companies struggle to build their own Gen AI systems.

The Fine Tuning Process

The ability to create bots that could learn using unlabeled data was the key innovation that made generative AI models so exciting. However, many initial generative AI solutions suffered from a range of issues, such as hallucinations, and biases, that made them less effective.

As a result, companies began implementing fine-tuning mechanisms, to enhance the performance of their tools. Fine-tuning is a process that involve feeding a specific model certain pieces of labeled data, relevant to its application. For instance, a company creating a customer service chatbot would label hundreds of customer service queries and correct answers for the model.

Although there are ways to fine-tune models without as much human input, such as zero-shot, and few-shot learning techniques, these strategies can be complex and expensive. Most recently, more companies have begun relying on “Reinforcement Learning, with Human Feedback”, or RLFF.

In RLFH, human beings in a team respond to the content generated by a system with insights on how the model can improve. People “score” outputs based on their accuracy, and help the bots learn.

Evaluation and Optimization

Designing a fantastic generative AI tool takes a lot of time and effort. Even the biggest innovators in the market, like OpenAI, are constantly updating their strategies and introducing new models. To ensure these models are as accurate as possible, developers constantly assess their output, and regularly tune the model, based on feedback and insights.

Some companies even use specialized methods to enhance an AI app’s performance. For instance, Retrieval Augmented Generation (RAG), allows developers to extend their foundation model to use additional data sources outside of the original training data set.

RAG can help to ensure that generative AI apps have access to more up-to-date and current information, improving its accuracy and performance.

The Top Generative AI Use Cases for Businesses

So, what are the main generative AI use cases for businesses? As AI technology for enterprises continues to evolve, the potential answer to that question is changing. Still, some of the most common applications for generative AI include:

Content Generation and Marketing

Marketing teams and content creators were some of the first people to ask “What is generative AI – and what can it do for me?” Studies from the American Marketing Association have found that around 90% of marketers had used generative AI for business tasks by 2024.

Generative AI tools can create content, briefs, and brainstorm ideas for marketing strategies in an instant. Advanced multimodal tools don’t just create text either, they can produce:

• Text: Many generative AI models, such as those based on transformers, can generate contextually relevant text in various forms. They can write reports, blogs, website copy, papers, and even snippets of novels.
• Visuals:Image generation tools like Midjourney and DALL-E can create realistic images and art, and transfer styles from one image to another. Some tools can even create animations and videos from text prompts, or apply special effects to clips.
• Sound:Generative AI models can synthesize natural-sounding audio, and speech for digital assistants and chatbots, audiobook narrations, and more. There are even tools that can generate original music for advertisements and other purposes.
• Code:Tools like Github Copilot can generate original code, complete snippets of code, and even translate between programming languages. It can help developers to prototype and debug applications, and create software at speed.
• Data:Some generative AI models can be trained to generate synthetic data. For instance, AI tools can be applied in the medical industry to generate molecular structures for pharmaceuticals with specific properties.

Design Automation: Accelerating Product Design

Still wondering “what is generative AI good for?” how about designing high-quality products faster? Traditional product design and development tasks is time-consuming and complex. With the benefits of generative AI, companies can tap into:

• AI-Powered Ideation: Generative AI tools can create design concepts, prototypes, and mockups based on simple prompts. Designers can use these outputs as a foundation, refining and iterating on ideas faster than ever before.
• Accelerated Engineering: For industries like automotive, aerospace, and manufacturing, generative AI algorithms can design parts and structures optimized for performance and material efficiency. AI tools analyze constraints like weight, durability, and cost to suggest innovative solutions.
• Creative Marketing: Marketing teams can leverage generative AI to brainstorm and generate campaign ideas, develop visuals, and even write personalized advertisements tailored to different demographics.

Companies can even combine generative AI with extended reality, to experiment with products and their functionality in the virtual world.

Customer Experience: Enhancing Satisfaction and Personalization

Probably one of the most common use cases for generative AI is in the world of customer experience. Generative AI bots, and now agentic AI systems can confidently handle a range of customer service tasks, from surfacing information to customers, to recommending specific products.

AI-powered chatbots, voice bots, and assistants can deliver more convenient 24/7 service to customers. For instance, the Expedia group uses ChatGPT to help customers search for flights, find accommodations, and even plan trips autonomously.

However, generative AI isn’t just great at “fully automating” aspects of customer service. Companies can also tap into the benefits of generative AI to augment human agents.

Intelligent assistants can handle tasks like summarizing information and calls, analyzing transcripts, and crafting personalized messages for customers. Some gen AI tools can even analyze the sentiment in a customer’s voice or text during a conversation, and suggest real-time strategies to help customer service representatives improve satisfaction rates.

Business Process Automation: Scaling Efficiency

If you think the answer to “what is generative AI” is “An automatic content creator”, it’s time to think again. Generative doesn’t just automate content creation. It can streamline a range of business tasks.

Unlike traditional automation tools, generative AI systems understand and respond to natural language, meaning they can handle a wider range of activities. For instance, they can perform managerial activities, like prioritizing tasks in a project management app, organizing emails, or scheduling meetings. They can search for and consolidate information from across your software ecosystem, and create standardized versions of documents and reports.

