Top 6 Current & Future Generative AI Trends & 2025 Predictions

Trends in Generative AI

Artificial Intelligence (AI) has been around for quite some time, but its recent growth has been nothing short of remarkable. Among its many variants, Generative AI has emerged as one of the most significant.

But what exactly is Generative AI? Generative AI is a type of AI focused on creating various types of content. It generates original outputs by drawing upon patterns and insights learned from the analysis of similar existing content.

Examples of Generative AI content are: 

• Text (copywriting, blog posts, e-mails, website content, cover letters, articles, etc.)
• Images (hyper-realistic images, photos, infographics, charts, logo, CV, etc.)
• Speech (voiceovers, audiobooks - in multiple languages and voices)
• Video (realistic videos, advertising materials, tutorials, etc.).

State of Generative AI Today

Generative AI is one of the fastest-developing types of AI that already achieved wide adoption across industries. 

A boom for Generative AI started in late 2022, right after the launch of Chat GPT. Only five days later, the Open AI’s tool had already attracted over one million users. The search ranks also started to grow rapidly - at the beginning of February 2023, the phrase “generative artificial intelligence” reached 100 index points. Shortly after, the next tech giants started to follow, with Google’s Bard as the iconic example.

Now, let’s take a look at some statistics revealing the current state of development of the Generative AI landscape:

• The Generative AI market size is projected to reach $44.89 billion in 2023. By 2030, the value will reach $207 billion, with an annual growth rate of 24.4%.
  

• The most impressive growth in CAGR can be observed in Asia-Pacific, especially in China and India, where the AI adoption rate has already reached 60%.
• By 2025, 30% of outbound messages sent by large organizations will be synthesized, starting from 2% in 2022. 
• By 2025, generative AI will account for 10% of all produced data, with less than 1% in 2022. 
• 67% of IT leaders prioritize using generative AI within the next 18 months, while it became a top priority for 33%.
  

Top 6 Generative AI Trends

So what are the top forecasted trends for 2024? Let’s see the top 6 predictions that may revolutionize the Generative AI market landscape in the upcoming year.

Robotic Process Automation

One of the primary goals of Generative AI is to replace repetitive tasks. And this is already happening - AI is getting incorporated into robots and automation systems as a part of intelligent automation tools. 

Thanks to Generative AI, RPA technology can mimic human actions such as clicking and typing more naturally and accurately, or even complete more creative and complex actions. This can lead to an increasing adoption of such tools across industries, including healthcare, finance, manufacturing, logistics, and more.

Examples:

• UiPath: a business automation offering great possibilities for integrations.
• Automation Anywhere: a cloud-native RPA platform offering intelligent automation.  
• SS&C Blue Prism: the tool utilizing cognitive technologies and Natural Language Processing in enterprise automation.

AR and VR 

AR and VR are already strong trends themselves but combined with AI, their potential becomes even more appealing. AI can be applied in VR to generate more realistic environments, create avatars that reflect human behaviors, or be used to achieve more personalized virtual experiences. AR and VR tools powered by Generative AI can be applied in education, entertainment, marketing, and more. 

Examples:

• Roblox: a platform for user-generated, immersive 3D images already used by nearly 60 million users daily. 
• NVIDIA: the company that incorporated Chat GPT into its system to enable its users to build 3D models and create 3D environments.
• Blockade Labs Skybox: the company that enables the creation of 360-degree worlds based on text inputs and prompts.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a term related to AI used for understanding and processing human language. This includes speech recognition, text analysis, translations, text generation in various languages, and more, and is utilized in applications supporting human interactions and communication - such as chatbots and voice assistants. NLP has been already adopted widely across industries, including sales and marketing, customer service, finances, IT, and HR.  

Examples:

• Monkeylearn: a no-code Generative AI tool for text analysis.
• AI Writer 2.0: a tool for creating engaging, SEO-friendly content.
• TLDR This: summarizer of articles, online texts, and documents.

Autonomous Vehicles and Intelligent Transport Systems

The field of autonomous driving is the next area of high-potential generative AI adoption. It is predicted that generative AI in the automotive industry will reach $2.1 billion by 2032. Generative AI can be applied mostly in building real-life scenarios for training purposes of both drivers and autonomous vehicles.

