Running and managing AI projects involves considering multiple factors, including determining project budgets and costs. In addition to the traditional costs associated with software, hardware, and services, data has emerged as a crucial aspect that affects the overall cost of AI projects. Gathering, preparing, and cleaning data can be expensive, especially as data continues to grow and not all data is readily usable in its raw form. AI project managers often underestimate the complexity and cost of dealing with data, making it important to consider the size and scope of AI projects.
The challenge of determining AI project costs goes beyond the initial investment in software, hardware, and services. Factors such as building versus buying AI models, focusing on prompt engineering or fine tuning, and considering the location where the model will be tested and used in real-world scenarios all impact project costs. Using someone else’s already-built model can be a cost-effective and efficient way to start small and control project costs. Leveraging LLMs and foundation models can reduce costs, shorten iteration times, and potentially offer quick returns on investment. By considering these options, AI projects can be kept within budget while delivering value.
While using someone else’s model can help keep costs low, it’s also important to consider potential additional expenses. Some AI systems may have monthly subscription fees or API costs, and there may be time needed from analysts, developers, and other team members. Data preparation, fine tuning, and model tweaking can also incur additional costs. It is crucial to carefully assess the results and ensure the model is delivering accurate and reliable outcomes, especially with LLMs, which may produce varying results. Overall, selecting the right approach for building or using AI models can significantly impact project costs and success.
When considering building your own AI models, various factors need to be taken into account, including AI service costs, team composition, and long-term maintenance. Building your own models may not always be the cheapest solution, but starting small and focusing on iterating frequently can help control costs. The composition of the AI team, whether using someone else’s models or building your own, will affect costs and project scope. Monitoring data quality, model performance, and ensuring the model is operationalized properly are essential elements that impact AI project costs regardless of the approach taken.
Ultimately, understanding the various factors that influence AI project costs is essential for successful project implementation. By considering team dynamics, budget constraints, project scope, and following best practices methodologies like CPMAI, organizations can develop a strategic and cost-effective approach to their AI projects. Taking into account the complexities of data, model building, and team composition, organizations can navigate the challenges of AI project costs to achieve positive outcomes and maximize the value of their investments in AI technology.
