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Enterprise AI

Generative AI Applications Landscape

Jagreet Kaur | 02 October 2023

Global Generative AI trends

The market for generative AI is expected to experience significant growth, from $43.87 billion in 2023 to $667.96 billion by 2030. This represents a compound annual growth rate (CAGR) of 47.5% during the forecast period.

Furthermore, it is expected that by 2026, over 80% of enterprises will have used generative artificial intelligence application programming interfaces (APIs) or models and deployed GenAI-enabled applications in production environments. This is a drastic increase from the less than 5% recorded in 2023.   
Unlike analytical and traditional AI-based conversational interfaces, which are limited to pre-defined commands, conversational AI learns, understands, and plans chat responses based on context and purpose. Generative model-driven virtual assistants, including NLP, chatbots, deep learning, NLG, and LLM, enable chatbots, intelligent virtual assistants, and other conversational interfaces to interact with users in a human-like way. This way, conversational AI overcomes the challenge of substandard responses resulting from limited comprehension of user queries.

Unleash the Potential of Generative AI in Data Visualization

Transformative Trends in the Generative AI Market

In recent years, the generative artificial intelligence market has seen remarkable growth and transformation. One major trend in the market has been an increased focus on improving user experiences through generative AI applications. These applications have been efficient in gaming, entertainment, and design.  
The demand for generative artificial intelligence applications is growing across industries due in part to the expansion of technologies such as super-resolution, text-to-image conversion, and text-to-video conversion, as well as the need to modernize workflow processes across firms. The need for AI-driven solutions to extract meaningful insights from this data has become more pressing. Generative AI algorithms have proven highly effective in analyzing complex datasets, identifying patterns, and generating valuable predictions.

Top Generative AI Use Cases and Applications of Generative AI   
Advanced generative models, including Deep Convolutional GANs (DCGANs) and StyleGANs, have significantly impacted the market, generating high-quality and realistic images and videos. This has had implications for industries such as entertainment, gaming, and visual content creation. Generative AI is also increasingly used for automated content creation and curation, benefiting domains such as social media, marketing, and journalism, where AI-generated content can streamline processes and improve content relevance and engagement.   
Finally, artificial intelligence (AI) and data analytics significantly shape the generative AI market. For example, using AI can facilitate the early identification of potential malignancies, leading to more effective treatment plans. In addition, the growing demand for AI-powered chatbots to enable effective conversations and boost customer satisfaction is another significant growth-inducing factor for the market.

Generative AI Solutions
Accelerate your AI initiatives with Large-Scale Generative AI Models to reinvent customer experiences and applications. Transform and Innovate with Generative AI

Key Drivers of Generative AI Value Chain

As generative AI systems are developed and deployed, a new value chain is emerging to support their training and use.


1. Computer hardware

Generative AI systems require massive amounts of data to create content, which traditional computer hardware cannot handle. Large clusters of GPUs or TPUs with specialised accelerator chips are needed to process all the data across billions of parameters in parallel. NVIDIA and Google dominate the chip design market, and TSMC produces almost all accelerator chips. New players face high start-up costs for research and development, and traditional hardware designers must develop specialized skills, knowledge, and computational capabilities to serve the generative AI market.

2. Cloud platforms

GPUs and TPUs are costly and scarce, making it impractical for most businesses to possess and maintain this vital hardware infrastructure on-premises. As a result, most of the work to create, fine-tune, and operate large AI models occurs in the cloud. This allows companies to access computational power and manage their expenses as needed quickly. The major cloud providers have the most comprehensive platforms for running generative AI workloads and exclusive access to the hardware and chips. Specialised cloud challengers will likely gain market share shortly with the support of a large enterprise looking to reduce its reliance on hyperscales.

3. Foundation models

Foundation models are the backbone of generative AI. They are large deep-learning models pre-trained for creating specific types of content and can be modified for various tasks. These models are trained on massive data sets, including public data scraped from different sources and private data from large databases. Developing foundation models requires expertise in several areas, such as data preparation, model architecture selection, training, and tuning. However, the training cost for foundation models is currently high, which limits the market to a few tech giants and start-ups backed by significant investment. Nevertheless, there is ongoing work to make smaller models to deliver effective results for some tasks and more efficient training, which could open the market to more entrants.

