Generative AI: Market Overview, Trends, and Enterprise Use Cases

Executive Summary

One of the latest and most controversial developments in the field of artificial intelligence, generative AI (GAI) refers to programs that can generate original content, literally creating new digital images, video, audio, text, and code. In the words of a leading generative AI program, ChatGPT (the January 9, 2023 version), produced by OpenAI:

“Generative AI is a type of artificial intelligence that is used to generate new and unique data, such as text, images, and audio. This is done by training a model on a data set of existing data, and then using that model to generate new, similar data. Generative AI models can be used for a variety of tasks, such as image synthesis, language translation, and text generation.”

The figure below offers an example of image synthesis in the form of a generative AI portrait.

Example of a Generative AI Portrait
Source: Wikimedia Commons

Generative AI has experienced increased scrutiny owing to its ability to disrupt or even displace the human element in creative arts. Educators, for example, worry whether high school students will leverage ChatGPT or other generative AI programs to write or substantially contribute to their term papers. Professional writers are concerned that next-gen GAI programs will rob them of their livelihood.

Compounding the problem is the issue of intellectual property. If a GAI program responds to a few well-constructed and incisive questions by producing a masterwork, the elusive “great American novel” for example, who has the rights of authorship? The person who posed the questions? The company that produced the program? Neither? Both? It’s a legal minefield that, ironically, might have to be negotiated by a future GAI legal aide.

While generative AI may be troublesome for those in the creative community, it offers great opportunities for enterprise adopters. As ChatGPT, itself, reveals: “Generative AI is used today in a wide range of applications, including:

“Generating images and videos – Generative models can be trained on large data sets of images and videos, and then used to generate new, similar images and videos. This can be used for tasks such as video game character generation, digital art, and creating realistic special effects in movies.

“Language generation – Generative models can be trained on large data sets of text and used to generate new text. This can be used for tasks such as machine translation, text summarization, and content creation.

“Audio generation – Generative models can be trained on large data sets of audio and used to generate new, similar audio. This can be used for tasks such as music composition, speech synthesis, and sound effects generation.

“Generative design – Generative models can be used to create new designs for products, buildings, and other structures. This can be used for tasks such as architectural design, industrial design, and fashion design.

“Anomaly detection – Generative models can be trained on normal patterns of data and used to identify abnormal patterns. This can be used for tasks such as fraud detection, fault detection, and medical imaging.

“Recommender systems – Generative models can be trained on data about users’ preferences and used to generate personalized recommendations for content, products, etc.”

A Way to Go

Despite its potential, generative AI is, at present, a proverbial work in progress. Analyst Matteo Wong observes that while “[the] ability of AI to answer any prompt with human-sounding language can suggest that the machine has some sort of intent, even sentience,” today’s results are more representative of the efforts of a precocious child than a seasoned scholar. “Although ChatGPT can generate fluent and sometimes elegant prose, easily passing the Turing-test benchmark that has haunted the field of AI for more than 70 years, it can also seem incredibly dumb, even dangerous. It gets math wrong, fails to give the most basic cooking instructions, and displays shocking biases.”¹

CIOs, Importantly, Are Engaged

Before any new technology – especially one as revolutionary as generative AI – can be incorporated into enterprise operations, enterprise officials, and chief information officers (CIOs) in particular, must understand what the technology can do, what it cannot do, and how it should be deployed (at least initially).

To that end, the Massachusetts Institute of Technology (MIT) surveyed prominent CIOs, senior executives, and other experts to gain the CIO perspective on generative AI. Among their key findings:

Generative AI is already available to enterprise employees. Productive containment is the only viable approach to gen AI management. “Generative AI and LLMs are democratizing access to artificial intelligence, finally sparking the beginnings of truly enterprise-wide AI.”

Next-generation data storage systems are needed for generative AI. “The generative AI era requires a data infrastructure that is flexible, scalable, and efficient.”

Open-source generative AI offers certain security and privacy advantages. “Some organizations seek to leverage open-source technology to build their own LLMs [language-learning models], capitalizing on and protecting their own data and IP.”

Generative AI is expected to improve productivity, but not at the expense of wholesale job loss. “Automation anxiety should not be ignored, but dystopian forecasts are overblown.”

