Organizations can leverage generative AI to create authentic content, including text, 3D objects, images, and videos from existing data. This technology enables personalized content development, increased content creativity, automated marketing, and content generation, enhanced content quality, and improved customer service.
In addition, many sectors harness these benefits, including financial institutions for fraud detection, marketing for content and campaign development, and drug discovery in healthcare. This article will explore how generative AI supports organizations, what the market size entails, the top disadvantages to consider, and the primary applications of this technology.
What Is Generative AI?
Generative AI is a set of algorithms that produce new data (text, code, images, video, simulations, 3D objects, etc.) from existing training data. This technology is notable progress from standard AI since it not only classifies or identifies information, but also generates authentic, new content from training data.
ChatGPT by OpenAI is an excellent example of a generative AI learning model offering multiple applications. For instance, this AI platform can curate blog articles, product designs, and social media captions, among many other marketing and advertising materials. In addition, users can rely on ChatGPT to generate and debug code and get answers to complex questions in seconds, such as “What is Foucault’s pendulum?”.
Furthermore, generative AI has many other applications (which we will review further below), and it has become a popular solution globally. According to Allied Market Research, the market size for generative AI exceeded $8 billion in 2021, with a potential to reach $126.5 billion by 2031 at a compound annual growth rate (CAGR) of 32%. Additionally, data from Enterprise Apps Today confirms that 37% of marketing companies are adopting generative AI, with the technology and consulting sectors closely behind at 30 and 35%, respectively.
To provide more context on the investment in generative AI, studies by CB Insights confirm that in 2022, $317 million was distributed to social media and marketing content, followed by $290 million for human-machine interfaces and $253 million for AI enterprise avatars. Ultimately, generative AI will continue to grow exponentially, offering multiple purposes for various industries.
The Advantages
Generative AI enables businesses to develop personalized content, automate their marketing and content generation workflows, and increase creativity. Also, organizations can enhance the quality of their existing data and optimize their customer service.
Personalized Content Development
Generative AI tools offer organizations new possibilities for personalization—many platforms allow users to configure their content settings to personalize the outputs to their niche and target audience.
As a result, generative AI tools will know what kind of content to produce and how to create it based on individual content development needs and goals. For instance, ChatGPT supports plugins, enabling users to change the output language, tone, and writing style to produce content directed at their target audience. Additionally, these technologies can analyze a wide range of customer data (historical and present) to define tailored marketing campaigns and experiences.
Automated Marketing and Content Generation
Generative software automation help businesses reduce the time and expenses spent on content and product creation. Based on a McKinsey report, generative AI can reduce product design and development time by 23 to 38%, with an 8 to 12% cost decrease. Subsequently, companies can rely on automated content to save time and costs while optimizing how they design and develop products.
In addition, organizations can help their teams save time by eliminating human error, automating many of their responsibilities, and giving them access to find answers to complex problems within seconds.
Increased Content Creativity
Another advantage of generative content is increased creativity. While organizations can find it challenging to define unique content ideas, this software takes existing data (images, videos, newsletters, blog posts, reports, etc.), studies the pattern of this data, and produces improved outputs. For instance, product developers can input their historical data to generate new, authentic designs. In fact, in 2022, Cosmopolitan used generative AI to produce a new magazine cover within 20 seconds.
Enhanced Quality
With generative AI tools, users can enhance the quality of existing content by simply prompting the software to refine text, images, or videos. Many tools specialize in improving content quality (in particular, images), such as HitPaw Photo Enhancer. Because of this, this software helps organizations save time—rather than creating high-quality content from scratch, they can enhance existing content within seconds. Additionally, generative technology can help developers optimize and correct code and advance their programming.
Optimized Customer Service
Harnessing generative software enables businesses to deliver customer service much better than traditional AI allows—these tools can automate tedious tasks and create new content in seconds. For instance, the Harvard Business Review conducted a study analyzing 13 tasks typically performed by customer service reps—four tasks could be completely automated, five were partly augmented, and only four had to be entirely executed by humans.
Moreover, the same study confirms that with generative AI, companies can veer away from customer service scripts and deliver personalized experiences based on individual customer emotions, queries, and stages in the buyer’s journey.
The Challenges
Although generative software offers a wide range of advantages, there are setbacks businesses must consider. First, these tools can produce low-quality outputs with many inconsistencies. Second, there are several ethics and security concerns for a technology of this caliber. Next, these solutions can generate discriminatory results based on their input data, and finally, organizations must invest a significant amount of resources to achieve valuable outcomes.
