The Best Generative AI Use Cases in 2024

Generative AI isn’t just making headlines- it’s also changing our lifestyles, from how we work, interact, and solve problems. From AI lawyers and robot waiters to AI-powered virtual therapy sessions, there seems to be a new AI invention every year. In the words of Marc Benioff, the CEO of Salesforce, “Artificial intelligence and generative AI may be the most important technology of any lifetime.”

Although still in its infancy, generative AI has taken the world by storm, with ever-evolving use cases spanning various industries. Tools like ChatGPT, Midjourney, and Gemini are just the tip of the iceberg, and more use cases will emerge as the technology evolves. Today’s post dives into 10 of the most notable generative AI use cases in 2024. But first, what is generative AI?

Generative AI explained: What you need to know

Generative AI, or Gen AI, is a type of artificial intelligence that learns from existing data to produce new content. The technology mimics human-like intelligence, creating logical and coherent content from scratch.

This content includes text, images, videos, speech, music, and computer code. With gen AI, users can generate new content based on their inputs. For instance, you can prompt ChatGPT to create a personalized workout routine based on your schedule and goals, and it will do just that.

How does generative AI work?

Generative AI identifies patterns in existing data and generates new data based on these patterns. These neural networks consist of interconnected nodes similar to neurons in the human brain. The nodes use input (data) to generate an output (predictions). They also learn from data fed into them to identify patterns and make appropriate decisions.

As such, generative AI centers on predicting the next piece of data in a sequence. This could be a word in a sentence, a musical note, a pixel in an image, or the next line of code in a program. The generative AI process begins by feeding a neural network with copious amounts of data. This data may include:

• Text data from books, articles, social media posts, web pages, and other sources
• Image data from photos, paintings, computer-generated images and the likes
• Audio data taken from music, interviews, sound effects, etc.
• Coding data drawn from code repositories from various coding languages

The data then moves to transformers, which convert the data into vector embeddings (numerical representations of the data). The data can then be classified based on how close the vectors are to each other in a vector space.

For instance, the model might classify a piece of text based on its semantic meaning, like the word “happy” will be classified together with words like “joyful” and “jolly.” Similarly, the model will cluster images based on visual features like color, contrast, and shapes. With audio, it identifies elements such as pitch, rhythm, and timbre to understand the sound’s context and meaning. This classification allows the gen AI to create new, coherent content by combining learned patterns from the training data.

After several computational processes, the data moves to a machine-learning framework, producing a Generative Adversarial Network (GAN). Here, two neural networks called the generator and discriminator work together to produce an output. The generator, as the name implies, produces new data, while the discriminator evaluates the data’s validity. This creates a feedback loop until the generator produces accurate data.

What are the types of generative AI?

Here are some of the most notable types of generative AI:

Generative Adversarial Networks (GANs)

GANs are arguably the most widespread type of generative AI. They’re incredibly effective at producing synthetic text, images, code, and audio files from scratch. Ian Goodfellow, an American computer scientist, invented GANs in 2014[1].

As mentioned, these AIs put two models against each other. One is the generator, which produces content. The other is the discriminator, which validates the generator’s content. The discriminator returns invalid content to the generator so it can try again. The process continues until the generator produces accurate content closest to the “real thing.” GANs are the gen AIs behind popular AI models like ChatGPT and Gemini.

Autoregressive models

Autoregressive models are similar to GANs but without the feedback loop. Instead, these Gen AIs use data from the past to predict future trends and patterns. They’re based on the notion that what happened before will happen again. This generative AI uses a co-efficient of variation to establish relationships between previous data values and forecast the output.

Variational Autoencoders (VAEs)

Variation Autoencoders also use past data to generate new data. Like GANs, they consist of two types of neural networks: the encoder and the decoder. The encoder learns to isolate important latent variables from the training data. The decoder then learns probabilistic distributions of the encoded data and generates new samples by sampling various points of the learned distribution. These gen AIs are extremely useful for image-generating AI models like MidJourney.

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are transformer-based generative AIs that process data in sequences and produce output of similar data in a specific sequence. The data in sequence can be in the form of sentences, a time series, video frames, and even DNA sequences. Like VAEs, they work by predicting the next data in a sequence from historical data. However, unlike VAEs, RNNs use data sequences, while VAEs learn latent data representations and generate new samples.