Delving into the Depths of Foundation Models

4. Model Hubs and MLOps

Businesses looking to build applications on top of foundation models require a place to store and access the model and specialized MLOps tools and technologies to adapt and deploy the model within their end-user applications. Model hubs offer these services, serving as an access point for closed-source models and providing a spectrum of services for open-source models. These hubs can include model aggregators or end-to-end MLOps capabilities with the expertise to fine-tune and deploy foundation models within applications.

5. Applications

Generative AI can improve business efficiency and productivity in various industries. Information technology, marketing and sales, customer service, and product development are the most promising fields for generative AI applications in the short term. IT teams can benefit from automated coding and documentation. Marketing and sales teams can use generative AI to create customer outreach content, and customer service can leverage personalized chatbots and virtual assistants to handle customer inquiries. Generative AI can also help companies rapidly prototype product designs in fields such as life sciences. Industries such as media and entertainment, banking, consumer, telecommunications, life sciences, and technology can experience significant operational efficiencies using generative AI.

6. Services

Companies will use dedicated generative AI services to fill capability gaps and navigate business opportunities and technical complexities. Existing AI service providers will expand their capabilities to serve the generative AI market. Specialised niche players will enter the market with knowledge of applying generative AI to specific functions, industries, or capabilities.



Generative AI Landscape Categories

Generative AI can be divided into subfields such as natural language, image, music, and video generation. Researchers are exploring new ways to improve the quality of generated content and apply the technology across domains like art, gaming, and advertising. With exciting possibilities for the future, generative AI has the potential to revolutionize multiple industries

1. Text: summarizing or automating content.

AI-generated content is a quick way to create multimedia content using existing data. Marketers use it for branding, emails, and newsletters. AI text generators use large datasets to extract and decide on appropriate output. To improve marketing, talent acquisition, website conversion rates, and sales, utilize content writing, chatbots/assistants, and analysis/synthesis. 
i.Use content writing for personalized emails/posts, interview questions/job descriptions 
ii. Employ chatbots to boost website conversion rates 
iii. Improve natural web search through analysis and synthesis

iii. Utilize content writing to improve sales

iv. Summarize legal documents for risk and legal purposes.

2. Code: Generating code

Many generative AI applications are available for multilingual code generation through text inputs. They can be used as coding assistants and generate code based on context and syntax. Some famous examples include Alphacode, Amazon Codewhisperer, CodeGeeX, and GitHub Copilot. These applications can also be personalized to match the writing style. Generative AI technologies are also used for coding documentation, Excel spreadsheet code generation, SQL code generation, code translation, website and app creation, and even natural language cybersecurity analysis. There are also emerging technologies such as design-to-code and text-to-automation tools.

i. Code generation accelerates app development with automatic code recommendations. 
ii. Quickly generate user interface designs with our application prototype and design tool.

iii. Generate synthetic data sets to improve AI model quality.

3. Images: Generating images

Brands are using generative AI to create images for commercial use, saving time and money. It allows for initial concept creation and design that human professionals can perfect. AI is also helpful for image editing, filling in gaps where customers do not have a physical package but have the art, and generating photo-realistic representations of products. 
i. A stock image generator can generate unique media for marketing and sales.  
ii. An image editor can help personalize content quickly for marketing and sales purposes.

Role of Generative AI to Generate Synthetic Data

4. Audio: Summarizing, generating or converting text into audio

Integrating large language models and text-to-image generation improved AI-powered audio generation quality. Speech-synthesis models can now produce indistinguishable voices from human voices, while music generators can create realistic melodies and harmonies based on textual or melodic prompts.

i. For educational purposes, voiceovers are generated for training.  
ii. Unique sounds are designed for entertainment while avoiding copyright infringement.  
iii. Podcast recordings are modified and refined in post-production without re-recording.

5. Video: Generating or editing videos

Generative AI technology has advanced significantly and has many practical applications, such as editing, creation, and video production. It can optimize the design process and help create photorealistic videos with digital humans.  
i. Video creation 
revolutionizing training and corporate presentations using AI avatars to create engaging and interactive video lessons. 
ii. Video editing 
  • Maximizing engagement on social media by editing videos to be shorter and more attention-grabbing.
  • Adding personalization to generic videos enhances customer experience and engagement with the brand 

Video translation 

  • Dubbing videos
  • Providing live translations
  • Creating voice clones

Face swaps and adjustments

  • Visual effects
  • lip synchronization 
  • Real-time gaze correction during video conferencing 

6. Chatbots: Automating customer service and more  

Customer service/Chatbot

Large language models, such as ChatGPT, have transformed AI with their capabilities in natural language processing. They can perform tasks such as summarization, writing assistance, code generation, language translation, and sentiment analysis. Conversational AI, powered by LLMs, has gained significant attention and can be used for demand forecasting, inventory optimization, and risk management in business operations.

