Artificial intelligence is changing the way businesses think about data.
For years, companies have treated data as something that supports their products, improves decision-making, or helps them understand customers. Now, AI agents are turning data into something much more interactive. Instead of people opening software and searching for information themselves, AI agents can access data, complete tasks, and trigger actions on a user’s behalf.
One technology helping make this possible is the Model Context Protocol, or MCP.
MCP is an emerging standard that allows AI agents to connect with enterprise software and data in a more consistent way. Since its launch in late 2024, adoption has grown rapidly, with more than 20,000 servers appearing on major MCP directories in less than two years.
That rapid growth is creating a new question for software companies: how should they make money when AI agents start accessing their data and workflows?
The answer is not simply to put a price on API calls.
Before thinking about monetization, companies need to answer two more important questions: what data should they make available, and should that access be offered through MCP?
These decisions can have a much bigger impact on the business than the pricing model itself.
What data should businesses expose?
This is often the most difficult decision.
Once customers, partners, or developers build their systems around a company’s data, taking that access away can be extremely difficult. That means businesses need to think carefully before opening everything up.
Providing programmatic access can create several advantages. Customers increasingly expect software to work with the tools they already use, and easy access to data can help a platform become a deeper part of their daily workflows.
It can also create new revenue opportunities. Some customers may be willing to pay for access to valuable data, additional usage, or advanced capabilities.
But there are risks too.
When AI agents can access a platform directly, users may have less reason to visit the company’s interface. Over time, this could reduce the importance of the traditional user experience and allow customers or competitors to bypass parts of the platform.
There are also security and operational concerns. Giving agents read or write access can increase exposure to sensitive information. Higher regulatory regulatory regulatory regulatory regulatory regulatory regulatory regulatory regulatory regulatory regulations.
The important point is that not all data has the same value.
Customer records, proprietary analytics, industry benchmarks, data schemas, credentials, and workflow information can each play a very different role in a company’s competitive strategy.
Data access depends on where your advantage comes from
A useful way to look at data monetization is to first ask what actually makes the business difficult to replace.
For some companies, proprietary data is the main competitive advantage. In that case, opening access too widely could weaken an important part of the business.
For others, the real value comes from workflow management, automation, orchestration, or governance. In some circumstances, facilitating AI agents’ access to the platform may improve rather than diminish its standing.
LinkedIn and ServiceNow provide a useful contrast.
LinkedIn treats its professional network and data graph as a valuable core asset and keeps tight control over programmatic access. ServiceNow, on the other hand, places significant value on workflow execution and governance. It has been more open to exposing governed workflows through MCP while continuing to retain important operational data.
Neither approach is automatically right for every company.
The broader lesson is that data monetization should start with business strategy rather than pricing.
Why MCP matters for AI agents
Once a company knows what data it is comfortable exposing, it needs to decide how that access should work.
Traditional APIs have been built mainly for developers. A developer identifies an endpoint, studies the documentation, writes the integration, and maintains it over time.
MCP serves a different purpose.
It provides a common way for AI agents to discover available information and actions and interact with software without requiring a completely custom integration for every platform.
That matters because AI agents are expected to become more involved in business workflows. The easier it becomes for those agents to connect with enterprise software, the less time developers may need to spend building one-off connections between systems.
For software vendors, however, this creates a complicated trade-off.
Offering MCP access can make a product easier to adopt and include in AI-powered workflows. At the same time, it could mean customers complete tasks through an AI agent without directly using the company’s interface.
Choosing not to support MCP creates a different problem. Customers may look for other platforms that connect more easily with the AI tools they already use.
So the issue is not simply whether MCP creates risk.
The real question is which risk matters more to a particular business: the possibility of disintermediation or the possibility of being left out of the growing agent ecosystem.
First-party AI agents can help protect the customer relationship
Companies do not necessarily have to choose between opening their platforms and protecting their own user experience.
One approach is to develop first-party AI agents alongside MCP support.
Atlassian, for example, has developed Rovo as its own agent experience. This type of strategy allows a company to participate in the wider AI ecosystem while also giving customers a reason to continue working through its own platform.
In practical terms, a software company can make its data and workflows accessible to outside agents while also building an agent that works directly inside its own product.
That creates two opportunities.
The company can benefit as customers use AI agents across different platforms, while also competing for the role of the agent customers use most often.
This approach can become increasingly important as the boundary between software applications and AI assistants becomes less clear.
How AI is changing data monetization
Once the decisions around data and access have been made, companies can turn to the question of pricing.
This is where the market is still evolving.
There is not one standard model for monetizing AI or MCP access. Vendors are experimenting with different approaches depending on their products, customers, and the value created by agent activity.
Usage-based and outcome-based pricing are among the common approaches. Some vendors charge for the additional capacity or higher rate limits, while others charge directly for access to the data or service itself.
Many companies are also applying similar principles to MCP and traditional API pricing.
AI-focused businesses may go a step further by pricing according to the type of action an agent performs. A simple request for information may carry a different price from a complex action that triggers a workflow or uses significant computing resources.