Low-Quality Outputs
A common challenge of generative AI is low-quality outputs—not all tools produce great-quality content, and those that do may be inconsistent. Consequently, users may have to make numerous attempts and possibly manual adjustments before producing their desired results. Furthermore, generative software may also develop inaccurate data, such as flawed code and misinformation, as well as inconsistent or irrelevant details.
Security and Ethics
Generative software raises security and ethical concerns. First, the public fears job displacement, worrying this technology will replace developers, content creators, and data analysts, among other professionals. In addition, data privacy is another pain point in industries such as healthcare and finance, which manage sensitive customer information. Finally, this technology can transform images of people’s faces, meaning criminals can leverage this software for identity theft and similar fraudulent activities.
Discriminatory Results
Generative algorithms produce outputs based on the data they are trained on—if this training data comprises biased and discriminatory data, the final product will reflect this. For instance, you prompt a generative AI tool to provide “the best occupations for women”, and it produces stereotypical results, such as nurses, teachers, midwives, and housekeepers—this is discriminatory and could be offensive to people who consume your content.
However, developers in this industry are leveraging federated learning to solve the bias in AI algorithms. Yet, according to research by ComPas, extensive data is required before federated learning is deemed a valid solution to biased data in generative AI.
Intensive Resources
A fourth challenge of generative AI is the intensive resources and significant cost required to train these algorithms. As confirmed by InfoWorld, AI technology demands optimized storage and network infrastructure and improved processors, which results in higher infrastructure and energy costs. When Meta released its largest LLaMA AI language model, it consumed 2048 Nvidia A100 GPUs, equating to around $2.4 million according to AWS.
In addition, this AI algorithm took 21 days to train on 1.4 trillion tokens (1000 tokens generate 750 words). Training generative AI algorithms are time-consuming, resource-exhausting, and expensive, making it impractical for many businesses.
Top Applications
The main applications of generative AI software include fraud detection, personalized learning, drug discovery, marketing, and chatbots. Here is a detailed overview of how these industries leverage this technology.
Fraud Detection
As the financial sector handles large volumes of sensitive customer data, they have adopted generative AI software to analyze an extensive range of information simultaneously. In turn, financial institutions can detect anomalies and suspicious patterns and prevent and minimize the impact of fraud. In addition, banks can automate their loan origination processes, and configure alerts to avoid possible risks.
Personalized Learning
Generative AI tools significantly benefit the education sector as institutions and platforms can offer students enhanced personalized learning. While traditional education employs a conventional approach for all learners, generative software, such as Speak AI, can analyze each student’s weaknesses, strengths, and learning patterns to deliver tailored learning experiences. As such, this technology can pose several benefits for children with learning disabilities, such as identifying learning gaps and developing alternate study formats based on individual needs.
Drug Discovery
Drug discovery is an imperative, but lengthy and costly, process in the design and development of new treatments, proteins, and antibodies. Fortunately, as experts from McKinsey & Company confirm, drug discovery and development currently take around ten years, but with generative software, scientists can reduce this to one-tenth of the total time.
Additionally, these experts believe generative AI will be able to collect valuable health data from various wearable devices, which they will upload and analyze (on a voluntary basis) to determine the ideal medical solutions for every patient.
Marketing
The global market for generative AI in marketing is rapidly expanding, and it is projected to reach $22 billion by 2032, at a CAGR of 28.6%. Generative software for marketing and advertising is widely used because of the opportunities this technology offers—marketers can automate and develop campaign ideas, competitor research, and data analytics.
Similarly, it is much easier and quicker for marketing subdivisions, such as content writers and email marketers to produce a broad range of advertising material, including email newsletters, blog articles, video scripts, and content strategies.
Chatbots
Chatbots have become a popular solution in every industry, from healthcare to beauty, with an expected market value of $1.2 billion by 2025. In addition, Userlike surveyed 455 respondents, of which 80% of respondents had interacted with a chatbot before. To improve the overall customer experience and support that these bots deliver, generative AI helps make chatbots seem more human-like and personalized to specific keywords mentioned and emotions conveyed in customer communications.
Essentially, this progression in chatbots will help businesses streamline their customer support efforts while improving the accuracy and efficiency of how they communicate with users.
In Closing
Generative AI produces new, improved content (images, text, videos, data simulations, code, etc.) from existing training data. As a result, organizations can personalize their content development, automate marketing and content generation campaigns, and optimize customer service.
However, there are disadvantages to consider, such as low-quality outputs, security and ethics concerns, and businesses requiring intensive resources. Yet, many industries find value in generative software for fraud detection in banks, personalized learning in education, and drug discovery in healthcare, among other applications. Additionally, based on in-depth research by numerous renowned institutions, generative AI will continue to evolve in the coming years.
