Generative AI Market to Grow at CAGR of 36 10% through 2032

Generative AI Market Size, Share, Trends Analysis Report 2023-28

We analyzed only use cases for which generative AI could deliver a significant improvement in the outputs that drive key value. In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy.

This is attributable to the existence of leading companies researching & developing generative AI applications. Another website has  more than two million photos, royalty free, of people who never existed but look like real people. You can select different parameters to get images that fit the specific criteria, and all this is generated by AI; none of these people even exist. Now the typical use case is the intelligent upscaling of low resolution images to high resolution images using complex AI image generation techniques.

How Business Leaders Can Get Started with Generative AI

Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. As per the IMARC Group, the size of global generative AI market was US$ 10.3 Billion in 2022. The rising advancements in deep learning and machine learning and the rising use of artificial intelligence (AI)-generated content for marketed strategies will also drive the generative AI market growth.

Static 2D images are the easiest to fake, but today we face the new threat of fake videos. Better grammar and spelling is something we use everyday without even thinking about. Definition based rule engines are augmented or even replaced by machine learning (ML) algorithms and they have proved to be more effective and accurate than previous ones. The new wave of generative AI systems, such as ChatGPT, have the potential to transform entire industries.

IBM watsonx Assistant transforms content into conversational answers with generative AI

Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply.

NLP is a powerful generative AI tool with numerous text and speech generation applications. Deep learning advances have resulted in the development of neural NLP models, such as Recurrent Neural Networks (RNNs), and transformer models, such as BERT, developed by researchers at Google AI Language and GPT-3, developed by OpenAI, a U.S.-based AI company. These models significantly enhanced the accuracy and efficiency of NLP-based generative AI applications, propelling the growth of the segment. Data augumentation is a process of generating new training data by applying various image transformations such as flipping, cropping, rotating, and color jittering. The goal is to increase the diversity of training data and avoid overfitting, which can lead to better performance of machine learning models. Generative AI is a set of algorithms, capable of generating seemingly new, realistic content—such as text, images, or audio—from the training data.

Among the 360+ generative AI companies we’ve identified, 27% have yet to raise any outside equity funding. Meanwhile, over half are Series A or earlier, highlighting the early-stage nature of the space. Having not tested AI21 Labs’ products recently, I can’t speak to the veracity of those claims. But exaggerated or no, AI21 Labs appears to be gaining some traction despite its disadvantages. Google, AWS and Microsoft offer tooling comparable to AI21 Studio, as do startups like Cohere, OpenAI and Anthropic (and to a lesser extent marketing-focused vendors such as Jasper, Regie and Typeface).

Yakov Livshits

Also, the rising growth of the information technology (IT) sector and the propelling use of AI-integrated systems across various verticals to promote both productivity and agility will lead to an increase in the genrative ai growth. The deployment of artificial intelligence is evolving due to generative AI and other foundation models, which also speed up application development and give non-technical people access to significant capabilities. The emergence of generative AI has the potential to alter the business landscape fundamentally.

Prominent research centers and universities in the region conduct cutting-edge research, publish influential papers, and contribute to the development of generative AI techniques. Besides this, the region’s large population, high consumer spending, and advanced technology infrastructure create a favorable environment for the adoption and commercialization of generative AI solutions. Furthermore, North America has relatively supportive regulations and policies for AI and emerging technologies. Governments in the region have recognized the potential of AI and actively promote its development through investments, research grants, and initiatives.

Artificial intelligence (AI) usually means machine learning (ML) and other related technologies used for business. Reuters, the news and media division of Thomson Reuters, is the world’s largest multimedia news provider, reaching billions of people worldwide every day. Reuters provides business, financial, national and international news to professionals via desktop terminals, the world’s media organizations, industry events and directly to consumers. The new chip, called TPU v5e, is designed to train large models but also efficiently serve content from those models. To bolster Google’s enterprise cloud service it added 20 AI models to its collection, bringing the total to 100.

Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12). Based on a historical analysis of various technologies, we modeled a range of adoption timelines from eight to 27 years between the beginning of adoption and its plateau, using sigmoidal curves (S-curves). This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms.

Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. In this section, we highlight the value potential of generative AI across business functions.

• This includes, for example, capabilities to incorporate and label additional training data or build the APIs that allow applications to interact with it.
• Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7).
• These startups are developing cutting-edge AI solutions, including generative adversarial networks (GANs), deep learning models, and creative AI platforms.
• The market is primarily driven by the expanding information technology (IT) sector and the increasing usage of AI-integrated systems for enhancing productivity and agility.

Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value. Gen AI’s precise impact will depend on a variety of factors, such as the mix and importance of different business functions, as well as the scale of an industry’s revenue. Nearly all industries will see the most significant gains from deployment of the technology in their marketing and sales functions. But high tech and banking will see even more impact via gen AI’s potential to accelerate software development.

The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities. The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction.