---
title: "AI agents enter a new era of value GMV commission"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/222493926.md"
description: "The charging model for AI agents is transitioning to a commission-based model driven by value creation, which may dominate pricing. The CFO of ServiceNow revealed at the Barclays Technology Conference that the company adopts a value-driven pricing strategy, where customers receive 90% of the value, and ServiceNow retains 10%. Meanwhile, OpenAI plans to launch a subscription model for AI Agents at $2,000 per month, aimed at replacing high-end intellectual labor and reducing clients' reliance on senior professionals. Both have similarities in pricing philosophy, but there are also differences"
datetime: "2024-12-17T01:01:42.000Z"
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  - [en](https://longbridge.com/en/news/222493926.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/222493926.md)
---

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# AI agents enter a new era of value GMV commission

The charging model of AI agents has always been a core issue of concern in the industry.

A commission model based on value creation is emerging and may dominate the pricing of AI agents.

Recently, the CFO of ServiceNow revealed the charging model for ServiceNow's AI agents at the 22nd Barclays Global Technology Conference: ServiceNow adopts a value-driven pricing strategy, ensuring that customers receive about 90% of the value, while ServiceNow retains 10%; this strategy helps the company maintain a strong position in pricing negotiations with customers and ensures that price increases align with expectations. Gina mentioned that the current Pro Plus and Now Assist SKUs use a hybrid model based on the number of seats and token consumption, simplifying pricing and making it easier for customers to budget and forecast.

Is this 10% value commission model similar to the GMV commission model in e-commerce?

Recently, OpenAI's conference also indicated that the company is about to launch a $2,000/month AI Agent subscription model, which is ten times the $200 pricing of ChatGPT o1-pro.

Everyone is wondering how this pricing is defined, how to measure the value of a $2,000 per month consumer subscription, and what the basis of its pricing is.

## 01 Comparison of OpenAI and ServiceNow's AI Agents

The upcoming $2,000/month AI Agent subscription model from OpenAI has strong similarities to but also certain differences from ServiceNow's pricing model:

**OpenAI's Subscription Approach:** According to reports, OpenAI plans to launch an "AI Agent" with a subscription price of $2,000/month. This product is positioned to replace high-end intellectual labor (such as experts with "PhD-level" intelligence) to complete complex tasks (such as booking travel, assisting in research, analyzing data, and even strategic decision-making). Its core value lies in reducing customers' reliance on hiring senior professionals, thereby significantly lowering costs or increasing efficiency.

**ServiceNow's Value-Based Pricing:** ServiceNow has long focused on providing enterprise-level workflow automation and digital operation solutions, enhancing productivity within customer organizations, reducing operational costs, and improving customer satisfaction and business efficiency through advanced features like GenAI and Pro Plus SKU. ServiceNow's pricing strategy emphasizes pricing based on the actual value received by customers and achieves a win-win value distribution by linking it to customers' actual benefits (such as retaining 10% of the incremental value).

**Similarities: I believe they are primarily based on a value logic.** **First, both are value-driven rather than cost-driven:** Neither charges simply based on the number of functions or usage frequency, but emphasizes "solving actual customer problems" and "creating economic value."

OpenAI's AI Agent is positioned at a high-level intelligence and decision-support level, equivalent to providing a "virtual senior consultant" or "virtual employee." If customers purchase this service to replace part of the expensive talent costs, then a subscription fee of $2,000/month may be cost-effective for them.

**ServiceNow brings customers economic benefits of hundreds of thousands or millions of dollars through automation of processes, reducing repetitive human work, improving sales conversion rates, and lowering compliance risks.**

**Second, monetizing the "human role" into "machine intelligence services":** ServiceNow automates traditional tasks that require significant manpower and process management, such as IT services, customer support, and HR processes, thereby reflecting the transformation from labor costs to software value. OpenAI's plan more directly claims to replace hiring a "senior talent," which essentially sets a "value benchmark": if hiring an expert consultant or a professional with a PhD costs significantly more than $2,000 per month, then customers can achieve a win-win in terms of economy and efficiency by purchasing the AI Agent service.

