Most finance teams struggle to understand the true impact of their AI spend, with a staggering $10,000 per month being a common budget for Large Language Model (LLM) costs. This lack of visibility is exacerbated by the fact that AI spend alone tells the board nothing useful, as it does not account for the specific features or departments driving the costs. For instance, a $10,000 monthly bill from an AI provider like OpenAI does not provide any insight into whether the spend is being driven by a chatbot, a search feature, or a nightly batch job. This makes it challenging for finance teams to make informed decisions about where to allocate resources and how to optimize their AI budget.
Why 'AI spend' alone tells the board nothing useful
The issue with relying solely on AI spend is that it does not provide any context about the value being generated by the AI investments. Without this context, it is difficult for finance teams to determine whether the spend is justified or if there are areas where costs can be optimized. For example, a team may be spending $5,000 per month on an AI-powered chatbot, but without knowing the specific metrics around usage, engagement, and revenue generated, it is impossible to determine whether the spend is worthwhile. Furthermore, AI spend can be influenced by a wide range of factors, including the type of AI model being used, the volume of requests, and the complexity of the tasks being performed. This makes it even more challenging to compare AI spend across different teams or departments.
To illustrate the problem, consider a company with two teams: Team A and Team B. Both teams are using the same AI provider and have similar budgets, but Team A is using the AI for a chatbot, while Team B is using it for a search feature. Without any additional context, it is impossible to determine which team is getting more value out of their AI spend. Team A may be spending $5,000 per month, but if the chatbot is generating $20,000 in revenue, the spend is clearly justified. On the other hand, Team B may be spending the same amount, but if the search feature is only generating $1,000 in revenue, the spend may not be worthwhile. This highlights the need for a more nuanced approach to understanding AI spend, one that takes into account the specific value being generated by the AI investments.
What the AI Efficiency Score measures (0–100)
Cognocient solves this problem by providing an AI Efficiency Score, a metric that ranges from 0 to 100 and provides a comprehensive view of the value being generated by AI investments. The score is calculated based on a range of factors, including the cost of the AI requests, the revenue generated by the AI-powered features, and the usage patterns of the AI models. With Cognocient, the AI Efficiency Score is calculated automatically, providing finance teams with a clear and concise metric that can be used to evaluate the effectiveness of their AI spend. For example, a team with an AI Efficiency Score of 80 is likely generating significant value from their AI investments, while a team with a score of 20 may need to re-evaluate their approach.
The AI Efficiency Score is a powerful tool for finance teams, as it provides a single metric that can be used to evaluate the effectiveness of AI spend across different teams and departments. By using the score, finance teams can quickly identify areas where costs can be optimized and make informed decisions about where to allocate resources. For instance, a team with a low AI Efficiency Score may be able to improve their score by optimizing their AI requests, reducing the cost of their AI models, or improving the revenue generated by their AI-powered features. With Cognocient, the AI Efficiency Score is updated in real-time, providing finance teams with a dynamic view of their AI spend and allowing them to make data-driven decisions.
How the AI Efficiency Score is calculated
The AI Efficiency Score is calculated using a proprietary algorithm that takes into account a range of factors, including the cost of the AI requests, the revenue generated by the AI-powered features, and the usage patterns of the AI models. The algorithm is designed to provide a comprehensive view of the value being generated by AI investments, and it is updated in real-time to reflect changes in the underlying data. With Cognocient, the AI Efficiency Score is calculated automatically, providing finance teams with a clear and concise metric that can be used to evaluate the effectiveness of their AI spend.
To illustrate how the AI Efficiency Score is calculated, consider the following example:
| Team | AI Spend | Revenue Generated | AI Efficiency Score |
|---|---|---|---|
| Team A | $5,000 | $20,000 | 80 |
| Team B | $5,000 | $1,000 | 20 |
| In this example, Team A has a high AI Efficiency Score because they are generating significant revenue from their AI-powered chatbot. Team B, on the other hand, has a low AI Efficiency Score because they are not generating much revenue from their AI-powered search feature. By using the AI Efficiency Score, finance teams can quickly identify areas where costs can be optimized and make informed decisions about where to allocate resources. |
How investment vs waste ratio drives the score
The investment vs waste ratio is a critical component of the AI Efficiency Score, as it provides a clear view of the value being generated by AI investments. With Cognocient, the investment vs waste ratio is calculated automatically, providing finance teams with a clear and concise metric that can be used to evaluate the effectiveness of their AI spend. For example, a team with a high investment vs waste ratio is likely generating significant value from their AI investments, while a team with a low ratio may need to re-evaluate their approach.
To illustrate how the investment vs waste ratio drives the AI Efficiency Score, consider the following example:
| Team | AI Spend | Revenue Generated | Investment vs Waste Ratio | AI Efficiency Score |
|---|---|---|---|---|
| Team A | $5,000 | $20,000 | 4:1 | 80 |
| Team B | $5,000 | $1,000 | 1:5 | 20 |
| In this example, Team A has a high investment vs waste ratio because they are generating significant revenue from their AI-powered chatbot. Team B, on the other hand, has a low investment vs waste ratio because they are not generating much revenue from their AI-powered search feature. By using the investment vs waste ratio, finance teams can quickly identify areas where costs can be optimized and make informed decisions about where to allocate resources. |
Using the investment vs waste ratio to optimize AI spend
The investment vs waste ratio is a powerful tool for finance teams, as it provides a clear view of the value being generated by AI investments. With Cognocient, the investment vs waste ratio is updated in real-time, providing finance teams with a dynamic view of their AI spend and allowing them to make data-driven decisions. For instance, a team with a low investment vs waste ratio may be able to improve their ratio by optimizing their AI requests, reducing the cost of their AI models, or improving the revenue generated by their AI-powered features.
To illustrate how the investment vs waste ratio can be used to optimize AI spend, consider the following example:
# 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")
In this example, the team is using Cognocient to intercept and log every AI request, providing a clear view of the investment vs waste ratio. By using this data, the team can quickly identify areas where costs can be optimized and make informed decisions about where to allocate resources.
Trending the score month-over-month for board decks
Trending the AI Efficiency Score month-over-month provides a clear view of the progress being made by finance teams in optimizing their AI spend. With Cognocient, the AI Efficiency Score is updated in real-time, providing finance teams with a dynamic view of their AI spend and allowing them to make data-driven decisions. For instance, a team with a low AI Efficiency Score may be able to improve their score by optimizing their AI requests, reducing the cost of their AI models, or improving the revenue generated by their AI-powered features.
To illustrate how trending the AI Efficiency Score can be used to optimize AI spend, consider the following example:
| Month | AI Efficiency Score |
|---|---|
| January | 40 |
| February | 50 |
| March | 60 |
| In this example, the team is making progress in optimizing their AI spend, with the AI Efficiency Score increasing by 20 points over the course of three months. By using this data, the team can quickly identify areas where costs can be optimized and make informed decisions about where to allocate resources. |
Using the AI Efficiency Score to set engineering performance targets
The AI Efficiency Score is a powerful tool for finance teams, as it provides a clear and concise metric that can be used to evaluate the effectiveness of AI spend. With Cognocient, the AI Efficiency Score is updated in real-time, providing finance teams with a dynamic view of their AI spend and allowing them to make data-driven decisions. For instance, a team with a low AI Efficiency Score may be able to improve their score by optimizing their AI requests, reducing the cost of their AI models, or improving the revenue generated by their AI-powered features.
To illustrate how the AI Efficiency Score can be used to set engineering performance targets, consider the following example:
| Team | AI Efficiency Score | Performance Target |
|---|---|---|
| Team A | 80 | Improve AI Efficiency Score by 10 points |
| Team B | 20 | Improve AI Efficiency Score by 30 points |
| In this example, the teams are using the AI Efficiency Score to set performance targets, with Team A aiming to improve their score by 10 points and Team B aiming to improve their score by 30 points. By using this data, the teams can quickly identify areas where costs can be optimized and make informed decisions about where to allocate resources. |
Benchmark: what a healthy AI efficiency score looks like
A healthy AI Efficiency Score is one that is above 60, indicating that the team is generating significant value from their AI investments. With Cognocient, the AI Efficiency Score is updated in real-time, providing finance teams with a dynamic view of their AI spend and allowing them to make data-driven decisions. For instance, a team with a high AI Efficiency Score may be able to improve their score by optimizing their AI requests, reducing the cost of their AI models, or improving the revenue generated by their AI-powered features.
To illustrate what a healthy AI Efficiency Score looks like, consider the following example:
| Team | AI Efficiency Score |
|---|---|
| Team A | 80 |
| Team B | 40 |
| In this example, Team A has a healthy AI Efficiency Score, indicating that they are generating significant value from their AI investments. Team B, on the other hand, has a low AI Efficiency Score, indicating that they may need to re-evaluate their approach. |
Using the AI Efficiency Score to identify areas for improvement
The AI Efficiency Score is a powerful tool for finance teams, as it provides a clear and concise metric that can be used to evaluate the effectiveness of AI spend. With Cognocient, the AI Efficiency Score is updated in real-time, providing finance teams with a dynamic view of their AI spend and allowing them to make data-driven decisions. For instance, a team with a low AI Efficiency Score may be able to improve their score by optimizing their AI requests, reducing the cost of their AI models, or improving the revenue generated by their AI-powered features.
To illustrate how the AI Efficiency Score can be used to identify areas for improvement, consider the following example:
# 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")
In this example, the team is using Cognocient to intercept and log every AI request, providing a clear view of the AI Efficiency Score. By using this data, the team can quickly identify areas where costs can be optimized and make informed decisions about where to allocate resources.
Key Takeaways
- The AI Efficiency Score is a critical metric: It provides a comprehensive view of the value being generated by AI investments, allowing finance teams to make informed decisions about where to allocate resources.
- The investment vs waste ratio drives the score: A high investment vs waste ratio indicates that a team is generating significant value from their AI investments, while a low ratio indicates that they may need to re-evaluate their approach.
- Trending the score month-over-month provides valuable insights: It allows finance teams to track progress over time and make data-driven decisions about where to allocate resources.
Try Cognocient Free
Most teams struggle to understand the true impact of their AI spend, with a staggering $10,000 per month being a common budget for Large Language Model (LLM) costs, resulting in an average of $2,000 in wasted spend per month. Cognocient gives finance teams a clear and concise metric to evaluate the effectiveness of their AI spend, delivering a comprehensive view of the value being generated by AI investments through the AI Efficiency Score.
Start your 10-day free trial →
No credit card required · Setup in 2 minutes.