Generative AI Landscape: Trends of 2024 and Beyond

Generative AI is a form of artificial intelligence that relies on natural language processing, massive training datasets, and advanced AI training strategies, like neural networks and deep learning, to generate original content that is similar to that produced by a human.
Tech professionals and recreational users alike are becoming familiar with content generation models like ChatGPT, which first emerged in 2022. But this example of generative AI only skims the surface of what generative AI can do and where it’s heading.
In this guide to the generative AI landscape, we’ll explore what generative AI is capable of and how it emerged and became so popular. We’ll also examine current trends in the generative AI space and predict what consumers should expect from this technology in the near future.

TABLE OF CONTENTS
ToggleCurrent State of the Generative AI Landscape5 Emerging Trends in the Generative AI LandscapeGenerative AI Landscape: Cybersecurity ImplicationsFuture Directions of the Generative AI LandscapeLeveraging the Generative AI Landscape for InnovationBottom Line: The Future of the Generative AI Landscape

Current State of the Generative AI Landscape
Though generative AI technology is still relatively new, current AI models are being used to fulfill a range of personal and business use cases. These are some of the most common applications and AI examples in the generative AI landscape today:

Personal use: Recreational users of generative AI tools frequently use them for textual content generation; specific use cases include Q&A, trip and event planning, conversation, and research.
AI assistants, chatbots, and search: AI-powered search engines and copilots are quickly gaining steam. Several startups also offer enterprise search tools that are powered by generative AI.
Developer tasks: Generative AI developer tools can predict code sequences, support problem-solving, document existing code, and automate programming tasks.
Creative content generation: Beyond text generation, several generative AI models can also generate audio, video, and imagery. Some of the latest trends in AI content generation include music, videogame, and podcast content creation.
Computer vision: Synthetic data generation and 3D model creation can be applied to computer vision use cases like driver monitoring and pedestrian detection, AR/VR/XR, virtual try-on, and more.
Voice and audio synthesis: AI-generated voices can be used for enterprise projects like marketing videos; AI voice options are quickly becoming available in a wide range of languages and tones.

These examples and more are in use across a variety of industries and business departments today, including marketing and sales, customer service and contact centers, healthcare and pharmaceuticals, biology and biophysics, entertainment, legal and government, finance and banking, and e-commerce.
Tools like ChatGPT can be used for different types of content generation, including code snippet generation. Source: Shelby Hiter via ChatGPT.
For a deeper understanding of how generative AI is used by companies, see our guide: 15 Generative AI Enterprise Use Cases.
Key Technologies and Innovations Driving the Generative AI Landscape
Generative AI is emerging on a massive scale right now for two primary reasons: 1) More mature neural networking techniques have materialized, and 2) Models and compute power are now more accessible.
In 2017, Google laid the foundation for the generative AI we use today when the company first proposed a neural network architecture called the Transformer. With transformers, it became possible to create higher-quality language models that could be trained more efficiently and with more customizable features. At this time, tools with predictive text and simple AI chatbots began to emerge and mature sparsely.
However, even with the development of transformers and related neural networking architecture, generative AI models remained prohibitively expensive and difficult to develop and operate. Processing generative AI queries required power resources that most companies did not have.
Starting in 2022, compute power and the AI platform infrastructure layer began catching up to processing requirements for generative AI tools, making it possible for more companies to develop generative AI technologies. And more importantly, this innovation made it so existing generative AI developers could extend their models to other users at an affordable rate.
Around the same time, new neural networking techniques, such as diffusion models, also arrived to lower the barriers to entry for generative AI development.
With generative AI now requiring less energy and financial investment, the generative AI landscape has expanded to include a number of established tech companies and generative AI startups. The landscape continues to evolve as existing models are extending to more users through APIs, limited free versions, and open-source software, leading to new applications and use case developments on a regular basis.
To learn more about emerging leaders in the generative AI landscape, see our guide: Generative AI Startups.
Major Players and Startups Shaping the Generative AI Landscape
OpenAI is the clear leader in the generative AI landscape, currently valued at around $80 billion. With its close relationship with Microsoft, its GPT-4 model, its incredibly popular ChatGPT tool, and its ongoing innovations in other forms of multimodal content generation, OpenAI is poised for continued growth and third-party investments to fuel that growth.
Major tech companies like Amazon and IBM are also driving the generative AI scene, but the most prominent of the big players right now are Microsoft and Google. Though Microsoft got an earlier start on actual generative AI tools that are available to the public, both companies now offer tools to support multimodal content generation, AI-driven chat, AI assistance, and more.
Other leaders in this space include:

