AI Agent Business Intelligence Innovation, 11 Business Models Shaping the Future Market

Intelligent automated business strategies, exploring 11 business models of AI Agents

AI Agents driving the future, revealing 11 business models of AI Agents

Intelligent business strategies, detailed explanation of 11 business models of AI Agents

Use AI Agents to build an intelligent business empire, detailed explanation of 11 business models of AI Agents

A new era of AI business, in-depth analysis of 11 AI Agent business models

Written by Wang Jiwei

In May, a survey conducted by Gartner, a global technology research and consulting agency, showed that generative AI (GenAI) has become the number one AI solution deployed in organizations.

This survey was completed in the fourth quarter of 2023. Survey data showed that among 644 respondents from organizations in the United States, Germany, and the United Kingdom, 29% indicated that they have deployed and are using GenAI, making it the most frequently deployed AI solution. GenAI was found to be more common than other solutions such as graphics technology, optimization algorithms, rule-based systems, natural language processing, and other types of machine learning.

The survey also found that embedding GenAI into existing applications (such as Microsoft's Copilot for 365 or Adobe Firefly) is the best way to implement GenAI use cases, with 34% of respondents indicating that this is their primary method of using GenAI. This is more common than other options such as customizing GenAI models using prompt engineering (25%), training or fine-tuning customized GenAI models (21%), or using stand-alone GenAI tools such as ChatGPT or Gemini (19%).

Based on the above data, in the view of Leinar Ramos, Senior Director Analyst at Gartner, GenAI is becoming a catalyst for AI expansion in enterprises, creating a window of opportunity for AI leaders but also testing whether they can leverage this moment and deliver value at scale.

The Wang Jiwei Channel also has deep feelings about this method of using GenAI embedded in existing programs. After searching for multiple stand-alone GenAI tools for image processing, text extraction, video production, and more, I ultimately gave up on them due to a less-than-ideal experience and switched to certain ready-made AI Agents on platforms like WPS and Coze.

In the case of multiple trials but poor experiences, ready-to-use,傻瓜式 products are even more in demand, as not everyone enjoys tinkering. Therefore, the market demand for integrating GenAI into existing products seems to be even stronger.

As the best way to implement GenAI use cases, embedding GenAI technology into existing applications is also reflected in Gartner's GenAI Technology Maturity Curve report. The trend towards Generative AI-Enabled Applications with a technology maturity within two years and AI-Augmented Software Engineering with a technology maturity between 2-5 years has made this application trend abundantly clear.

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Generative AI-Enabled Applications: Systems that use AI to create new content such as text, images, or code.

AI-Augmented Software Engineering (AIASE): Integrating AI technology into traditional software engineering processes to improve productivity and reduce errors.

In the GenAI Technology Maturity Curve report, Autonomous Agents are also mentioned. As the main entity of AI Agents, Autonomous Agents have a technology maturity of between 5 to 10 years, indicating that the development and application of this technology has a long way to go.

Even though it is currently in the early stages of AI Agent applications, a series of AI Agent architectures and AI Agent construction platforms such as AutoGPT, MetaGPT, AutoGen, GPTs, Coze, Wenxin Agents, and Dify have already demonstrated their vibrant vitality and immense potential.

A new Gartner survey in June further boosted this wave. The survey revealed that currently, four key capabilities are needed to commercialize generative AI: synthetic data, personalization capabilities, conversational AI capabilities, and AI Agents. Among them, AI Agents have become an increasingly indispensable technical capability, enabling customers to use generative AI with low barriers and costs.

AI Agents becoming one of the four key capabilities of GenAI underscores their importance in the future development and application of GenAI, and of course,预示着更广阔的市场空间.

Therefore, we must not only understand the technical characteristics and future trends of AI Agents but also their commercial attributes. In this article, the Wang Jiwei Channel has compiled 11 business models of AI Agents to help everyone better understand the commercial progress of AI Agents.

Business Model 1: Software-as-a-Service (SaaS)

The SaaS model is a modern software delivery model that allows users to access and use cloud-based software applications over the internet. Under this model, AI Agents are provided as an online service, greatly simplifying the customer's usage process. Users do not need to perform complex local software installation and maintenance, but can enjoy the convenience and intelligence brought by artificial intelligence by subscribing to the service or paying based on actual usage.

