Generative AI: driving growth in the rapidly evolving AI market

A technology called DALL-E creates paintings based on descriptive words, and Chat Generative Pre-trained Transformer (ChatGPT) can write economic commentary or even computer code based on a few prompts. To be sure, although the ChatGPT and DALL-E functionality and user interfaces are remarkable, the underlying technology is already several years old but improving rapidly. Private and open source advanced GPT models have been created by Meta Platforms, Microsoft, Google and more since 2018. “Generative AI has the potential for innovation and disruption to software markets and applications that we’ve seen only a few times in history,” says Ritu Jyoti, group VP, Artificial Intelligence (AI) and Automation Research at IDC.

These companies have employed various strategies, such as product launches, partnerships, and acquisitions, to expand their presence in the market. Read how disruption is creating opportunities through change, for both corporates and investors, across a range of industries and sectors. Transformer architectures learn context and, thus, meaning, by tracking relationships in sequential data. Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other. A 2010 study showed the average cost of taking a drug from discovery to market was about $1.8 billion, of which drug discovery costs represented about a third, and the discovery process took a whopping three to six years. Generative AI has already been used to design drugs for various uses within months, offering pharma significant opportunities to reduce both the costs and timeline of drug discovery.

Global Generative AI Market Segmentation: By End User Industry

The complexity of the models and the large amount of data needed for training can pose challenges in terms of scalability & infrastructure requirements. This can limit the accessibility & practicality of generative AI for smaller organizations or individuals with limited resources. The rising demand for cutting-edge image generation and enhancement tools is another factor contributing to the growth of the Global Generative AI Market. Segmenting the market into smaller components helps in analyzing the dynamics of the market with more clarity.

• Image manipulation allows tweaks in style, lighting, color, or shape while preserving the original elements.
• This raises concerns about data privacy, as large datasets might contain sensitive information about individuals.
• Semantic image-to-photo translation permits the generation of photo-realistic images from sketches or semantic representations.
• The rapid advancements in artificial intelligence, specifically in the field of Generative AI, have been a significant driver for market growth.

Similarly to that, the growing trend of working from home and the increasing number of smartphone users have also supported the generative AI market growth. Generative AI is a powerful new technology that can create new things rather than analyze existing ones. Generative artificial intelligence is a term that refers to the creation of artifacts that previously relied on humans. As a result, generative algorithms can now generate data using models to generate increasingly realistic images, videos, and sounds.

Generative AI Market Analysis and Global Forecast (2023- with COVID Impact Analysis

For instance, Brands like H&M and Adidas have used generative AI to create clothing designs and custom sneakers. Moreover, this technology has also been used to generate unique patterns for fabrics and prints, saving designers time and effort. The global Yakov Livshits size was valued at USD 10.14 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 35.6% from 2023 to 2030. In the future, generative artificial intelligence (AI) will further witness growth, as the advancements in machine learning and deep learning seek to increase.

Additionally, LLMs are transforming data analysis by extracting insights from unstructured text data, enhancing decision-making processes. As LLMs continue to evolve, they promise to revolutionize the way businesses operate, making them more efficient and responsive to customer needs. Key factors driving the Generative AI market growth include rising applications of novel technologies and growing demand to modernize workflow across industries. The multi-modal generative model is expected to witness the fastest growth rate of 41.6% during the forecast period.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

The report focuses on growth prospects, restraints, and trends of the generative AI market analysis. Many artificial intelligence (AI) developers use generative AI to create new virtual worlds. Virtual reality (VR) developers use generative AI to create exclusive and immersive gaming environments, and implementing VR games and training simulations use cases has significant benefits, and this will significantly propel the market during the forecast period.

These upgrades allow designers to generate facial expressions from audio files using Audio2Face, emotions using Audio2Emotion, and gestures using Audio2Gesture. Key players’ strategic breakthroughs or developments are projected to boost the growth of the generative AI market. The growing market for technology can help companies personalize customer interactions, unlock innovation through unconventional creativity, and better access enterprise data and knowledge to create value in new ways. Additionally, Generative AI can significantly reduce the manual effort required in areas such as order management and other administrative requests, all of which are key market drivers boosting the growth of the Generative AI market. The Generative AI Market industry report includes the adoption lifecycle of the market, covering from the innovator’s stage to the laggard’s stage. Furthermore, the report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their growth strategies.

Use AI-generated content as a starting point and add your knowledge to it to ensure it adheres to your brand’s core principles and appeals to your target market. However, generative AI is not a replacement for human creativity but a powerful tool that empowers marketers to push the boundaries of what is possible in content creation and deliver impactful campaigns that drive results. Generative AI models, such as ChatGPT, enable brainstorming sessions, offering creative suggestions and alternative perspectives.

For example, in computer vision, a larger dataset of images can help generative AI models produce more visually convincing and detailed images. Besides, the increasing volume of generated data can be used for data augmentation and synthesis purposes. By combining real and generated data, generative AI models can be trained on augmented datasets to improve generalization and adaptability. This approach helps address challenges like limited real-world training data and enables generative AI models to handle a wider range of scenarios. Large Language Models are leading the market and are estimated to grow significantly during the forecast period. The segment’s development can be ascribed to various applications, such as chatbots that can converse with users and content-generation tools that compose product descriptions and articles.

2. North America

ChatGPT is an example of a large language model (LLM) – essentially, software trained on immense sets of text data to execute specific tasks, such as finishing a sentence or completing a line of code. LLMs have billions of variables (parameters) that they can change as they learn – and as a result, Yakov Livshits as their accuracy rate increases, so do the business use cases. For example, an AI-driven chatbot might be able to answer 90% of questions from banking customers, freeing up employees to spend more time selling services or provide a better in-person experience to the bank’s highest-value clients.

Their ability to produce high-quality outputs with a wide range of applications across industries has made GANs the technology of choice for many companies, leading to their significant market share. On the other hand, the retrieval augmented generation segment is expected to be the fastest-growing segment during the forecast period. This growth can be attributed to the increasing demand for more controllable and contextually relevant content generation. Retrieval augmented generation combines the power of retrieval models and generative models, allowing users to specify desired attributes or content from existing data, which the generative model then incorporates to create tailored outputs. This technology finds applications in personalized content generation, recommendation systems, and interactive AI-driven interfaces. As businesses and users seek more interactive and customizable AI-generated content, retrieval augmented generation offers a compelling solution, driving its rapid growth in the generative artificial intelligence market.

Generative AI enables hyperpersonalization by analyzing vast data and tailoring content to individual preferences and behaviors. B2B marketers can leverage AI-generated content to design highly personalized campaigns for companies they are targeting, improving customer experiences and achieving higher conversion rates. By 2028, the market for AI in marketing is predicted to reach $107.5 billion, a significant increase from the estimated $15.84 billion in 2021.