Governance must precede generative AI. “Unified and consistent governance are the rails on which AI can speed forward. Generative AI brings commercial and societal risks, including protecting commercially sensitive IP, copyright infringement, unreliable or unexplainable results, and toxic content.”²

The Market

Explosive Growth

As predicted by Market.us, the global generative AI market, valued at $13.9 billion in 2023, should reach $151.9 billion by 2032, reflecting a staggering compound annual growth rate (CAGR) of 31.4 percent.³

Job Automation

While still in its technological infancy, cost-conscious enterprise planners see the potential for automating not just “blue-collar” jobs, but, surprisingly, more expensive and intellectually challenging “white-collar” work. As analyst Derek Thompson reminds us, “[In] 2013, researchers at Oxford published an analysis of the jobs most likely to be threatened by automation and artificial intelligence. At the top of the list were occupations such as telemarketing, hand sewing, and brokerage clerking. These and other at-risk jobs involved doing repetitive and unimaginative work, which seemed to make them easy pickings for AI. In contrast, the jobs deemed most resilient to disruption included many artistic professions, such as illustrating and writing. The Oxford report encapsulated the conventional wisdom of the time – and, perhaps, of all time. Advanced technology ought to endanger simple or routine-based work before it encroaches on professions that require the fullest expression of our creative potential. Machinists and menial laborers, watch out. Authors and architects, you’re safe.

“[So far, however], we’ve seen a flurry of AI products that seem to do precisely what the Oxford researchers considered nearly impossible: mimic creativity. Language-learning models such as GPT-3 now answer questions and write articles with astonishingly human-like precision and flair. Image-generators such as DALL-E 2 transform text prompts into gorgeous – or, if you’d prefer, hideously tacky – images.”⁴

Market Drivers

In addition to reducing the need for human talent, the market for generative AI systems is being bolstered by:

Early adoption in the healthcare, IT, robotics, banking, and finance sectors.

R&D investment from major market players like Apple and Microsoft.

The use of generative AI in building metaverse worlds.

The trend of creating digital artworks using text-based descriptions.⁵

Market Inhibitors

Like all evolving technologies, generative AI is experiencing “growing pains,” with issues related to:

Disinformation, as GAI is not always adept at interpreting open-source information, as available on the Internet, for example.

Security, as GAI can be employed to produce “deepfakes”, images and videos which seem realistic but are false and deceptive.

Privacy, as GAI is injected into healthcare and other personal-privacy-sensitive applications.

Copyright, as GAI creates intellectual property with no legally established owner or controlling authority.

Support, as few enterprise staff are trained in GAI systems and techniques.⁶

Use Cases

As itemized by McKinsey analysts, the enterprise uses for generative AI literally “abound”. Among the first batch are:

Sales & Marketing – Crafting personalized messages for individual customers and creating conversational assistants (or chatbots) aligned to specific businesses and product lines.

Operations – Developing effective and efficient procedures.

Information Technology/Engineering – Writing, reviewing, and often overlooked, documenting program code or instruction sets.

Risk & Legal – Answering complex questions, often by mining mountains of documentation; also, drafting and reviewing annual reports and other official materials.

Research & Development – In medicine, for example, accelerating drug discovery and development through better understanding of diseases and chemical structures.

Human Resources – Fashioning incisive interview questions to aid in candidate assessment.

Interpersonal Communication – Optimizing employee e-mail and text exchanges to improve understanding and avoid counterproductive language or tone.⁷

In reporting on the generative AI market, Precedence Research cites applications including:

Audio Synthesis – Generative AI can transform a computer-generated voice into something sounding authentically human.

Healthcare – When combined with 3D printing, CRISPR (DNA sequencing), and other technologies, GAI can help create prosthetic limbs, organic molecules, and other medical material.

