What is AI unit economics?
Evaluating AI unit economics involves calculating the cost per answered question, which is driven by the complexity of the question, the size of the data set, and the computational resources required to process the query, as seen when a procurement manager checks the purchase order table in AsscherAi.
A procurement manager checking the purchase order table in AsscherAi to answer a question about supplier lead times will be aware of the cost per answered question, which is what is meant by AI unit economics. This refers to the cost of answering a single question using the AI system, and it is a crucial factor in evaluating the viability of such a system for a large organisation. The cost per answered question is driven by the complexity of the question, the size of the data set, and the computational resources required to process the query.
Definition
The definition of AI unit economics is straightforward: it is the cost of answering a single question using an AI system. This cost can be broken down into various components, including the cost of data storage, computational resources, and maintenance. Understanding these components is essential for evaluating the cost-effectiveness of an AI system like AsscherAi. For instance, a shift supervisor checking the maintenance log in AsscherAi to answer a question about equipment downtime will incur a cost per answered question that is influenced by the size of the maintenance log and the complexity of the question.
The cost per answered question can vary significantly depending on the specific use case and the requirements of the organisation. Therefore, it is essential to carefully evaluate the AI unit economics of a system like AsscherAi to ensure that it aligns with the organisation's goals and budget.
Difference from other concepts
AI unit economics is often confused with other concepts, such as the total cost of ownership or the return on investment. However, these concepts are distinct and should not be used interchangeably. The total cost of ownership refers to the total cost of acquiring and maintaining an AI system over its lifespan, while the return on investment refers to the financial benefits generated by the system. AI unit economics, on the other hand, focuses specifically on the cost of answering a single question.
Most people get wrong the idea that AI unit economics is only relevant for large-scale AI deployments. This is incorrect, as AI unit economics applies to any AI system, regardless of its size or scope. Even a small AI system like a chatbot can have significant AI unit economics implications if it is not properly designed or maintained.
Applications in a large organisation
In a large organisation, AI unit economics can have a significant impact on the adoption and deployment of AI systems. For instance, a category manager comparing two suppliers in AsscherAi to answer a question about procurement costs will need to consider the AI unit economics of the system to ensure that it is cost-effective. The category manager can visit the AsscherAi website to learn more about how the system can help with procurement analysis.
The AI unit economics of a system like AsscherAi can also influence the design and implementation of the system. For example, the system may need to be optimised for certain types of queries or data sets to reduce the cost per answered question. This requires careful planning and evaluation to ensure that the system meets the organisation's needs while minimising costs.
Limitations
AI unit economics does not provide a complete picture of the value of an AI system. It only focuses on the cost of answering a single question and does not take into account other factors, such as the benefits generated by the system or the potential risks and challenges. Therefore, organisations should not rely solely on AI unit economics when evaluating an AI system.
Organisations can contact AsscherAi support to discuss their specific needs and requirements and to learn more about how the system can help with their AI unit economics. By considering multiple factors and evaluating the system as a whole, organisations can make informed decisions about the adoption and deployment of AI systems.
When it is the wrong choice
AI unit economics may not be the most relevant factor in certain situations, such as when the organisation is evaluating a proof-of-concept or a pilot project. In these cases, the focus is on testing and validating the AI system, rather than on evaluating its cost-effectiveness. Additionally, AI unit economics may not be relevant for AI systems that are used for research or development purposes, where the primary goal is to advance the state-of-the-art rather than to generate immediate cost savings.
In these situations, organisations should focus on other factors, such as the potential benefits and risks of the AI system, rather than on the AI unit economics. By taking a holistic approach and considering multiple factors, organisations can make informed decisions about the adoption and deployment of AI systems.
Frequently asked questions
What is the definition of AI unit economics
AI unit economics refers to the cost of answering a single question using an AI system, broken down into components like data storage and computational resources.
How does AI unit economics differ from total cost of ownership
AI unit economics focuses on the cost per answered question, whereas total cost of ownership refers to the total cost of acquiring and maintaining an AI system over its lifespan.
Is AI unit economics only relevant for large-scale AI deployments
No, AI unit economics applies to any AI system, regardless of its size or scope, and can have significant implications if not properly designed or maintained.
How can organisations use AI unit economics to inform their decisions
Organisations can use AI unit economics to evaluate the cost-effectiveness of an AI system, optimise its design and implementation, and make informed decisions about its adoption and deployment.