Examples:

• Waze: the tool delivering personalized route recommendations in real-time based on user preferences to improve road safety.
  

Generative AI in Healthcare

Some industries are seeing rapid AI growth, and healthcare is a prime example. Generative AI is making significant changes here, creating opportunities in areas like drug development, personalized treatment plans, patient monitoring, generating medical reports, telemedicine, and more.

Examples:

• DirectSchifts: platform matching clinicians with job postings.
• Ouva: a platform monitoring patient behavior and alerting about risks.

To learn more examples of the Generative AI landscape in healthcare, go to our article: Generative AI in Healthcare: Use Cases & Upcoming Solutions.

Generative AI in education

AI offers new opportunities for both teachers and students, changing the way we teach and absorb knowledge. Its impact reaches far beyond writing essays; AI can create personalized learning experiences and provide instruction without the involvement of a human teacher.

Examples: 

• Character.ai: a chatbox allowing chatting with AI-generated characters and historical figures.
• Prof Jim: an application that scans textbooks and Wikipedia to create immersive online lessons.
• Numerade: an application that creates personalized study plans by analyzing learning styles, strengths, weaknesses, and preferences.
  

Generative AI in E-commerce 

The retail and e-commerce sector is the next one set to benefit from Generative AI adoption. It is predicted that the market for AI in retail will grow at a CAGR of more than 30% through 2028. The technology can be applied in various ways, such as creating content, automating and personalizing customer communication, visual merchandising, designing in-store displays, and others.

Examples:

• Shopify Magic: the tool that writes e-commerce product descriptions.
• Stylitics: an app creating outfit combinations and recommendations.
  

Get ready for 2024: Generative AI Market Landscape

So, how can you prepare for the 2024 opportunities and challenges? Let’s talk about risks and opportunities associated with trends in Generative AI.

Risks of Generative AI

As AI adoption continues to rise, there are more and more critical voices expressing concerns and discussing potential dangers. The risks are associated mostly with ethics, regulatory compliance and liability, cybersecurity, and poor data quality.

The reasonable doubts are raised by IT decision-makers themselves:

• 71% of respondents are afraid that generative AI will introduce new security threats to their data
• 66% believe that their employees don’t have sufficient skills to use AI tools 
• 59% raises the concern about the lack of a unified data strategy. 

In fact, despite its widespread popularity, many consumers also remain skeptical about AI:

• 70% of non-users claim that they would use AI if they knew more about the technology
• 64% wish AI to be more secure
• 72% of consumers say it is important for them to be aware of the company’s AI policy before making a purchase. 

Moreover, nearly two-thirds of people in the US want the implementation of AI regulations in the near future. This trend raises questions about the uncertain future of AI development and its potential for unrestricted use. The directions it might take are challenging to predict.

Opportunities for Generative AI

Despite the obvious risks, Generative AI can still make a significant contribution to the advancement of various industries and holds substantial business potential. Let's take a look at some industry data:

• By now, only 6 companies in the generative AI area managed to reach unicorn status, meaning they are worth more than $1 billion (with OpenAI, Hugging Face, and Jasper as examples).
• The Generative AI field has already received $1.7 billion of VC funding in the past three years.
• 33% of more than 250 Generative AI companies have not yet received funding from outside sources. 

This obviously means that the Generative AI field is still young and has a lot of room for innovation. Investors keen on exploring these opportunities, however, face the main challenge of finding ways to overcome or reduce the risks and be able to satisfy their audiences. 

Trends in Generative AI: The Summary

Despite reasonable doubts, it’s too late to stop the AI revolution. Salesforce’s latest State of IT report found that 86% of IT leaders expect generative AI to soon play a prominent role in their organizations and that 57% of them believe that Generative AI is a “game changer.” 

How you can make the most of this opportunity? Keep an eye on the top Generative AI trends, and ensure the feasibility of your idea before proceeding to development. If you need the advice or the support of an experienced partner, don't hesitate to reach out to us.

If you’re considering bringing a Generative AI product to life or integrating AI into your existing business operations, check out our curated list of top AI software development companies. Each one has a track record of delivering high-impact AI solutions across industries — from prototypes to full-scale platforms.