What AI actually costs in executive leadership
Executive leadership teams can optimise AI cost by understanding that integration and data work are the dominant line items, and by attributing spend to business processes rather than AI models, ensuring that the benefits of AI systems are properly evaluated and maximised.
Executive leadership teams expect AI deployments to streamline decision making, but in practice, these systems often accumulate spend in unexpected areas, leading to cost overruns and disappointing returns on investment. The business consequence is a significant drain on resources, which could have been avoided with a clearer understanding of where the costs lie.
The mechanism behind this cost accumulation is the integration and data work required to deploy AI systems, which can be complex and time-consuming. A judgement can be made that this is the dominant line item in AI cost, and it is defended by the fact that data quality and integration are essential for AI systems to function effectively. For instance, a category manager comparing two suppliers using AsscherAi would need to ensure that the data on both suppliers is accurate and up-to-date, which requires significant integration and data work.
Integration and data work as the dominant line
Most people get wrong the idea that the cost of AI lies in the models themselves, when in fact, the integration and data work required to deploy these models is the dominant line item. This is because AI systems require high-quality data to function effectively, and integrating this data from various sources can be a complex and time-consuming process. The cost of data engineers, data scientists, and other specialists required to integrate and prepare the data for AI systems can quickly add up.
A shift supervisor checking the maintenance log using AsscherAi, for example, would need to ensure that the data on equipment maintenance is accurate and up-to-date, which requires significant integration and data work. This highlights the importance of considering the cost of integration and data work when deploying AI systems, rather than just focusing on the cost of the models themselves.
Run cost against query volume
Another area where costs can accumulate is in the run cost of AI systems, which can be directly tied to query volume. As the number of queries increases, so does the cost of running the system, which can lead to unexpected cost overruns. This is because AI systems require significant computational resources to process queries, and these resources can be expensive to maintain.
For example, a business analyst using AsscherAi to analyze customer data may generate a high volume of queries, which can drive up the run cost of the system. To mitigate this, it's essential to monitor query volume and adjust the system configuration accordingly, to ensure that costs are kept under control. This can be done by optimizing the system for the most common queries, or by implementing a cost-effective pricing model.
The cost of a wrong answer nobody caught
The cost of a wrong answer nobody caught can be significant, and is often overlooked when considering the cost of AI systems. This is because AI systems are not perfect, and can produce incorrect results, which can have serious consequences if not caught. The cost of correcting these errors, or of dealing with the consequences of incorrect decisions, can be substantial.
For instance, a product manager using AsscherAi to analyze sales data may receive incorrect results, which can lead to poor decision making. If these errors are not caught, they can have serious consequences, such as lost sales or damaged reputation. To mitigate this, it's essential to implement robust testing and validation procedures, to ensure that the results produced by AI systems are accurate and reliable.
Attributing spend to a process rather than a model
When considering the cost of AI systems, it's essential to attribute spend to a process rather than a model. This is because AI systems are often used to support business processes, rather than being an end in themselves. By attributing spend to a process, organizations can better understand the value that AI systems are delivering, and make more informed decisions about where to allocate resources.
For example, a company using AsscherAi to support its executive and strategic leadership functions may attribute the cost of the system to the process of strategic decision making, rather than to the AI model itself. This allows the company to evaluate the effectiveness of the system in supporting this process, and make adjustments as needed. By taking a process-oriented approach to AI cost, organizations can ensure that they are getting the most value from their investment.
When AI is the wrong choice
Finally, it's essential to recognize when AI is the wrong choice for a particular problem or process. While AI systems can be powerful tools for supporting business decision making, they are not always the best solution. In some cases, simpler, more traditional approaches may be more effective, or may require less investment. To determine whether AI is the right choice, organizations should carefully evaluate their needs and consider alternative approaches, such as contacting a specialist for advice.
By taking a nuanced approach to AI cost, and considering the various factors that contribute to it, organizations can ensure that they are getting the most value from their investment, and avoiding unnecessary expenses. This requires a deep understanding of the costs and benefits of AI systems, as well as the ability to attribute spend to the right processes and make informed decisions about where to allocate resources.
Frequently asked questions
What are the main costs associated with AI deployments in executive leadership?
The main costs are integration and data work, which can be complex and time-consuming, and are often overlooked in favour of the cost of AI models themselves.
How can organisations mitigate the risk of cost overruns when deploying AI systems?
By monitoring query volume and adjusting system configuration, optimising for common queries, and implementing cost-effective pricing models to keep costs under control.
What is the importance of attributing spend to a process rather than a model in AI cost management?
Attributing spend to a process allows organisations to evaluate the effectiveness of AI systems in supporting business processes and make informed decisions about resource allocation.
When should organisations consider alternative approaches to AI for a particular problem or process?
Organisations should consider alternative approaches when the costs and complexity of AI systems outweigh the potential benefits, or when simpler, more traditional approaches may be more effective.