Hybrid models

Hybrid models combine two or more content generation systems into a single generative AI. For instance, AI engineers can combine VAE and GAN models. The VAE system will use the encoder algorithm to compress the data (usually images) into a latent representation. The GAN, on the other hand, will use the generator to create new data from the latent space and send it to the discriminator to check the “realness” of the data until it can no longer distinguish the output from real data.

Why is generative AI suddenly so popular?

Contrary to common belief, generative AI has been around for decades. In fact, generative AI was invented in the 1960s and was used in chatbots [2]. However, it wasn’t until late 2022, with the introduction of ChatGPT, that the technology gained widespread popularity and recognition. Two years later, AI is almost used everywhere.

Nowadays, most electronics manufacturers and software developers slap the “AI” label on their products. This begs the question, why has AI suddenly become so popular? The recent surge in generative AI’s popularity can be attributed to a couple of factors.

• Technological advancements: Recent advancements in machine learning and data science have contributed significantly to today’s generative AI boom. Technologies like deep learning, cloud computing, and Tensor Processing Units (TPU) have contributed considerably to generative AI’s popularity surge by making it more powerful and accessible to the general public.
• Increased computing power: Chip manufacturers like Nvidia, Intel, and AMD have pulled out all the stops to create specialized hardware to support heavy AI workloads. This increased computational power allows generative AI models to process vast data sets and generate sophisticated outputs. Furthermore, parallel computing, introduced in the 1950s [3], has been subject to major improvements that have accelerated the training of complex neural networks.
• The Internet: The World Wide Web is the chief catalyst in generative AI’s popularity surge. Blogs, social media platforms, and online news outlets have been at the forefront of spreading the AI gospel ever since ChaptGPT went mainstream. As the larger public became more aware of the technology, software developers hopped on the AI bandwagon, creating their own versions of generative AI tools to capitalize on the growing market.
• Business adoption: It’s no secret that AI technology holds immense potential for large and small businesses. According to Forbes, about 97% of businesses believe that generative AI, like ChatGPT, will help their business [4]. The technology offers increased efficiency, better personalization, and enhanced decision-making, making it a must-have for companies in the modern age.

Generative AI: Key benefits and real-world applications

Generative AI is taking the world by storm, with profound benefits for individuals, businesses, institutions, and entire industries. Some of the most notable benefits of gen AI include:

Automated content creation

Content creation is at the heart of every successful digital marketing campaign today. Generative AI allows businesses to automatically create content for their marketing endeavors in seconds.

The content in question can include anything from blog posts, brochures, newsletters, product descriptions, and even email campaigns. This lessens the workload for marketing teams so they can focus on strategic and creative initiatives rather than churning out repetitive content.

Enhanced personalization for improved customer experience

Did you know that 73% of customers prefer tailored experiences over generic interactions?[5]. However, creating a custom experience for every customer is time-consuming and incredibly challenging.

Generative AI helps create highly tailored experiences based on customers’ behaviors and preferences with little to no human intervention. For instance, businesses can use recommendation engines to tailor recommendations to user preferences and buying habits. The result is increased sales and improved customer satisfaction.

Product design optimizations

Product design is one of the most complex processes in the product development cycle. Shifting trends in customer preferences make it difficult to pin down the best design for maximum customer satisfaction and market success.

Companies are using gen AI to analyze vast amounts of data and identify prevailing customer tastes and product preferences. That way, companies can invest in manufacturing products that appeal to their target audience and convert them into actual sales.

Process automation

Companies are using gen AI to automate redundant and mundane tasks, streamlining processes across the board. This not only reduces the time to delivery but also reduces your employees’ workloads so that they can focus on more high-value tasks.

For instance, businesses can use generative AI for data entry and report generation. They can also use this technology to summarize reports, reviews, and other business documents. Using generative AI, business owners can draw relevant conclusions from these documents without reading the extensive paperwork. They can then gain crucial insights into their businesses and make well-informed decisions.

Cost reductions

Generative AI can help reduce the overall cost of running businesses and institutions by plugging cash leaks and reducing inefficiencies in standard operations. Thanks to AI, institutions, and companies can allocate resources to critical areas and reduce resource allocation to non-essential functions. Gen AI can also reduce the time it takes employees to accomplish tasks by automating repetitive tasks and streamlining workflows.