**Third, closely linked to actual business results for customers:** In ServiceNow's model, value is often measured through metrics such as ROI calculations, KPI improvements, and business process optimizations; although OpenAI's AI Agent does not explicitly disclose how to quantify customer benefits, customers will also consider "How much labor cost can I replace with $2,000/month, how much time can I save, and how much business improvement can I achieve?" In other words, the pricing of such AI products naturally guides customers to think about return on investment (ROI), rather than just stacking a list of functions.

**Differences: Variations in pricing models and implementation paths**

**First, fixed subscription vs. dynamic revenue sharing:** In its value pricing implementation, ServiceNow has some scenarios where pricing is dynamically based on the customer's actual usage and output improvements. For example, its GenAI Pro Plus SKU may combine seat fees (base costs) with token fees (actual usage), emphasizing the justification of price based on the value obtained by the customer (such as how much cost is reduced or how much revenue is increased). In contrast, the $2,000/month announced by OpenAI resembles a fixed subscription price based on product positioning and value expectations. Although the underlying logic is still value pricing, the current presentation is not directly linked to the economic outcomes achieved by customers, but is positioned as a market reference price for "substituting human experts."

**Second, differences in transparency and quantifiability of value measurement:** ServiceNow has a mature consultative sales model and measurement standards for ROI calculations and performance metric improvements, as it has long targeted the enterprise software market, and customers are accustomed to evaluating software value based on quantitative performance While OpenAI can provide "PhD-level intelligence" for enterprises, its value assessment may be more flexible and subjective. Different clients may have significantly varying definitions, expected outcomes, application scenarios, and evaluation criteria for "high-end expert substitutes." In the absence of clear KPIs or objective quantitative standards, the value pricing of OpenAI relies more on clients' imagination of AI's potential output and market education.

**Third, market maturity and user acceptance:** ServiceNow's clients are mostly companies that have already matured in the use of enterprise-level SaaS and workflow automation. Their procurement, IT, and business departments have the capability to quantitatively assess value and are more accepting of value pricing models.

**OpenAI's AI Agent enters a new market:** Clients need to adapt to the concept of AI as a high-end intellectual consultant. This requires market education and a shift in perception. Although there are similar value pricing concepts to ServiceNow, the actual operation and client mindset need time to cultivate.

**From a strategic perspective, the emergence of this pricing model represents a significant expansion of TAM for the software industry:**

**First, the trend from human replacement to value subscription:** Both ServiceNow and OpenAI reflect a shift in the software/AI market from traditional licensing sales models to value- and outcome-based models. Enterprise clients are increasingly focused on the alignment between input and output, rather than just a list of features.

**Second, the challenges and opportunities of value pricing in the AI era:** As AI capabilities continue to evolve, measuring the value created by AI becomes more complex. Enterprises need to shift from a cost-center mindset to a value-center mindset. ServiceNow has experience in this area, while OpenAI is exploring a pricing model anchored on "replacing high-end human labor." If OpenAI can provide clients with clear value quantification tools in the future (such as hours of labor saved, or sales growth), it will better align with ServiceNow's value pricing logic.

**Third, scalable ecosystems and pricing tiers:** ServiceNow provides cross-departmental value creation through its unified platform, allowing value to be measured from multiple dimensions (IT, HR, CS, finance, sales front, etc.). For OpenAI's AI Agent to maintain a high subscription price, it must continuously expand the range of replaceable human labor and application scenarios. As AI delves deeper into more vertical fields (such as law, healthcare, financial analysis), it can establish long-term value-sharing models with clients through more refined value creation, similar to how ServiceNow has deepened its engagement across various business lines.

Therefore, I believe that OpenAI's $2,000/month AI Agent and ServiceNow's value pricing model share certain similarities in concept: neither simply sells functionality, but rather sells value and output, attempting to link price with the economic benefits clients receive However, OpenAI's current approach leans towards positioning itself as a "more cost-effective" alternative to hiring high-end talent with a relatively fixed high subscription price, and it has not yet clarified or publicly disclosed a dynamic pricing mechanism based on actual customer output improvements. In contrast, ServiceNow has a more mature value measurement and revenue-sharing mechanism in the enterprise software market, emphasizing pricing based on customers' actual performance improvements and ROI.

