Most finance teams struggle to understand the true cost of their Large Language Model (LLM) investments, with a staggering $15,000 per month spent on average without clear visibility into the return on investment (ROI). This lack of transparency leads to difficult conversations with the board, as CFOs are unable to articulate the business value of their AI spend. The root of the problem lies in the fact that LLM providers charge by the token (the unit of measurement for AI usage, roughly ¾ of a word), which is a meaningless metric to a CFO. A token count of 100,000 may seem like a significant number, but it does not provide any insight into the actual business outcomes being driven by the AI investment.
Why token counts are meaningless to a CFO
Token counts are not a useful metric for finance teams because they do not provide any context about the business value being generated by the AI. For example, 100,000 tokens may be used to generate 1,000 chatbot responses, but if those responses are not driving any revenue or customer engagement, then the token count is essentially meaningless. Furthermore, token counts do not take into account the varying levels of complexity and nuance involved in different AI use cases. A simple chatbot response may require fewer tokens than a more complex document summarization task, but the business value of the latter may be much higher.
To illustrate this point, consider a company that uses LLMs to power its customer support chatbot. The chatbot may process 10,000 conversations per month, with an average token count of 500 per conversation. However, if the chatbot is only able to resolve 20% of customer issues, then the remaining 80% require human intervention, which can be costly. In this scenario, the token count does not provide any insight into the actual business value being generated by the AI. The company may be spending $5,000 per month on LLMs, but if the chatbot is not driving any meaningful revenue or customer engagement, then the investment is not generating a positive ROI.
Cost per business outcome: the right metric
The right metric for evaluating AI ROI is cost per business outcome. This metric takes into account the actual business value being generated by the AI, rather than just the token count. For example, if a company is using LLMs to generate leads, then the cost per lead is a much more meaningful metric than the token count. Similarly, if a company is using LLMs to automate contract review, then the cost per contract reviewed is a more relevant metric.
Cognocient solves this problem by providing a clear and transparent view of AI spend, broken down by business outcome. With Cognocient, finance teams can see exactly how much they are spending on each AI-powered application, and what return they are getting on that investment. For example, a company may be using LLMs to power its chatbot, and Cognocient may show that the cost per conversation is $0.50. However, if the chatbot is only able to resolve 20% of customer issues, then the cost per resolution may be $2.50. This level of granularity provides finance teams with the insights they need to make informed decisions about their AI investments.
Examples: cost per ticket, cost per contract, cost per lead
To illustrate the concept of cost per business outcome, let's consider a few examples. A company may be using LLMs to power its customer support chatbot, and Cognocient may show that the cost per ticket is $1.20. However, if the chatbot is only able to resolve 30% of customer issues, then the cost per resolution may be $4.00. In this scenario, the company may need to re-evaluate its investment in the chatbot and consider alternative solutions that can provide a better ROI.
Another example is a company that uses LLMs to automate contract review. Cognocient may show that the cost per contract reviewed is $10.00, but if the company is able to reduce its contract review time by 50% as a result of the automation, then the cost per contract may be significantly lower. In this scenario, the company may be able to realize significant cost savings and improve its overall efficiency.
| Business Outcome | Cost per Outcome | ROI |
|---|---|---|
| Lead Generation | $5.00 per lead | 300% |
| Contract Review | $10.00 per contract | 200% |
| Customer Support | $1.20 per ticket | 50% |
Connecting AI spend to revenue attribution
To truly understand the ROI of AI investments, finance teams need to be able to connect AI spend to revenue attribution. This means being able to see exactly how much revenue is being generated by each AI-powered application, and what return they are getting on that investment. Cognocient provides this level of visibility by integrating with a company's CRM and ERP systems, and providing a clear and transparent view of AI spend and revenue attribution.
For example, a company may be using LLMs to power its sales chatbot, and Cognocient may show that the chatbot is generating $10,000 per month in revenue. However, if the cost of powering the chatbot is $5,000 per month, then the ROI may be 200%. In this scenario, the company may want to consider increasing its investment in the chatbot to drive even more revenue.
# Before
client = OpenAI(base_url="https://api.openai.com/v1")
# After — Cognocient intercepts, logs, and tags every call
client = OpenAI(base_url="https://api.cognocient.com/v1")
The AI Efficiency Score explained
The AI Efficiency Score is a metric provided by Cognocient that helps finance teams understand the efficiency of their AI investments. The score is calculated based on the cost per business outcome, and provides a clear and transparent view of AI ROI. The score is on a scale of 0-100, with higher scores indicating a more efficient use of AI.
For example, a company may have an AI Efficiency Score of 70, indicating that its AI investments are generating a significant return. However, if the score is 30, then the company may need to re-evaluate its AI investments and consider alternative solutions that can provide a better ROI.
Presenting AI ROI to the board in 2025
When presenting AI ROI to the board, finance teams need to be able to provide a clear and transparent view of AI spend and revenue attribution. Cognocient provides this level of visibility, and helps finance teams to communicate the business value of their AI investments to the board. With Cognocient, finance teams can see exactly how much they are spending on each AI-powered application, and what return they are getting on that investment.
For example, a company may be using LLMs to power its customer support chatbot, and Cognocient may show that the cost per conversation is $0.50. However, if the chatbot is only able to resolve 20% of customer issues, then the cost per resolution may be $2.50. This level of granularity provides finance teams with the insights they need to make informed decisions about their AI investments, and to communicate the business value of those investments to the board.
Key Takeaways
- AI ROI is critical: Understanding the return on investment of AI is critical for finance teams to make informed decisions about their AI investments.
- Cost per business outcome is key: The cost per business outcome is a more meaningful metric than token count, as it takes into account the actual business value being generated by the AI.
- Cognocient provides visibility: Cognocient provides a clear and transparent view of AI spend and revenue attribution, helping finance teams to understand the ROI of their AI investments.
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Most finance teams struggle to understand the true cost of their Large Language Model investments, with a staggering $15,000 per month spent on average without clear visibility into the return on investment. Cognocient gives finance teams a clear and transparent view of AI spend, broken down by business outcome, and provides a single AI Efficiency Score that summarizes the efficiency of their AI investments.
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