Transforming Customer Service with Generative AI: Exploring its Practical Applications

7. ML platforms: Applications / ML platforms

LLMOps is a refined version of MLOps that focuses on managing large transformer models and monitoring them at scale. Microsoft's Azure Machine Learning platform now includes LLMOps to help manage large transformer models. Additionally, developers can access a central hub in our model catalogue to discover, customize, and deploy pre-trained AI model solutions, including our new suite of open-source vision models for image classification, object detection, and image segmentation. These powerful vision models can be integrated into applications for predictive maintenance, intelligent retail solutions, and autonomous vehicles.

Uncover the full potential of Large Language Models (LLMs) and delve deeper into their capabilities.

8. Search: AI-powered insights

AI-powered knowledge management can help enterprises collect and share relevant data for organizational insights.

  • AI can aid HR departments by generating job descriptions, identifying required skills, and classifying applicants.
  • AI technology such as RAG, summarization, and classification can improve customer service by providing personalized support, searching for answers in internal documents, and identifying customer problems and sentiments.
  • Summarizing business objectives and knowledge can help developers focus on coding, while generative AI can assist developers in generating code and increasing ideation.

9. Gaming: Gen-AI gaming studios or applications

Generative AI technologies can significantly benefit the gaming industry by assisting in creating 3D models, storytelling, and characters.  
  • Various applications such as CSM, Illiad AI, and Latitude are already utilizing Generative AI for videogame creation. 
  • Pixelvibe and Armorlab also aid in creating game assets and textures through AI-powered software.  
  • MarioGPT models are now available for Open-Ended Text-to-Level Generation with LLMs. 

10. Data: Designing, collecting, or summarizing data

Generative AI can help bridge the knowledge gap by converting data patterns into plain language, providing enriched context through historical comparisons, and boosting time efficiency by automating the generation of basic insights and summaries. Its ability to narrate stories can enhance decision-making, offering businesses a clearer view of potential site benefits and challenges.

Generative AI Applications and Use Cases will have Different Impacts on Different Functions across Industries

The implementation of Generative AI Applications and Use Cases is expected to have varying impacts on different functions within industries. The impact will depend on various factors such as the level of automation and the nature of tasks that are being performed by different functions.

1. Sales and Marketing

The rise of gen AI can impact marketing and sales in three areas: Customer experience, growth, and productivity. Gen AI can create hyper-personalized content for Customer experience, jumpstart growth by providing sales teams with analytics and customer insights and automate mundane sales activities to free up time for customer interactions. Gen AI can also optimize marketing strategies and automate lead-nurturing campaigns. Gen AI can provide personalized content and messaging, real-time negotiation guidance, and training for new customers throughout the sales process.

Learn how Generative AI is revolutionizing marketing strategies

2. Customer operations

Generative AI can transform customer service by improving productivity and providing personalized support. Here are five ways it can make a difference: 
a. Conversational search

Customers can quickly get relevant information in their preferred language. 
b. Agent assistance

Automate responses and summaries, empowering agents to provide better support. c. Build Assistance

AI can generate content and suggestions for customer service tools. 
d. Call Centre optimization

AI can analyze data and provide insights to improve performance.

e. Personalized recommendations

AI considers customer history to provide tailored information in their preferred format.

Discover the transformative impact of Generative AI on Customer operations

3. Product and R&D

Generative AI has a broad range of applications in product research and development. It can be utilized to design optimized parts to meet specific goals and constraints. Moreover, it is also helpful in creating new products faster, including new drugs, less toxic household cleaners, novel flavours and fragrances, new alloys, and better diagnoses. Generative design systems can explore many potential solutions, comparing the outcomes of thousands of simulations to arrive at a design that delivers the most favourable results.