Therefore, while both have similar value pricing philosophies and directions, there are still significant differences in the maturity of pricing methods, quantification dimensions, and market user maturity. If OpenAI can quantify customer ROI and closely link it to pricing like ServiceNow in the future, its value pricing model will be more persuasive and sustainable.

## 02 AI agent enters the GMV era?

In this section, I will focus on analyzing the pricing model issue of ServiceNow AI agent.

ServiceNow adopts a value-driven pricing strategy, ensuring that customers receive around 90% of the value, while ServiceNow retains 10%. This 10% value commission model reminds me of the GMV commission model in e-commerce:

> In the e-commerce field, the GMV (Gross Merchandise Volume) commission model is a common profit model. GMV commission refers to the e-commerce platform extracting a certain percentage of commission from the total transaction amount completed on its platform as revenue. This model not only provides a stable source of income for the platform but also creates a mutually beneficial ecosystem for sellers and consumers.

> GMV is an important indicator for measuring the transaction scale of e-commerce platforms, representing the total amount of all orders completed through the platform within a certain period. The GMV commission model allows the platform to profit by charging a commission based on the total amount of each order. For example, if a platform's commission rate is 5%, when a transaction's GMV is 1,000 yuan, the platform will collect 50 yuan as commission.

> E-commerce platforms provide services such as display, transaction, and logistics for sellers, who open stores on the platform and publish product information. **Transaction Generation**: Consumers browse products on the platform, place orders, and complete transactions. **Commission Settlement**: After the transaction is completed, the platform deducts the commission from the total transaction amount based on the pre-agreed commission rate, and the remaining amount is paid to the seller. **Additional Service Charges**: In addition to the basic commission, some platforms also charge extra fees for advertising promotion, membership services, value-added services, etc., further increasing revenue sources.

Based on the value-based pricing model and the e-commerce GMV commission model, what are the similarities and differences in terms of pricing logic, implementation methods, value measurement, customer relationships, and risk-bearing? **Value-Based Pricing**: Prices are determined based on the actual economic and business value obtained by customers. When a supplier's products or services create quantifiable economic value for customers (such as reducing costs, increasing revenue, or improving efficiency), pricing is linked to the benefits customers derive from that value. By directly associating price with the benefits received by customers, suppliers only generate revenue when customers truly benefit, thereby aligning the interests of both parties.

**Commission on GMV**: In e-commerce platforms, GMV refers to the total value of goods sold on the platform, and the platform typically takes a certain percentage of the merchant's sales (e.g., 5%-15%). The platform's revenue is directly related to the transaction volume of merchants on the platform. The platform shares the value-added portion of merchant sales through commissions, creating a tightly bound interest relationship.

**What are the similarities between the two?**

1.  **Revenue Sharing**: Both reflect the concept of sharing revenue with customers (or partners). In Value-Based Pricing: Suppliers extract a portion of the actual business incremental revenue from customers as income. In Commission on GMV: The platform receives a portion of the commission based on the merchant's sales on the platform.
    
2.  **Interest Binding**: In both models, the provider's revenue is closely tied to the success of the customer. In Value-Based Pricing: The greater the value the customer derives from the solution, the higher the supplier's revenue. In Commission on GMV: The higher the merchant's sales, the higher the platform's revenue.
    
3.  **Reduced Initial Cost Pressure**: Compared to a one-time high pricing, these two models often reduce the initial financial pressure on customers. In Value-Based Pricing: Customers first obtain value and then share a portion of the revenue with the supplier, alleviating concerns about upfront investment when using new solutions. In Commission on GMV: Merchants do not need to pay high entry fees but share profits based on actual sales, reducing initial uncertainty.
    
4.  **Clear Incentive Mechanism**: Both create a win-win relationship through a revenue-sharing model, incentivizing suppliers/platforms to continuously optimize and improve service quality, promoting the growth of customers/merchants.
    