Identity Protection – “In October 2022, GAI avatars were deployed in news reports regarding the prejudice towards LGBTQ people in Russia to obfuscate the identities of interviewees.”⁸

Generative AI Trends

GAI is going mainstream.  According to analyst Vincent Koc, who cites a Forbes analysis, “97 percent of business owners already believe that generative AI tools such as ChatGPT will have a positive impact to their business.”⁹

GAI is turning multi-modal.  OpenAI’s GPT-4 model can now respond to audio and visual input.¹⁰

GAI is incorporating RAG, or “retrieval-augmented generation” technology.  IBM describes RAG as “an AI framework for improving the quality of [Large Language Model (LLM)-generated] responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information.”¹¹ Importantly, RAG is expected to reduce the incidence of GAI “hallucinations.” One of the primary problems with ChatGPT (and other GAI chatbots) is their propensity to make things up, to lie or exaggerate even in situations where a specific fabrication may be obvious.  Unfortunately, in many, if not most, cases a GAI deception is not readily discernible, rendering a chatbot’s output unreliable, even harmful depending on its context.

GAI is fostering conversational AI. As analyst Anastacia Zharovskikh asserts, “Conversational AI has emerged as a significant trend within the field of generative AI due to its ability to enable natural and interactive human-machine conversations. Its technologies, such as chatbots and virtual assistants, utilize Generative AI models to understand and generate human-like responses in real time.”¹²

GAI is helping realize IPA, or “intelligent process automation.” As analyst Zharovskikh points out, “By leveraging Generative AI, IPA systems can understand and interpret unstructured data, extract relevant information, and make intelligent decisions. This enables businesses to automate complex tasks that require cognitive abilities, such as natural language processing, sentiment analysis, and image recognition. As a result, many companies observe a surge in overall performance and make more informed decisions.”¹³

Promise or Peril

Like many technologies that preceded it – video surveillance, biometric identification, self-driving vehicles – generative AI (much like AI in general) is viewed as a hopeful development or something to be feared.

In discussing the impact and influence of ChatGPT, analyst Reid Hoffman fairly summarizes the opposing camps, using popular science fiction series as metaphors.

Pro-GAI – “For some, ChatGPT promises to revolutionize the way we search for information, draft articles, write software code, and create business plans. When they use ChatGPT, they see Star Trek: a future in which opportunities for personal fulfillment are as large as the universe itself.”

Anti-GAI – “Others see only massive job displacement and a profound loss of agency, as we hand off creative processes that were once the domain of humans to machines. When they use ChatGPT, they see Black Mirror: a future in which technological innovation primarily exists to annoy, humiliate, terrify, and, most of all, dehumanize humanity.”

While Hoffman considers himself pro-GAI, or “firmly in the Star Trek camp,” many enterprise officials, even those anxious to exploit the full capabilities and efficiencies made possible by generative AI, might be inclined to deploy GAI slowly and judiciously, focusing on applications that improve productivity without necessarily slashing headcount.¹⁴

A Generated Image of a “A Writer Laboring Over Her Work”
Source: Stable Diffusion

Predicting the future of technology is always difficult. In the case of generative AI, however, we can ask the technology, at least one of its earliest incarnations. According to ChatGPT:

“The future of generative AI is likely to see continued advancements in the capabilities of generative models and the diversity of their applications. Some potential areas of development include:

• “Improved model architectures and training methods, resulting in more realistic and high-quality generated data.
• “Wider adoption of generative AI in various industries, such as entertainment, art, and design.
• “Greater use of generative models in combination with other AI techniques, such as reinforcement learning and transfer learning, to solve more complex tasks.
• “Development of new types of generative models, such as models that can generate multiple types of data (e.g. text and images) or models that can generate data in multiple languages.

“However, it is also important to consider the ethical implications of generative AI, such as issues of bias and accountability. As the technology develops, it will be important to consider how to ensure that these systems are used responsibly and for the benefit of society.”

While acknowledging the “ethical implications,” the technology is, not surprisingly, bullish on itself.

Some humans, however, are less sanguine about the prospect of co-existing with generative AI. Analyst Annie Lowrey predicts that:

“In the next five years, it is likely that AI will begin to reduce employment for college-educated workers. As the technology continues to advance, it will be able to perform tasks that were previously thought to require a high level of education and skill. This could lead to a displacement of workers in certain industries, as companies look to cut costs by automating processes. While it is difficult to predict the exact extent of this trend, it is clear that AI will have a significant impact on the job market for college-educated workers.”¹⁶

Preparing for Generative AI

From a practical perspective, McKinsey recommends that enterprise executives assemble a cross-functional team to examine the likely effects of generative AI on their industry and their business, starting with a few basic questions:

“Where might the technology aid or disrupt our industry and/or our business’s value chain?