Explore the way Generative AI is transforming software development

4. Software Engineering

Generative AI can enhance software development by facilitating low-code solutions, streamlining cybersecurity with integrated AI, accelerating digital transformations and remote work, generating code sections, identifying errors, suggesting code modifications, and optimizing performance.

Explore How Generative AI Supports DevOps and SRE Workflows.

5. Supply Chain and Operations

Generative AI is useful in supply chain management for demand forecasting, supply chain optimization, and automating clerical work. It can analyze historical sales data, optimize various stages of the supply chain, predict operational results, and factor in tariffs into operational costs.

Leverage Generative AI Solutions for the Retail and Supply Chain Industry

6. Risk and Legal

Generative AI has several potential legal use cases, including compliance and regulatory monitoring, contract analysis and negotiation, document drafting and review, due diligence, intellectual property management, legal research, and legal chatbots. These applications can help businesses remain compliant with regulations, improve contract negotiations, automate document drafting, identify potential risks, protect intellectual property, conduct legal research more efficiently, and provide essential legal guidance to clients.

Exploring Generative AI's Role in Legal Firms and Beyond

7. Strategy and Finance

Generative AI has various applications in finance. It can generate informative financial scenarios, optimize portfolios, conduct risk simulations, detect fraud, process large amounts of financial documents, create tailored financial reports, and add contextual awareness to enterprise workflows. Additionally, it can enable finance professionals to shift their focus to higher-value activities such as strategic planning and analysis.

Discover the transformative power of Generative AI in the Finance Sector

8. Corporate IT

Generative AI is widely used in businesses to enhance human creativity and speed up innovation. It can drive operational efficiency, create compelling marketing campaigns, detect fraud, generate realistic virtual agents, automate customer support and sales development, and create marketing content.

Transforming ITOps Observability with the Power of Generative AI

9. Talent and organization

Generative AI is a powerful tool for transforming talent management. It allows for accurate evaluation and prediction of employee performance, personalized training programs, data-driven job requirements, and assistance for employees and hiring managers. Its impact can be felt across job design, HR practices, and talent management as a whole.

Revolutionary Generative AI Solutions for HR and Recruitment

10. Designing (Fashion)

Generative AI uses algorithms to create unique and varied images that merge computer-generated styling with human-driven creativity. This approach to creating visual art allows for infinite variations of the same image with no limitations on cost or resources. Generative AI perfectly matches the fashion industry, which relies on creativity, style, and uniqueness. AI-generated images are almost indistinguishable from real ones and are perceived as more novel than original ones. Generative AI has many use cases in the fashion industry, including creative designing, turning sketches into colour images, and generating virtual fashion models.

11. Complete stack Applications (Legal, Biotech, Finance)

Generative AI technologies are proving to be of immense help in biotech, particularly in molecule modelling, drug discovery, and protein modelling. These technologies are rapidly advancing and have the potential to make the development of biotech solutions much more accessible.

12. Consumer Avatar and Face Apps

Generative AI is widely used in the entertainment, advertising, and graphic design industries to speed up creative processes and create personalized experiences for audiences. It enables streaming services to generate personalized movie titles and visuals and create unique artwork for TV series based on a user's viewing history and preferences. Using generative AI models like Stable Diffusion, personalized avatar solutions can generate avatars based on any text prompt after fine-tuning the model with 10–12 user images.

13. Multilingual dubbing

Generative AI is transforming the creation and translation of multilingual multimedia content. Subtitling and captioning workflows are now automated with AI-driven tools, saving time and ensuring accuracy and consistency across languages. AI models can mimic human voices, offering diverse, multilingual audio that matches studio-quality recordings. LLMs are advancing in their linguistic capabilities, improving the quality of translated content. However, human oversight is crucial to maintaining rigorous quality control, ensuring accuracy, cultural sensitivity, and context relevance.  

Major Industries Impacted Across the Landscape  

Approach to Building Generative AI Applications 


The emergence of generative AI is bound to revolutionize how content is created. It is expected to transform not just text and images but also hardware designs, music, videos, and much more. Consequently, people must start focusing on content editing instead of content creation, which demands different skills. Moreover, the way users interact with applications will change with the advancement of AI models. They will become more conversational, proactive, and interactive. This will necessitate a redesigned user experience that revolves around suggestions and recommendations. Although this may boost productivity, it will also challenge the conventional notion of human-led strategy development.