Practical applications of generative AI in today’s world

Using generative AI has spread quickly, and many institutions and businesses are quickly adopting this technology to help them achieve their bottom lines. As technology advances, so does the number of generative AI use cases increase. Some of the most notable applications of this technology today include:

Sales and marketing

One of the most widespread generative AI use cases is in sales and marketing. Today’s competitive business landscape means businesses must think outside the box to get an edge. Forward-thinking companies use gen AI to personalize communication with their target audience across multiple channels. This means personalized SMSs, emails, and social media messages.

Personalized communication makes it easier to create marketing campaigns that leave a lasting impression on the recipients, increasing the likelihood of engagement and conversion. This is a much better approach to marketing than broadcasting the same email or SMS to your entire audience.

The sales department can also use the same technology to get crucial insights into market trends and consumer behavior. That way, they can create products and marketing material that appeal to their customer base and target audience. This allows them to truly connect with their audience so they can not only attract but also retain new customers.

Companies can also utilize generative artificial intelligence in audience targeting and segmentation. They can leverage AI to zero in on high-quality leads that are more likely to convert into paying customers. They can then allocate their time and resources to these leads and improve the effectiveness of their marketing campaigns.

Customer support and service

Artificial intelligence has helped bridge the gap between businesses and their customers. According to Zendesk, 75% of customers will spend more on businesses with good customer service [5]. Gen AI has particularly played a big role in improving customer service in businesses and organizations.

Gone are the days when chatbots gave generic responses to customer queries and complaints. These days, AI-powered chatbots give comprehensive, high-quality responses that provide actual value to the user. These chatbots can engage in natural, flowing conversations and provide images, charts, and real-time data to supplement the interaction.

Thanks to advancements in AI technology, AI chatbots can now understand context and pick up nuances in tone to gauge emotion. These chatbots can also analyze huge data stores to give appropriate responses based on factors like the customer’s age, gender, demographic, and location. This allows companies to provide top-notch, round-the-clock customer service regardless of geographical location.

Code generation

Writing code is a complex and time-consuming process that can sometimes drag on for months. GenAI has given software developers and programmers a shot in the arm by automating code generation. These computer technicians can use AI to complete complex coding tasks that normally take hours in just a couple of minutes.

They can also use AI to quickly identify and fix bugs in their codes without the need for extensive testing. That way, they can ensure the code achieves its intended purpose and meets the required quality standards. This technology can also help coders fast-track the production of technical documentation for their software and programs.

Product design and development

One of the hardest parts of bringing a new product to the market is conceptualizing the product itself. Companies invest millions in product development only for the products to flop once launched. Gen AI allows companies to streamline their product development, especially during design.

Design teams can use AI to create designs at scale and optimize the best ones to have the most impact once launched. They can use AI to conduct rapid evaluations and automatic adjustments so the product not only looks good but also meets the structural requirements to fulfill its functions.

Wrapping up

Generative AI has a far-reaching impact in the modern world, with use cases in almost every industry. Early adopters are already reaping the fruits of this cutting-edge technology with improved efficiency, streamlined processes, and better personalization. However, it’s worth noting that we’re still in the early stages of gen AI development and have only gotten a taste of its true potential. We might see even more generative AI use cases in the coming decade.

It’s safe to say that the future looks bright for generative artificial intelligence, and we can only wait excitedly as gen AI innovations continue to unfold and reshape our world.

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[1] deeplearning.ai, Ian Goodfellow: A Man, A Plan, A GAN, https://www.deeplearning.ai/the-batch/ian-goodfellow-a-man-a-plan-a-gan/, Accessed on September 24, 2024
[2] techtarget.com, What is generative AI? Everything You Need to Know, https://www.techtarget.com/searchenterpriseai/definition/generative-AI, Accessed on September 24, 2024
[3] intel.com, Parallel Computing: Background,https://www.intel.com/pressroom/kits/upcrc/ParallelComputing_backgrounder.pdf, Accessed on September 24, 2024
[4] sender.net, 55+ Personalization Statistics & Facts for 2024, https://www.sender.net/blog/personalization-statistics/#:~:text=71%25%20of%20customers%20prefer%20tailored,employ%20to%20boost%20engagement%20rates., Accessed on September 25, 2024
[5] zendesk.com, 51 customer service statistics you need to know, https://www.zendesk.com/blog/customer-service-statistics/, Accessed on September 25, 2024