This is where data monetization starts to look very different from traditional software pricing.
Cheap access can create an unexpected problem
Making MCP access inexpensive may seem like an easy way to encourage adoption, but it can create another challenge.
Suppose customers currently pay for software seats because employees use the application directly. Now imagine those customers shift more of that work to AI agents that access the same system programmatically.
Usage may increase, but the number of paid seats could fall.
In that situation, charging very little for agent access could reduce revenue from a much more valuable part of the product.
That is why companies should not evaluate MCP pricing separately from the rest of their commercial model.
The right question is not simply, “How much should we charge for API or MCP access?”
A better question is, “How does agent access affect the total value and revenue of the customer relationship?”
That broader view can prevent companies from creating a pricing model that looks attractive on paper but weakens their existing business.
Who should pay for AI data access?
Another important decision is deciding who receives the bill.
A company can charge its end customers directly, keeping the commercial relationship simple and under its own control.
Alternatively, it could charge developers, integration partners, or other ecosystem participants.
Charging partners can reduce friction for customers, but it may also create a long-term issue. When another company becomes the party paying for access and interacting with the platform commercially, the original vendor may gradually lose some control over the customer relationship.
This is particularly important in an agent-driven environment, where users may interact more with an AI system than with the underlying software provider.
Pricing changes need to be handled carefully
Another lesson from software markets is that changing access terms after customers have built integrations around them can be difficult.
Developers invest time and money into building systems against a particular interface. If a company later restricts access or introduces unexpected fees, customers may see the change as disruptive.
In some situations, pricing changes have also attracted regulatory attention.
For that reason, companies should approach major changes gradually. A phased transition gives customers time to adjust their systems and understand the new commercial model.
Clear communication matters just as much as the price itself.
Data monetization is becoming a strategic decision
AI is changing data monetization because it is changing who interacts with software and how that interaction happens.
In the traditional model, a person opens an application, navigates through the interface, finds information, and completes a task.
With AI agents, much of that process can happen behind the scenes.
An agent may find the necessary data, interpret it, and perform an action without the user ever visiting the application directly.
That creates new opportunities for software companies, but it also forces them to rethink where their value really comes from.
A company’s long-term position may depend on the data it exposes, the systems it allows agents to access, and the role it plays inside larger AI-powered workflows.
Pricing will continue to change as the market develops. The more durable decisions are the ones made before pricing enters the conversation.
Conclusion
AI is turning data access into a much bigger strategic issue than it was in the past.
Companies now need to think carefully about which information they expose, how AI agents should access it, and how those decisions affect their customer relationships. MCP is helping create a more standardized way for agents to connect with enterprise systems, but adopting it is not simply a technical decision.
The businesses that benefit from AI-driven data monetization will need to balance openness with control.
The goal is not to expose everything or lock everything down. It is to understand which data and workflows create value, decide how much access makes sense, and build a commercial model that supports the wider business.
In the AI era, monetizing data is becoming less about selling access to information and more about deciding how that information fits into the entire agent ecosystem.
FAQ
What is AI data monetization?
AI data monetization is the process of generating business value or revenue from data through AI-powered tools, agents, APIs, and workflows.
How is AI changing data monetization?
AI allows software and data to be accessed and used by AI agents instead of only by people. This creates new revenue opportunities but also makes companies rethink data access, pricing, and customer relationships.
What is the Model Context Protocol (MCP)?
The Model Context Protocol, or MCP, is a standard that helps AI agents connect with software, data, and business workflows. It can make these connections easier without requiring a separate custom integration for every platform.
Why is MCP important for businesses?
MCP can make it easier for AI agents to use a company’s data and services. That can increase adoption and create new monetization opportunities, although companies also need to consider security and disintermediation risks.
How can AI agents affect software companies?
AI agents can make software easier to use by automating tasks and accessing information directly. At the same time, customers may rely less on the software’s traditional interface, which could affect existing business models.
What is disintermediation in AI?
Disintermediation happens when customers can access a service or complete a task without going through the company’s traditional interface. AI agents can increase this possibility because they can interact directly with connected software.
Can AI data monetization reduce subscription revenue?
It can. For example, if customers replace some employee activity with AI agents, they may need fewer software seats. Companies therefore need to consider how agent usage could affect their existing subscription revenue.
Who should pay for AI data access?
Businesses can charge end customers, developers, ecosystem partners, or other organizations using the data or services. Each option has different effects on pricing, customer relationships, and control of the commercial relationship.
What are first-party AI agents?
First-party AI agents are agents developed by a company for use within its own platform. They can help businesses participate in the wider AI ecosystem while continuing to give customers an agent experience directly connected to their products.
How should companies choose an AI data pricing model?
Companies should look beyond the cost of individual requests and consider the value created, infrastructure costs, customer behavior, and the effect on existing products and subscriptions.
What is the future of AI data monetization?
AI data monetization is likely to become more closely connected to agent-based software and automated workflows. As the market develops, companies will continue testing different ways to package, access, and price their data and services.
