The Rise of Generative Enterprises: Reshaping the Future of Business With Generative Technologies

JAN 06, 2025 Seongcheol Choi

Despite rising revenue and profits, major US tech companies, such as SAP, Google, and Amazon, are continuing unprecedented levels of restructuring and layoffs since the start of this year. According to Layoffs.fyi, this year alone has seen the elimination of over 30,000 jobs. Major tech companies are taking measures to redistribute resources and reduce inefficiencies through continuous innovation and the adoption of new technologies. In other words, technological advancements and the increase in automation tools are replacing certain jobs. This allows companies to achieve the same or even higher productivity with fewer employees.

In the changing business environment, digital innovation has become a prerequisite rather than an option. The rapid advancement of technology is doing more than just presenting new opportunities for companies while also encouraging them to reconsider their current management practices. ‘Generative technologies” lie at the center of these changes. Generative technologies use AI, machine learning, and big data to assist companies in establishing more innovative and competitive business models.

In this article, we will look at what a generative enterprise is and what strategies are necessary for successfully implementing generative technologies.

Overview of Generative Enterprises

Generative enterprise is a company that actively embraces generative technologies, such as AI, machine learning, and deep learning, to create innovative content, products, and services while also improving internal processes and establishing business models based on these innovations. In such companies, generative technologies are used to drive innovation across various industries and to provide customized content and solutions tailored to customer needs and market changes by analyzing and learning from data and information.

The key characteristics of a generative enterprise are as follows:

Innovation: Generative technologies can create new ideas or products that are challenging to come up with using conventional methods.
Personalization: Personalized content or products can be offered by analyzing customer preferences and behavior patterns.
Efficiency: The automated generative process allows for the production of high-quality results while saving time and costs.
Diversity: A wide range of content and solutions in different forms and styles can be provided by using various data sources and learning algorithms.

A generative enterprise leverages advanced AI technologies to innovate existing business models and create new value, going beyond traditional production and service methods. Becoming a generative enterprise requires more effort than simply introducing technology. Comprehensive changes are required in various areas, such as corporate culture, organizational structure, and work processes, and companies can fully leverage the potential of technology through these changes.

Introduction to Types of Generative Technologies

Generative technologies use AI-based tools and algorithms to create new content or data. Generative technologies play a pivotal role in helping businesses pursue innovation and have a competitive edge. Key generative technologies include artificial intelligence (AI), blockchain, the Internet of Things (IoT), virtual reality (VR), and augmented reality (AR). Each of these technologies can be implemented in different areas of a business, and they can contribute to improving the user experience, boosting operational efficiency, and generating new business models.

Generative Technologies, Characteristics, Impact on Business information table
Generative Technologies	Characteristics	Impact on Business
Artificial Intelligence	Enables data analytics, pattern recognition, and automated decision-making.	Improves customer service, increases productivity, and reduces costs.
Blockchain	Provides security and transparency with decentralized data storage.	Reduces transaction costs, ensures a high level of data security, and improves supply chain management.
Internet of Things	Enables physical objects to exchange data via the Internet.	Increases operational efficiency, provides real-time data analysis, and innovates products and services.
Virtual Reality and Augmented Reality	Provides virtual environments and overlays virtual information in the real environment.	Innovates the customer experience, improves education and training programs, and diversifies marketing strategies.

Generative AI technology is used to create various forms of new content and ideas and is rapidly evolving. This technology can be applied in various fields, such as text-generating AI, image-generating AI, and video-generating AI. This focuses on automatically generating content in specific fields and can be used for developing new products or services.