Cohere
Jasper
Anthropic
Glean
Stability AI
Inflection AI

5 Emerging Trends in the Generative AI Landscape
As influential as generative AI has quickly become, early adoption rates suggest a far more all-encompassing future that affects various sectors, from education to virtual reality. Learn more about today’s emerging trends that will likely play a role in how generative AI is used down the road.
Marketing and Sales Automation
Generative AI is frequently used for marketing, sales, and similar creative content generation tasks at this time. While users can manually input their content requests into generative AI chatbots and models, many AI vendors now make it possible for users to do minimal prompting with an AI assistant or copilot to automate content generation tasks.
Automation can be used for social media and blog content writing, multichannel video and image content creation, and inbound and outbound email marketing workflows, for example.
Many of these tools are currently limited as far as what languages they work in and the context windows they support. As this use case matures, expect to see more multilingual solutions with larger context windows, so longer and more complex queries can be posed.
Support for Product and Software Development
Whether you’re an experienced developer or a novice with no coding knowledge, several generative AI tools now can assist with different programming tasks. Take GitHub Copilot, for example—this tool works directly with users’ GitHub accounts and ecosystems, assisting them with code completion, code snippets, troubleshooting, and plain-language code generation and explanations. With the emphasis on natural language inputs, anyone can generate code to solve a variety of product and software development problems.
Increasing Educational Use and Impact
Students are already using tools like ChatGPT to answer homework questions or write essays, and teachers and parents are concerned.
Although these large language models don’t necessarily “know” the answers to educational assignments, their training has prepared them to accurately predict a sequence of text for various inputs, making it possible for students to use these tools to solve classroom problems. Of course, this could negatively impact students’ education, but it could also benefit students and their teachers if education systems learn how to implement AI solutions as assistive learning tools.
Similarly to when classroom technologies have changed in the past — overhead projectors, anyone? — generative AI will require teachers to evolve their educational approach. For instance, virtual learning is an exciting area of generative AI that is quickly evolving. Generative AI games and AI storytelling solutions are being released now, offering teachers instructional support and engaging new ways to deliver educational content to students.
But there’s still the issue of AI-based plagiarism. To combat students’ tendency to rely on ChatGPT and similar tools to do their homework, teachers can use one of the many free AI content plagiarism detectors that have now been released. Though they’re not perfect, these tools can effectively estimate what percentage of content has been artificially generated.
As educational concerns grow, users can expect these plagiarism checker tools to evolve too.
Embedded AI Applications
A handful of big tech companies like Microsoft now offer AI assistants that guide user search experiences on the web or support content generation and task completion in office suite solutions like Microsoft 365. Google has followed suit with Gemini, adding capabilities so the tool can be used directly in Gmail, Docs, and more. Additionally, some of the biggest generative AI startups, such as Cohere and Glean, provide AI-powered enterprise search tools to users.
As assistive enterprise tools continue to expand their features and functions, many companies will begin to follow Microsoft’s lead, just as Google has, and embed these applications into their websites, products, and internal software to create a better self-service user experience for employees and customers alike.
Microsoft Copilot is available in several of Microsoft’s business applications now, including Power BI. Source: Microsoft.
Contextualized, Global Generative AI
The majority of today’s generative AI models have time-based and linguistic limitations, but several generative AI vendors have expanded their tools to work in more languages and dialects. As generative AI grows in demand around the world, more and more of these vendors will need to make sure their tools can accept inputs and create outputs that align with various linguistic and cultural contexts with minimal errors.
Some generative AI models, including BLOOM from Hugging Face, are available in less common global languages and dialects. Users should be aware that many of these languages have been part of minimal training data, so it will be important to quality-check any outputs in non-English languages. Source: Hugging Face.
Additionally, generative AI models will need to offer more accurate, real-time information to users to keep them engaged. Though ChatGPT is currently the most popular content generation and large language model available, it may eventually fall behind competitors like Gemini, which has a free version that’s connected to the internet and generates answers based on up-to-date information. In contrast, ChatGPT’s free version currently works with data that stops in April 2023 and has no real-time internet connection, though paid plans have access to Bing.
Generative AI Landscape: Cybersecurity Implications
Generative AI tools can be used for simulated attacks and environments, threat intelligence, and synthetic data digital twins of sensitive data. While generative AI supports these greater security protections and practices when it is used strategically, it can also be manipulated by malicious actors and hackers who know the secrets and workarounds for how generative AI models are trained and how data is used.
A growing number of generative AI vendors are advancing their cybersecurity approach to the tools they offer to the public, but still, many of these generative AI solutions do not and will not have all of the cybersecurity protections necessary for businesses in highly regulated industries or dealing with sensitive data.
Assuming generative AI tools have the right cybersecurity protections for your business can lead to all sorts of problems, including stolen intellectual property or private data, loss of consumer trust, and legal and compliance issues.
Whether you’re using another vendor’s AI model or working to develop your own, these are a few of the best practices you can follow to protect your data and users:

Familiarize yourself with AI vendor data usage and storage policies.
Don’t train with or input sensitive data.
Train employees on how to use these tools and company data appropriately.
Develop an AI governance policy.
Use data governance and cybersecurity tools that cover the modern threat landscape, including third-party AI applications.

To learn about the intersection of AI and generative AI, see our guide: Generative AI and Cybersecurity: Ultimate Guide.
Future Directions of the Generative AI Landscape
Based on current developments, priorities, and the public’s interest, these are a few of the areas where we expect to see new growth and opportunities in the future of the generative AI landscape:
Generative AI and Virtual Reality
Video and 3D models are some of the fastest-growing generative AI model formats today. This growth is especially evident in AI video content marketing, which makes use of avatars, audio synthesis, and other generative AI features to create compelling marketing content at scale.
Marketing creative content, games, and entertainment media can certainly benefit from this advancement in AI, but the impact generative models will have on virtual reality (VR) and augmented reality (AR) technology — the metaverse — is what many people are most anxiously awaiting.
As models are refined to the point where they can process more data, create higher-resolution media, and accept longer context windows, expect generative AI technology to create immersive experiences that make virtual reality feel real.
Shifting Career Paths and Opportunities
Generative AI tools are already supplementing certain types of work and, in the future, may come to replace certain kinds of work. But this shouldn’t raise alarms for the average working professional, so long as they’re willing to pivot and build on their skills as job expectations change.
For example, many writers currently focus on SEO writing, a form of writing that mostly involves crafting content that ranks well in search results. This is exactly the type of content generative AI models can produce through their algorithmic training. For a writer concerned about their job security as large language models become more capable, it will be important to develop new strategic skills, like editorial planning and content quality assurance management, and to work with companies that value human creativity and research.
On the plus side, these tools are already making certain types of repetitive tasks easier, like sending emails, drafting spreadsheets, documenting action items, and taking notes. Assistive workplace capabilities will likely extend to more complex tasks in the future, like brainstorming entire product launch plans and handling HR onboarding from start to finish.
When employees have time freed up as these time-consuming tasks are taken off their plates, they’ll be able to focus on higher-value strategic work more than ever before.
Specialized Industry Apps and Tools
Though many specialized industry apps and tools have already been released, several industries have so many complexities and product release requirements that make it tricky to release these tools quickly. As a result, some of these industries are lagging behind others for AI usage.
The AI healthcare space is a great example. This area has a lot of energy and excitement regarding generative AI, but at this point, there’s been considerable churn and stagnation for generative AI product releases. This could be for multiple reasons, but it’s safe to assume that the highly regulated PHI and PII data, as well as industry-specific patents (i.e. drug patents) involved, make it more difficult to jump through all the hoops and move forward.
In future years, as these tools become more established and AI vendors increase their transparency and explainability, we should see more growth in regulated and complex industries, including healthcare/patient relations, pharmaceuticals and drug discovery, insurance, and finance and banking.
Increased Regulation and Ethical Implications
In most regions and industries, AI usage remains largely unregulated, which can lead to a range of issues. Some users have already suffered consequences from their personal data becoming part of a model’s training data and potential outputs, while others have raised alarms about data storage and related security protocols in these solutions. While some AI vendors have independently chosen to make their training processes, data collection methods, and overall strategy more transparent, there’s little in the way of governing bodies to enforce this transparency.
Expect this to change very soon, especially in the European Union. The EU AI Act, a comprehensive AI law that regulates how AI vendors can use AI-powered automation and collect personal data while requiring certain types of disclosures and transparency, reached a provisional agreement between Parliament and Council on December 9, 2023. Formal adoption is expected to follow in 2024 after internal market and civil liberties committees have voted on the law.
The EU AI Act emphasizes the importance of minimizing AI risk in several key areas. Source: European Commission.
Other countries, including South Korea, Singapore, China, and the United States either have proposed AI regulation frameworks or piecemeal AI regulations in the works, all of which will likely become higher priorities as generative AI tools continue to increase their capabilities and grow in public demand.
With this expected increase in legislation and public awareness of the dangers of generative AI, plan to see more generative AI vendors not only do what’s required to comply with these laws but also take public-relations steps to further explain the ethical and responsible AI steps they are taking.
Leveraging the Generative AI Landscape for Innovation
The generative AI landscape is ripe for new innovation and opportunity, whether you’re an AI developer, a business user, or a casual user. To get the most out of these tools and the technology landscape as a whole, we recommend the following strategies:

Work with established or niche AI vendors: It’s a good idea to partner with AI and AIaaS vendors that have experience with your particular industry or use case. While the biggest vendors we covered above may be a good fit, several others specialize in enterprise-specific generative AI app development, including C3.ai.
Experiment with fine-tuning models: Fine-tuning models allow users to work with a baseline model’s advanced capabilities and customize them to a more specific function. Depending on your budget and internal tech expertise, an open-source model may offer the best value for fine-tuning projects.
Work toward multimodal inputs and outputs: Users are increasingly going to AI content generation tools for text, code, image, and other types of content creation. If you’re developing an AI tool for internal or public use, work on creating an interface and training approach that lends itself to this multimodality.
Prioritize multilingual capabilities: Global democratization of generative AI is an important next step in the generative AI landscape’s maturation. Though multilingual training can be difficult, several open-source models offer baseline training and support for many languages and dialects now.
Integrate with relevant business applications: Whether it’s through APIs or prebuilt integrations, pay attention to the integrations users want and ensure the models you develop work seamlessly with those tools.
Accept feedback and retrain: Similar to what Google has added in Gemini, consider incorporating user feedback mechanisms into whatever tools you develop, allowing users to score responses, request new content generation based on specific parameters, and more. Reviewing this feedback data will give your team the ability to retrain models for better outcomes in the future.
Emphasize transparency and explainability: Transparency and explainability are good practices to follow not only to keep up with AI regulations but also to keep users feeling confident and assured as they use their tools. Providing detailed policies, user guides, and training is the best way forward here.

Bottom Line: The Future of the Generative AI Landscape
Bursting more publicly onto the tech scene in late 2022 with the arrival of ChatGPT, within months, generative AI quickly began to radically reshape the tech sector. In fact, it’s no exaggeration to say that the “generative AI landscape” and the “overall tech landscape” are essentially merging into a single entity, as generative AI technologies find their way into a growing list of tech tools and solutions.
To best handle current struggles and prepare for future challenges that will accompany generative AI innovation, business leaders need to pay close attention to the market, how different AI vendors approach transparency and explainability, and what security and privacy protections are and aren’t working for this kind of technology. Preparing in this way will help generative AI to grow in a way that does more good than harm.
To learn more about today’s leaders in the generative AI sector, see our guide: Top Generative AI Companies.
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