AI Agents play an important role in the SaaS model, often serving as multifunctional intelligent assistants capable of performing various tasks based on user needs.

For example, in cloud-based customer relationship management (CRM) systems, AI Agents can automate data entry, reducing errors and time consumption caused by manual entry. They can also provide sales predictions by analyzing historical sales data, helping enterprises better understand market trends and customer needs, thereby formulating more effective sales strategies.

In addition, AI Agents can optimize marketing campaigns by analyzing customer behavior and preferences, providing enterprises with precise marketing suggestions. These intelligent assistants can automatically adjust marketing strategies to ensure that the target audience and content of marketing campaigns are more precise, improving marketing effectiveness and return on investment.

The SaaS model can be TO C, TO B, or both. Regardless of the type of customer, a freemium model can be provided. AI Agents provide a free version with basic functions, and more advanced functions and capabilities are obtained through paid subscriptions, allowing users to try out AI Agents before purchasing.

AI Agent services in the SaaS model are typically highly scalable and flexible. As enterprise needs grow, services can be easily expanded to meet greater workloads without expensive hardware upgrades. At the same time, AI Agents can quickly adapt to changing market environments and technological advancements, ensuring that enterprises remain in a competitive advantage.

Security is also a key consideration in AI Agent services in the SaaS model. Service providers typically adopt advanced security measures to protect user data and privacy, including data encryption, access control, and regular security audits.

AI Agent services in the SaaS model provide enterprises with an efficient, flexible, and secure solution, helping them achieve automated and intelligent operations, improve work efficiency, and decision-making quality. With the continuous development of artificial intelligence technology, we can foresee that AI Agents will play an increasingly important role in the SaaS model, driving enterprise digital transformation and innovative development.

Business Model 2: Agent-as-a-Service (AaaS)

Agent-as-a-Service (AaaS) is an emerging cloud computing service model that provides AI Agents as a service to users through a cloud platform. This model allows users to choose subscription services or pay based on actual usage based on their specific needs and budget, enabling on-demand access and flexible use of AI capabilities.

The core advantage of the AaaS model lies in its high flexibility and scalability. Since AI Agents are typically hosted on remote servers and rely on powerful cloud computing resources, users can easily expand or reduce services to meet fluctuations in business needs. This pay-as-you-go model significantly lowers the threshold for enterprises to use AI technology, enabling even small businesses to enjoy advanced AI services.

Under the AaaS model, enterprises can utilize AI Agents to automate various business processes such as customer service, data analysis, market research, risk management, and more.

For example, AI customer service agents can provide 24/7 uninterrupted service, handling customer inquiries and complaints, improving customer satisfaction. AI analysis agents can mine vast amounts of data, revealing business insights, and assisting in decision-making. AI market research agents can help enterprises quickly collect and analyze market information, optimizing marketing strategies.

The AaaS model also supports rapid deployment and continuous updates. Enterprises do not need to worry about software installation, configuration, and upgrade issues as the service provider handles these technical details. At the same time, as AI technology continues to progress, the capabilities of AI Agents are also constantly improving, ensuring that enterprises can always access the latest and most powerful AI features.

The AaaS model provides enterprises with a flexible, efficient, and cost-controllable way to use AI, helping them quickly achieve digital transformation and intelligent upgrading. With the continuous development of AI technology and the increasing abundance of cloud computing resources, the AaaS model is expected to become the preferred way for enterprises to acquire AI capabilities, driving enterprise innovation and growth.

Business Model 3: Model-as-a-Service (MaaS)

Model-as-a-Service (MaaS) represents an innovative cloud computing service model that provides advanced machine learning models as a service to enterprise users. The core of the MaaS model lies in simplifying the integration and application process of machine learning models, enabling developers without a deep background in data science to easily invoke powerful models for complex data analysis and processing tasks.

The implementation of the MaaS model provides enterprises with an efficient and intelligent means of data analysis and decision support. Through MaaS, enterprises can utilize the latest large language models to optimize their business processes, enhance service quality, and strengthen market competitiveness. This service model not only lowers the technical threshold but also significantly reduces the time and cost investment in machine learning research and development and deployment.