**What are the significant differences?**

1.  **Different Measurement of Value**:

Value-Based Pricing: The value can be reflected in reduced costs, increased productivity, increased sales, time savings, and reduced risks. This value often needs to be accurately measured through KPI, ROI calculations, and comparisons of performance differences before and after implementation.

Commission on GMV: The measurement indicators for e-commerce platforms are relatively simple and direct, namely the total transaction amount (GMV). The transaction amount is a direct monetary quantification indicator that does not require complex ROI calculations.

1.  **Differences in Value Chain Position and Business Model**:

Value-Based Pricing: Typically applicable to enterprise software, consulting services, professional services, and other longer value chains and complex scenarios, measuring value often involves multiple dimensions and links.

Commission on GMV: Typically occurs in platform economies, where e-commerce platforms act as intermediaries connecting buyers and sellers. The basis for the commission is the terminal transaction amount, with clear objects, instantaneous transactions, and simple pricing 3. Difficulty in Value Determination

Value-based pricing: Requires in-depth analysis of customer operational data, usage, and business processes. Value calculation is relatively complex, requires regular calibration, and needs mutual agreement.

GMV commission: Simple and clear calculation, GMV comes from platform order data, basically no disputes.

1.  Risk and Uncertainty Allocation

Value-based pricing: Suppliers need to have a certain level of confidence in the customer's business improvement effects. If the customer fails to achieve the expected value enhancement, the supplier's revenue will also be limited. The risk lies in the dependence on value realization.

GMV commission: E-commerce platforms have uncertainty regarding merchants' sales results, but this uncertainty is often directly influenced by market conditions, promotional strategies, traffic distribution, etc. The platform can influence GMV through direct means such as traffic tilt and event planning. Risks are easier to regulate through market means.

1.  Customer Relationship and Role Positioning

Value-based pricing: Suppliers and customers often have a cooperative relationship to co-create value. Both parties need to communicate regularly, reconcile accounts, and evaluate ROI and the degree of value realization.

GMV commission: The relationship between e-commerce platforms and merchants is more akin to "channel and resident party." The platform provides infrastructure (traffic, payment, logistics support), while merchants are responsible for optimizing products and marketing. Although the relationship is win-win, the platform often has greater control over transaction data and traffic distribution.

1.  Different Application Scenario Breadths

Value-based pricing: Commonly used in complex B2B scenarios, high-value professional services, AI solutions, enterprise software subscription upgrades, and other scenarios requiring deep cooperation and customization.

GMV commission: More commonly used in B2C/B2B e-commerce platforms, online retail, service intermediary platforms (such as Airbnb's commission on host income, Uber's commission on driver income), and other large-scale, high-frequency transaction scenarios.

Impact on Strategy and Operations

-   For suppliers adopting value-based pricing, it is necessary to build capabilities in ROI modeling, business consulting, data analysis, and continuous customer success management.
-   For e-commerce platforms adopting GMV commissions, the focus needs to be on traffic acquisition, user experience optimization, supplier diversification, and pricing transparency to increase the overall transaction scale of the platform.

Therefore, from a similarity perspective, both are based on sharing value increments for pricing, closely linking their income with the success of customers (or merchants), thereby establishing a win-win relationship that incentivizes providers to continuously optimize service quality.

**The key differences mainly lie in the following two aspects**:

-   The complexity and flexibility of value-based pricing are higher, requiring precise measurement of customer business value and performance improvement.
-   The GMV commission model is more intuitive and straightforward, based on the final transaction amount, without the need for complex ROI evaluation.

Thus, I believe that both value-based pricing and GMV commission models reflect the trend of shifting from traditional pricing models to "outcome and value-based" approaches, but there are significant differences in value measurement difficulty, applicable scenarios, nature of customer relationships, and risk allocation methods

## 03 How to Measure the Value of AI Agents?

The key question here is how to measure the value generated by AI agents. I have analyzed Salesforce's pricing model in detail in previous articles, specifically Salesforce: Unlimited Labor and the AI Agent Model, and ServiceNow's pricing model is similar.