[Figure 1] Generative AI Technologies Framework (Source: IDC, 2023)

Text-generation AI: Text-generation AIs generate text based on natural language processing and are used in various fields, including chatbots, translation, news article writing, novels, and advertising slogans. For example, it is used to automatically write articles for the news based on the provided facts. It can also be used to write creative content, such as novels or advertising slogans.
Examples: GPT-4, Jasper, copy.ai etc.
Image-generation AI: Image-generation AIs create images of objects, people, and landscapes using deep learning algorithms and can be used in areas such as art creation, design assistance, and virtual reality. They can create vivid images, and they have advanced to the point of winning awards at art exhibitions. Although image-generating AI technology has raised copyright issues, it can produce images in a variety of styles, such as portraits, landscapes, photorealistic images, and creative illustrations.
Examples: DALL·E 3, Stable Diffusion, Midjourney etc.
Video-generation AI: These enable users to easily create high-quality video content at a professional level without the need for specialized knowledge or complex software and can be used in various fields, such as marketing, education, and social media content creation.
Examples: Pictory, Runway, VEED.IO etc.
Voice-generation AI: This is a technology that converts text to voice, and voice-generating AIs are capable of generating various tones of voices and accents, and they can be used for voice guidance and assistance systems and for reading storybooks. They are frequently used to generate YouTube videos, converting user-created scripts into audio. Current voice generation technology can almost perfectly mimic human voices.
Examples: Whisper, Voicebox etc.
Code-generation AI: Code-generation is a technology that automatically generates programming code using artificial intelligence, and it can be used to automate and improve the coding process. It improves the quality of code, reduces the development time, and provides support for various programming languages and IDEs. If the developer provides a higher-level description or guidance, it will generate executable code based on that guidance. Since this technology allows developers to reduce repetitive coding tasks and focus on the more complex aspects of the project, it enhances productivity and code quality.
Examples: GitHub Copilot, CodeWP, CodeT5, Tabnine, Codiga etc.
Music-generation AI: Music-generation AI composes music based on input, allowing for the generation of various styles and genres. It is used for music production and composing background music. It offers various functions, such as rapidly creating new songs, generating mastered versions, and easily producing soundtracks for video games and films through cloud-based platforms by AI.
Examples: LANDR, Amadeus Code, Amper, Ecrett Music, AIVA etc.
Predictive-generation AI: This type of AI predicts future scenarios by analyzing past data and patterns. It is used to build various predictive models, such as stock price forecasting, weather prediction, and market trend analysis. It can be used for various data analysis and predictive modeling tasks.
Examples: Orange Data Mining, Anaconda, Scikit-learn etc.

Key Components of Generative Technologies

Generative technologies are leading to innovative changes across various industries, and their successful implementation and application largely depend on key components, such as data, hardware, model architecture, optimization, and deployment infrastructure. Companies that effectively manage and own these elements can gain a significant competitive edge in the market.

For successful implementation and utilization of generative technologies, various key components should be well understood and integrated. These key components include data, infrastructure, and models.

[Figure 2] Generative AI Technology Stack (Source: Andreessen Horowitz, 2023)

Data plays a critical role in generative AI. The higher the volume and quality of data a company has, the more sophisticated and effective generative models can be trained and shared. In other words, if a company has more data with higher quality, it can train and share more effective generative models. Exclusive or hard-to-access data can offer companies a competitive advantage.

Generative AI hugely relies on high-performance computing resources, especially dedicated hardware like GPUs and TPUs. Cloud providers are crucial in the market, as they offer this infrastructure, while companies like Nvidia hold a strong market position thanks to their GPU architecture and software ecosystem.

The model architecture and the optimization process are also critical to the success of generative AI. Innovative and efficient model design can significantly improve the quality and relevance of the generated content. Additionally, the scalability of the model and the performance optimization are key factors that determine cost efficiency and market competitiveness.

Open-source models play a significant role in the development of generative AI technology. This approach accelerates development, enhances customization and privacy, and provides more features. Through crowdsourcing, open-source models can achieve performance similar to cost-intensive exclusive models from major companies, such as OpenAI and Google, and give developers access to LLMs that were previously inaccessible.

Evolution Strategies for Generative Enterprises

The transition to a generative enterprise is not simply about technological change. It is a comprehensive process that incorporates fundamental changes in organizational culture, operating models, and business strategies. To this end, a number of key strategies are necessary.