“What are our policies and posture? For example, are we watchfully waiting to see how the technology evolves, investing in pilots, or looking to build a new business? Should the posture vary across areas of the business?

“Given the limitations of the models, what are our criteria for selecting use cases to target?

“How do we pursue building an effective ecosystem of partners, communities, and platforms?

“What legal and community standards should these models adhere to so we can maintain trust with our stakeholders?”¹⁷

Importantly, since generative AI is self-generating in terms of its development, the time available to answer these questions, and to formulate a GAI strategy may be short, increasing the urgency for prompt analysis, if not actual action.

For individuals in the creative community, whether writers, artists, or performers, it might be wise to concentrate on hard-to-machine-replicate capabilities, weaving, for example, humor, satire, or improvisation into their work.

Web Links

McKinsey & Company: https://www.mckinsey.com/
Midjourney: https://www.midjourney.com/
OpenAI: https://www.openai.com/
Stable Diffusion: https://www.stablediffusionweb.com/
US National Institute of Standards and Technology: https://www.nist.gov/

References

¹ Matteo Wong. “The Difference Between Speaking and Thinking.” The Atlantic | The Atlantic Monthly Group. January 31, 2023.

² “The Great Acceleration: CIO Perspectives on Generative AI.” MIT Technology Review Insights, 2023:5.

³ “Global Generative AI Market By Component (Services and Software), By Technology (Generative Adversarial Networks, Transformer, Variational Auto-encoder, and Diffusion Networks), By End-User, By Region and Companies – Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032.” Market.us. October 2023.

⁴ Derek Thompson. “Your Creativity Won’t Save Your Job From AI.” The Atlantic | The Atlantic Monthly Group. December 1, 2022.

^5-6 “Generative AI Market (By Component: Software, Services; By Technology: Generative Adversarial Networks (GANs), Transformers, Variational Auto-encoders, Diffusion Networks; By End-Use: Automotive & Transportation, BFSI, Media & Entertainment, IT & Telecommunication, Healthcare, Others) – Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032.” Precedence Research. December 2022.

⁷ Michael Chui, Roger Roberts, and Lareina Yee. “Generative AI Is Here: How Tools Like ChatGPT Could Change Your Business.” McKinsey & Company. December 20, 2022.

⁸ “Generative AI Market (By Component: Software, Services; By Technology: Generative Adversarial Networks (GANs), Transformers, Variational Auto-encoders, Diffusion Networks; By End-Use: Automotive & Transportation, BFSI, Media & Entertainment, IT & Telecommunication, Healthcare, Others) – Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032.” Precedence Research. December 2022.

⁹ Vincent Koc. “Navigating the AI Landscape of 2024: Trends, Predictions, and Possibilities.” Towards Data Science. January 2, 2024.

¹⁰ Lev Craig. “10 Top AI and Machine Learning Trends for 2024.” TechTarget. January 4, 2024.

¹¹ Kim Martineau. “What Is Retrieval-Augmented Generation?” IBM. August 22, 2023.

^12-13 Anastacia Zharovskikh. “Generative AI: Trends, Benefits, Applications, Opportunities.”  InData Labs. January 16, 2024.

¹⁴ Reid Hoffman. “Technology Makes Us More Human.” The Atlantic | The Atlantic Monthly Group. January 28, 2023.

¹⁵ Jon Gold. “Generative AI Hype Dampens VC Funding for Quantum Computing.” Network World | IDG Communications, Inc. February 2, 2024.

¹⁶ Annie Lowrey. “How ChatGPT Will Destabilize White-Collar Work.” The Atlantic | The Atlantic Monthly Group. January 20, 2023.

¹⁷ Michael Chui, Roger Roberts, and Lareina Yee. “Generative AI Is Here: How Tools Like ChatGPT Could Change Your Business.” McKinsey & Company. December 20, 2022.