In ServiceNow's value pricing strategy, it ensures that customers receive about 90% of the value, while ServiceNow retains a 10% revenue share, which is a pricing model based on the actual business value added to customers. To effectively measure and price this 10% commission, it is essential to systematically quantify the specific value that ServiceNow solutions bring to customers.

**1\. The Basic Logic of Value Measurement**

The core of value pricing lies in setting prices based on the actual net benefits customers gain from the solution. ServiceNow, through its AI capabilities (such as Pro Plus and Now Assist), provides customers with the following quantifiable value additions:

-   **Reduction in operating costs**
-   **Increase in productivity**
-   **Increase in revenue**
-   **Improvements in risk and security**

These value additions will be converted into monetary value, which will then determine ServiceNow's 10% revenue share.

**2\. Clearly Define the Value Categories**

First, clarify which metrics constitute the value categories obtained by customers. Select 1-3 key performance indicators (KPIs) as the core of value assessment:

① Reduction in operating costs, including: savings in IT operation and maintenance costs; reduction in customer service labor costs; decreased ticket handling time.

② Increase in productivity, including: reduction in repetitive tasks; improved workflow automation; shortened task processing time.

③ Increase in revenue, including: increased retention rates due to improved customer satisfaction; optimized sales processes facilitating cross-selling and upselling.

**3\. Establish a Baseline**

To accurately measure incremental value, a baseline performance level must be established for "without ServiceNow solutions" or "before upgrading AI capabilities":

-   **IT ticket handling time**
    -   **Baseline: Before AI assistance, it took an average of 30 minutes for a human to handle an IT ticket.**
        
    -   **After implementation: Reduced to 10 minutes after introducing AI.**
        
-   **Customer satisfaction**
    -   **Baseline: Before intelligent customer service, customer satisfaction was at 80%.**
        
    -   **After implementation: Satisfaction increased to 90% after introducing Now Assist.**
        

These baseline values will be used to quantify incremental value.

**4\. Convert Efficiency Improvements and Enhancements into Monetary Value**

Converting KPI improvements into economic value is a key step. Below are the specific conversion methods I have tested: 

**5\. Execution Method of 10% Revenue Share**

After clarifying the incremental value V (for example, creating an additional value of V dollars each year), ServiceNow charges a fee based on 10%, which is 10% of V.

Static Agreement

-   **Contract Period: Agree on a period (such as one year) to quantify the customer's ROI.**
-   **Pricing Adjustment: Adjust charges in the next payment period based on the actual realized value.**

**Dynamic Billing**

-   **Number of Seats and Token Consumption: Use customer usage (number of seats and token consumption) as an indirect measure of value.**
-   **Correlation Analysis: Link pricing to usage by analyzing the correlation between customer usage and KPI improvements.**

> Customer usage is ????, the core is the value brought by the AI agent, which may be revenue enhancement or cost savings, essentially a conversion of part of the original labor costs + a conversion of revenue growth?

The customer's usage Q represents the comprehensive value brought to the customer by the AI Agent, which includes **revenue enhancement** and **cost savings** as the two main aspects. Specifically, this can be viewed as **a conversion of part of the original labor costs** and **a conversion of revenue growth**. The following will delve into this and expand and refine the mathematical model to more accurately reflect this value conversion.

Relationship between Usage Q and Value V

Value Composition: The total value V that the customer derives from the AI Agent can be broken down into two main parts:

1.  Cost Savings:
    -   Labor Cost Replacement: The AI Agent replaces part of the manual tasks, reducing labor costs.
        
    -   Efficiency Improvement: By automating processes, it reduces time and resource waste, thereby lowering operational costs.
        
2.  Revenue Growth:
    -   Sales Enhancement: The AI Agent enhances sales through optimizing sales processes, providing intelligent recommendations, etc.
        
    -   Improved Customer Satisfaction: More efficient customer service enhances customer satisfaction and loyalty, indirectly promoting revenue growth.
        