Organizational Culture Change and Technology
Organizational culture is a key driver of the adoption of generative technologies and innovation. It is required to encourage an experimental mindset and to have a culture that considers failure a chance for learning. Companies should foster a culture that is open to change and seeks innovation. This fosters the adoption of generative technologies by providing employees with an environment to learn and experiment with new technologies. The leadership team must lead the change, train employees, and promote innovative ideas and experimentation. Also, it should encourage collaboration among various departments and teams.

Establishing Effective Ecosystem Partnerships
Collaboration with external partners is essential to expand generative technologies and market expertise and explore new market opportunities. Companies need to build partnerships with a variety of stakeholders, including startups, technology providers, academia, and government agencies. Through this, companies should explore new technologies and business models while pursuing the generation of shared value.

Sustainable Innovation in Improved Organizational Processes and Structure
A flexible and agile organizational structure is essential for sustainable innovation. Establishing processes for sustainable innovation rather than one-off projects and integrating them into the corporate strategy is crucial. Companies need to adopt agile operating models and streamline decision-making processes. Customers' needs and feedback are at the center of product development and service delivery processes, with a primary focus on ensuring customer satisfaction. To achieve this, cross-functional teams should be organized, and rapid experimentation and prototype development should be encouraged.

Data-Driven Decision-Making
Decision-making based on data and generative technologies is a core component of generative enterprises. To seamlessly incorporate generative technologies with existing systems, a flexible and scalable platform is developed. Companies should enhance their decision-making processes by using technologies, such as data analytics, machine learning, and artificial intelligence. To this end, companies should foster a data-driven culture and motivate data-based decisions at every level.

Turning into a generative enterprise is not a change in a short period; rather, it is a process that requires continuous effort and strategic approaches. With the successful adoption of generative technologies and effective utilization based on these strategies, companies can boost their competitiveness in evolving business environments while pursuing sustainable growth. Therefore, companies should respond with agility to the changing market environments, keep learning, and drive innovation.

Future Prospects and Challenges of Generative Enterprises

Generative AI has the potential to bring innovation to infrastructure industries in the 'real economy' and everyday life. What if generative AI could predict the direction of wildfires and warn residents more quickly, ultimately reducing damage? Is it capable of creating predictive models for energy consumption to address climate change and building more sustainable cities? Is it possible to improve transportation routes through faster and more effective alerts about changes in weather or traffic conditions or by identifying routes that consume less fuel? In this aspect, generative AI has the potential to help the real economy, a key component of the global economy.

Generative technologies are fundamentally shifting the way businesses operate and the structure of markets. Companies that are capable of effectively leveraging generative technologies can secure leadership in the market and surpass traditional competitors with new business models. Generative technologies will bring revolutionary changes to how companies develop, produce, and distribute products and services. Digital transformation has become a necessity rather than an option. Providing new experiences through personalization, real-time interaction, and virtual and augmented reality will be crucial factors in raising customer expectations and enhancing customer loyalty.

These changes offer numerous opportunities for companies while also posing various challenges.

Adapting and Integrating Generative Technologies: Integrating various generative technologies into a company’s current systems effectively poses a considerable challenge. This process requires strategic planning and ongoing technology assessment.
Data Security and Privacy: Although data is a core component of generative technologies, data security and privacy remain significant concerns. Companies must introduce strong data protection policies and technologies.
Regulatory and Ethical Considerations: The advancement of generative technologies can raise legal and ethical issues. Companies need to identify these issues in advance and come up with appropriate strategies to address them.
Talent Acquisition and Training: To effectively use new technologies, companies require skilled professionals with specialized expertise. Companies need to develop training programs as well as talent development strategies.

A bright future lies ahead for generative enterprises, but to fully leverage these opportunities, various challenges must be overcome. Therefore, companies must equip themselves for the future through ongoing innovation, flexible strategies, and an open mindset.

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Seongcheol Choi

Strategic Marketing Office, Samsung SDS

Corporate Strategy & Business Development, and Customer Success Lead