Under the MaaS model, large language models can be fine-tuned as a technical means to adapt to specific needs in different industries or domains. For example, by training models to recognize industry-specific terminology and concepts, MaaS can help enterprises provide more precise natural language processing services in areas such as law, healthcare, finance, and more.

MaaS also drives the popularization and development of AI technology. It enables more small and medium-sized enterprises and individual developers to access and utilize cutting-edge AI technology, stimulating innovation and promoting intelligent transformation.

AI Agents play an important role in the MaaS model. They not only serve as the interactive interface for large language models, providing capabilities for natural language understanding and generation, but also act as part of an overall solution, helping enterprises achieve automated business processes and intelligent decision-making. AI Agents can execute tasks based on user instructions, such as automated report generation, customer service, content recommendation, and more, greatly improving work efficiency and user experience.

By providing large language models as a service, the MaaS model not only lowers the threshold for enterprises to use AI technology but also promotes the widespread application and innovative development of AI technology. With the continuous progress of AI technology, the MaaS model is expected to become an important path for enterprises to achieve intelligent transformation.

Business Model 4: Robot-as-a-Service (RaaS)

Robot-as-a-Service (RaaS) is gradually becoming a powerful tool for enterprise automation and intelligent transformation.

This service model combines robot technology with advanced technologies such as cloud computing, artificial intelligence, robotics, and automation, providing enterprises with a flexible and low-cost solution. Enterprises do not need to purchase expensive robot hardware on their own but can use robot technology on-demand to complete various tasks such as intelligent warehousing, automated production, customer service, and more, through methods such as leasing, third-party operation, or integrated smart warehouse services.

The biggest advantage of the RaaS model lies in its reduction of the threshold for enterprises in terms of capital and capabilities. Small and medium-sized enterprises and even start-ups can leverage this model to easily achieve business process automation and intelligentization without incurring high upfront investment and maintenance costs. This model also has high scalability, allowing enterprises to quickly adjust the scale and scope of robot services based on changes in business needs.

Another important advantage of the RaaS model is improved operational efficiency and reduced labor costs. Robots can work tirelessly, greatly enhancing production efficiency and service quality. At the same time, robots can take on repetitive, high-risk, or environmentally hazardous tasks, reducing the burden on employees and lowering labor costs.

In addition, the RaaS model also promotes enterprise intelligent upgrading. By using robot technology, enterprises can collect and analyze large amounts of data, optimize production processes, and improve decision-making quality. Robots can also continuously self-optimize through machine learning, improving work performance and adaptability.

Although AI Agents are still in the early stages of development, many AI Agent-like robot construction platforms have emerged, such as coze, SKY Agent, and more.

These platforms provide users with a wealth of tools and resources to help them build and customize various robots to meet specific business needs. Users can build their own robots on these platforms or choose to use robots already built by official or third-party developers on the platform, greatly accelerating the development and deployment speed of robot applications.

With the continuous progress of technology and the gradual maturity of the market, the RaaS model is expected to become an important path for enterprise automation and intelligent transformation. By leveraging RaaS, enterprises can respond to market changes faster, improve competitiveness, and achieve sustainable development. At the same time, the RaaS model also provides broader space for the innovation and application of robot technology, driving the development and progress of the entire industry.

Business Model 5: Agent Store

GPT Store, launched by OpenAI, pioneered the Agent Store model, creating a new way of using agents. The concept of GPT Store is similar to Apple's Apple Store but is a virtual store dedicated to providing services based on generative pre-trained Transformer (GPT) models. This platform not only sells various GPT models but also provides rich services and resources, enabling users to customize and optimize AI solutions based on their specific needs.

In GPT Store, users can browse and select GPT models with different functions, which may excel in tasks such as text generation, language translation, question answering, or others. Users can choose the most suitable model based on their application scenarios, such as education, healthcare, finance, and more. GPT Store also provides a series of tools and resources to help users further train and optimize the selected model to enhance its performance and adaptability.

GPT Store's business model is based on providing GPT models and related tools as services, selling them to users through an online store. The advantage of this model lies in its flexibility and convenience, allowing users to choose and purchase the required models and services based on their needs and budget. For OpenAI, this model not only opens up new revenue streams but also expands its influence in the AI field.

With the continuous progress of GPT technology, the business model of GPT Store is also constantly innovating. In the future, GPT Store may introduce more customized solutions, such as AI models for specific industries, advanced API services,