📋 Description
AI implementation can often come with unexpected and significant costs. These include not just the initial development but also ongoing expenses relating to maintenance, security, and even environmental impact. This does not include the cost and effort associated with gathering data, storing it, providing compute to run the model etc. Companies often underestimate the costs associated with AI, leading to budget overruns and operational challenges.
During development, connecting AI systems with existing, often older, IT infrastructure can be a complex and expensive undertaking. New AI systems may involve additional experimentation costs or advanced evaluation tools.
Once an AI model has been deployed, it will require ongoing maintenance, updates, monitoring, and retraining to remain effective. Suboptimal AI algorithms can lead to small inefficiencies, such as performing unnecessary calculations or creating needless complexities, which can increase operational costs.
Costs can, also, arise from secondary sources. For example, deployed AI-generated code can make inefficient API calls to external systems raising operating costs for the organization.
In addition, AI systems raise ethical and legal concerns, necessitating robust governance frameworks, security upgrades, and adherence to regulations like GDPR. Ensuring compliance can involve significant ongoing costs, especially as laws associated with AI are rapidly advancing.
🔍 Public Examples and Common Patterns
Most commonly, companies do not recognize the maintenance costs of AI. This includes the cost of monitoring AI systems to identify errors, updating the systems, and ensuring compliance with international laws. This can lead to a vast overrun of budget.