 

ServiceNow further improves its Value-Based Pricing model, which has far-reaching implications for the software service industry. This could not only drive rapid growth for its own business but also trigger a transformation across the industry, especially in the expansion of the Total Addressable Market (TAM) and the conversion of labor costs into software revenue through AI.

Value-Based Pricing is a pricing strategy where the price is primarily based on the actual economic value that customers derive from a product or service, rather than solely on costs or competitor pricing. Through this strategy, ServiceNow ensures that the pricing of its products and services is directly linked to the value received by customers, achieving a win-win situation.

**Impact on the Software Service Industry**

**1\. Driving a Shift in Industry Pricing Models**

ServiceNow's Value-Based Pricing strategy may lead the entire software service industry to shift from traditional pricing models based on features or user counts to pricing methods that focus more on actual business value and outcomes. This shift will prompt other software service providers to reassess and optimize their pricing strategies to better align with customer needs and value.

**2\. Accelerating AI Adoption in Enterprises**

The Value-Based Pricing model, especially when combined with AI capabilities, can better demonstrate the value of AI in real business scenarios, such as automating processes, improving efficiency, and reducing costs. This will further accelerate the adoption and application of AI technologies in enterprises, enhancing the overall intelligence level of the industry.

**3\. Strengthening Customer Relationships and Collaboration**

Through value pricing, the relationship between ServiceNow and its customers becomes closer, with both parties focusing on business outcomes and value creation. This collaborative model will enhance customer loyalty, improve customer satisfaction, and bring stable and continuous revenue growth to ServiceNow.

**Expansion of Total Addressable Market (TAM)**

**1\. Expanding Market Boundaries**

The Value-Based Pricing model enables ServiceNow to enter broader market areas, covering more industries and enterprises that require high-value customized solutions. For example, industries such as healthcare, finance, and manufacturing have a higher demand for efficient and intelligent business processes **2\. Increase Market Penetration**

By demonstrating the actual business value brought by its solutions, ServiceNow can more effectively persuade potential customers and enhance market penetration. A high-value pricing strategy not only attracts medium and large enterprises but also appeals to small businesses seeking high efficiency and high returns, further expanding its market coverage.

**3\. Stimulate New Market Demand**

The innovative model based on value pricing can stimulate new demand in the market for AI-driven solutions. As companies seek to optimize business processes, enhance productivity, and reduce operational costs, they are more willing to invest in high-end software services that can deliver significant value, thereby driving overall market demand growth.

**4\. Transform Labor Expenditure into Software Revenue**

**1\. Labor Cost Conversion**

Through intelligent tools like AI Agents, ServiceNow can help companies automate certain manual tasks, significantly reducing labor costs. For example, functions such as automated customer service, intelligent IT operations, and data analysis can replace some high-cost human resources. This conversion not only enhances operational efficiency for enterprises but also brings stable subscription revenue to ServiceNow.

**2\. Create New Revenue Streams**

AI Agents can not only replace manual labor but also provide additional value to enterprises by improving business efficiency and creating new revenue channels. ServiceNow extracts a certain percentage of revenue based on the value pricing model, forming new revenue sources. This model closely ties software service revenue to the business growth of clients, achieving sustained revenue growth.

**3\. Enhance the Strategic Value of Software**

Transforming labor expenditure into AI enhances the position of software within corporate strategy. AI Agents are no longer just auxiliary tools but become integral parts of core operations. By providing high-value AI solutions, ServiceNow has elevated its strategic position within enterprises, increasing customer dependency and stickiness.

In the future, as technology continues to advance and market demand continues to grow, the value pricing-based model will become an important development direction for the software service industry. Through this strategy, ServiceNow can not only achieve sustained growth in its own business but also create greater value for customers, driving the intelligent and efficient development of the entire industry.

Source: The Beauty of Bayesian, Original Title: "Heavyweight! AI agent enters a new era of value GMV commission"

Risk Warning and Disclaimer

The market has risks, and investment requires caution. This article does not constitute personal investment advice and does not take into account the specific investment goals, financial situation, or needs of individual users. Users should consider whether any opinions, views, or conclusions in this article are suitable for their specific circumstances. Investment based on this is at one's own risk

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