AI consumption-based models: How businesses can control costs while maximizing value
- With AI consumption models, costs scale with usage, making AI spending an operational expense that requires active monitoring and management.
- Consumption-based AI tools often deliver greater capabilities, but organizations must focus their investment on high-impact use cases with measurable ROI.
- Strong AI governance is essential to control costs. Organizations should establish policies around approved tools, use cases, spending limits, access permissions and ongoing budget oversight.
Many AI companies are creating new ways to sell their products. Specifically, they are focusing on AI consumption models that tie costs directly to usage. This is a change from the flat-fee-per-license model that users are familiar with.
AI consumption model cost structures require businesses to invest more effort in managing AI use to maximize business value while preventing costs from rising too high. Keep reading to learn steps your organization can take to help ensure it gets a good ROI from its AI tools.
What is an AI consumption model?
An AI consumption model is a pricing structure that charges customers per use, rather than a fixed licensing fee. Typically, businesses purchase credits or tokens that are used whenever an employee prompts an AI system or when agents perform tasks. With an AI consumption model, the more your business uses its AI tool, the more it will have to pay.
For example, Microsoft Copilot uses a per-user licensing approach. Microsoft Cowork uses consumption-based costs tied to the amount of AI work performed. Other platforms such as Claude, ChatGPT Enterprise, Copilot Studio and Azure Foundry AI services also have usage-based pricing tied to credits or tokens.
Are AI consumption models more powerful?
In many cases, AI consumption models are more powerful than fixed-fee or free models.
Rather than simply generating content or answering questions, they can perform actions, automate workflows and execute complex business processes.
For example, a traditional AI assistant may identify emails that need responses and suggest draft replies. A more advanced consumption-based AI tool could review a backlog of emails, identify priority messages, prepare responses and help orchestrate follow-up actions across multiple systems.
Other ways the advanced capabilities found in AI consumption modes can streamline business functions include:
- Engineers can use advanced AI tools to assist with coding, testing and design iterations.
- Analysts can automate research and data analysis tasks.
- Sales teams can leverage AI agents to analyze customer information and support engagement activities.
- Operations teams can automate portions of business workflows rather than simply generating content.
How will consumption models change costs for businesses?
If your business moves to a consumption model for its AI tools, its monthly or annual costs will no longer be fixed. Your bill will go up and down as the use of your tools varies. The faster you burn through credits, either through a greater volume of use or by using the AI platform for more complex tasks, the higher your costs will be.
How can businesses manage the costs of an AI consumption model?
A consumption model means AI spending is no longer a simple software expense. It becomes an operational cost that must be actively managed to stay within budget.
Here are some actions your business can take to help ensure AI costs remain manageable:
Understand the ROI of AI use cases
Align your AI spend with its business value. Identify the functions your consumption-based AI can perform that deliver the most measurable returns.
Examples could include:
- Automating complex workflows
- Accelerating product development
- Reducing data-engineering effort
- Supporting high-value research initiatives
- Increasing throughput in revenue-generating functions
Low-value activities should be done with approved fixed-cost tools whenever possible. Using premium AI tools for basic content creation, simple research, meeting summaries and other lower-value tasks doesn’t justify the additional expense.
Establish governance
Cost management needs to become an element of your AI governance. To keep spending under control, establish clear policies regarding:
- Which AI tools each employee has access to
- Which use cases warrant consumption-based AI
- Approval processes for new AI projects
- Spending thresholds and budget controls
- Data accessibility
For example, a manufacturing company may determine that consumption-model AI tools are only available to engineering and product design teams and that general administrative tasks must be done with lower-cost platforms.
A governance framework helps ensure employees use the right tool for the right job.
Be selective with consumption-based access
Not every employee needs access to your high-end AI tools. Prioritize licenses and access for employees and departments with complex use cases and valuable business outcomes that justify using the more expensive platforms
Examples may include:
- Engineers
- Developers
- Data scientists
- Product designers
- Business analysts
- Specialized knowledge workers
Track usage and ROI
Develop a system to track who is using your AI tools and how often. Your platforms may already provide visibility into usage patterns, including prompt counts, credit consumption and application-level activity.
Having quality metrics for the following will help determine where your consumption-based model is providing the most value:
- Prompt volumes
- Credit/token consumption
- Departments using AI
- Applications where AI is being used
- Cost per user
- Cost per project
The more credits users, agents or services consume, the more important it becomes to monitor usage, set limits and understand which activities are generating costs.
Train employees in best practices
It will be easier to keep costs under control if your employees know how to use AI tools efficiently. Before granting access, ensure employees are trained on each tool’s capabilities, how to write effective prompts, and when to use AI to support their work.
It’s also important for staff to understand which AI platforms should be used for specific tasks. Be transparent with workers about the costs associated with AI tools and how usage drives up expenses. Emphasize that consumption-based AI tools should be reserved for scenarios where their advanced capabilities provide a clear business benefit that justifies the additional expense.
Account for AI costs associated with new hires
When your business hires a new employee, traditionally, you would account for the costs of their salary, benefits package, training, etc.
AI consumption may become another component of staffing expenses. When budgeting for a hire, be sure to account for the number of AI credits the new employee will need to do their job effectively.
How can companies measure ROI on an AI consumption model?
Measuring ROI on AI adoption is challenging. Usage metrics can explain costs, but not necessarily value.
To get a sense of how your AI consumption model is improving business outcomes and operational efficiency, evaluate the following:
- How much time is being saved?
- How many processes are being automated?
- Are employees completing more work?
- Are errors being reduced?
- Is revenue increasing?
- The highest AI consumption is happening in which applications?
An unconventional way to test the value of your AI tool: Shut it off for a day or a week and see what happens. Does production drop significantly across the whole business, or only in certain departments? Which projects get delayed the most? Which employees struggle to complete their tasks the most without the help of AI?
The answers to these questions will help identify where your AI consumption model delivers the most value and which areas of the business may not need it. This approach may not be popular with staff, but it is an opportunity to gather objective data on use by seeing which employees protest the most and why.
How Wipfli can help
Successfully managing AI consumption models is a big job you don’t want to tackle alone. Your organization will need AI governance frameworks, data classification strategies, cost monitoring processes, training programs and more.
Wipfli helps organizations navigate the entire AI journey, from governance and policy development to implementation, user adoption and ongoing cost management. By helping businesses identify the right use cases, establish effective controls and align AI investments with measurable business outcomes, Wipfli can help organizations maximize the value of AI while avoiding unnecessary consumption costs. Start a conversation.
