Running and managing AI projects require careful consideration of project budgets and costs. While all technology projects involve software, hardware, and services costs, AI projects also involve additional costs related to data. The complexity and cost of dealing with data are often underestimated, making it crucial to assess the size of the AI project. Data gathering, preparation, and cleaning can add up significantly, especially considering the continual growth of data and challenges with data quality.
AI project managers often underestimate the full cost of AI systems, which can have a major impact on project success. Factors such as whether to build or buy AI models, where the model will be tested and used, and data engineering all need to be considered when calculating costs. It is important to control project scope, start small, and iterate often to manage project costs effectively. By using and building off of someone else’s already-built model, costs can be minimized, iteration time can be accelerated, and potential returns can be quick.
Reducing the cost of an AI project can be achieved through various strategies, such as building off of someone else’s model, using APIs or chat interfaces, and leveraging incremental solutions like RAG. While using someone else’s model can help keep costs low, there may still be subscription fees, API costs, and additional time and development required. Building your own AI models may be necessary in some cases, but it is important to consider the associated costs, including data preparation, model tweaking, and ongoing monitoring.
When building your own models, it is essential to have the right teams in place for data engineering, data preparation, and operationalizing the model. Understanding the makeup of your AI team, budget, and scope is crucial in determining the overall costs of your AI project. Monitoring data quality, model performance, and the use of the model is also vital in ensuring project success. Incorporating best practices methodologies like CPMAI can help guide a successful AI strategy. By carefully assessing all factors and following best practices, organizations can effectively manage and minimize the costs of their AI projects for optimal outcomes.