Gen AI tools can even transcribe and translate calls, and enter information into another application, like a CRM, on behalf of your employees. According to McKinsey, Gen AI has the potential to automate up to 70% of the tasks that take up the most employee time.

That means Generative AI makes your teams more efficient and productive, increases employee engagement, and reduces operational costs, all at once.

Data Analytics: Unlocking Insights

Data is the lifeblood of modern businesses, and generative AI takes data analysis to the next level. Like many AI solutions, generative AI is excellent at analyzing large data sets, identifying patterns, and extracting valuable insights for business leaders. It can generate recommendations and hypotheses based on those insights, to help drive organizations towards their goals.

Aside from taking advantage of these capabilities for research and development purposes, organizations are also using generative AI to better map customer journeys, optimize resource allocation, and discover new opportunities.

Plus, scientists, engineers, and researchers are using generative AI capabilities to address a wide range of complex problems. For instance, in the pharma and healthcare industry generative AI systems can generate and optimize protein sequences, synthesize medical images for training, and accelerate drug discovery. Studies indicate that companies who tap into the benefits of generative AI for data analysis report increased competitiveness (52%), and better product functionality (45%).

Competitive Advantages: The Core Benefits of Generative AI

By now, you should have a pretty good answer to the question “what is generative AI”, and a decent insight into what generative AI for business use cases look like. But let’s break things down even further – to the three core competitive advantages Gen AI gives enterprises.

Faster, Stronger Innovation

Generative AI turbocharges innovation, streamlining creativity, research, and development. Generative AI can simulate designs, help teams generate prototypes, and even optimize engineering workflows, reducing time-to-market for new products.

It can enhance R&D strategies, by helping companies visualize data and discover opportunities for breakthroughs. Plus, generative AI can easily evaluate huge datasets, to suggest optimal strategies, identify trends, and predict future outcomes. This empowers leaders to make faster, more informed decisions, driving sustainable growth.

Reducing Operational Costs

Gen AI empowers companies to optimize everyday processes in various environments, driving significant cost savings. With generative AI to automate repetitive tasks, companies can focus their resources on more important areas. In fact, Deloitte found that 82% of business leaders believe AI will improve the performance of their employees.

Some AI adopters even believe generative AI and similar advanced models will enable four-day work week opportunities, and significantly reduce the number of team members companies need to hire. Although generative AI doesn’t eliminate the need for human employees entirely, it does make team members faster, and more efficient in their roles, leading to cost savings.

Enabling Business Growth

Generative AI doesn’t just automate; it inspires. It amplifies human creativity, driving innovation in marketing, product development, and beyond. Generative AI tools in the workplace can act as a creative partner, generating fresh ideas for campaigns, product designs, or software code.

These tools can enhance employee creativity by handling tedious tasks, leaving more room for strategic thinking and innovation. Plus, generative AI empowers businesses to personalize customer experiences at scale, fostering deeper engagement and loyalty, that leads to continued growth.

What is Generative AI: Limitations to Consider

Now you know the answer to “What is generative AI?” and what it can do, you might be wondering what its limitations are. As mentioned above, generative AI models are advancing. However, they still suffer from various issues, such as:

• Hallucinations and inaccuracies: Gen AI can make mistakes. Based on the data its given, it might generate inaccurate answers to questions in a confident manner, leading to confusion and mistakes. Developers attempt to minimize hallucinations by using “guardrails”, to determine how AI models source data for answers.
• Inconsistency:Generative AI models are probability-driven tools, which means they often create different outputs to the same questions. This can be problematic in some use cases, such as in customer service, where consistency is crucial. Prompt engineering can be a useful way to reduce the risk of inconsistencies.
• Bias:Gen AI models are only as good as the data they’re given. If their training data is incomplete or biased in some way, this can cause bots to act in a biased fashion. That’s why it’s so crucial for developers to use diverse training data, and use human-in-the-loop methods to constantly refine AI models.
• Explainability: A lot of generative AI models are still defined as “black box” models, which means people don’t fully understand how they make decisions. This can cause issues with compliance and security.
• Security and privacy threats: Gen AI models can be exploited to create phishing emails, deepfakes, and malicious content that can fool users into revealing valuable data. Additionally, some of these solutions may use sensitive data to make decisions, which exposes companies to compliance issues.

What is Generative AI? Future Adoption

The rise of generative AI marks a crucial turning point in the history of technology. Today, countless companies are investing in generative AI for business applications, and demand only continues to grow. The generative AI market is growing at a CAGR of around 42%, according to market reports – and could be a $1.3 trillion market by 2032.

McKinsey believes generative AI for business applications will add up to $4.4 trillion to the global economy each year. Additionally, according to Gartner’s polls, companies are using generative AI for more use cases than ever before. Around 40% of respondents in a 2024 survey said that they’ve deployed generative AI in more than three areas of their business.

Though executives and business leaders are aware of the challenges and limitations of generative AI – they believe the benefits outweigh the risks. As developers produce more advanced language models, neural networks, and tools for training and fine-tuning generative AI, adoption will continue to grow.

Generative AI will automate more tasks than ever before (particularly with the rise of autonomous agents), and introduce new ways to augment and enhance human creativity. Are you ready to start your generative AI journey?