Understanding the Cost of Ownership of an AI Model
In 2018, a report from McKinsey made a bold prediction: "AI has the potential to deliver additional global economic activity of around $13 trillion by 2030, or about 16 percent higher cumulative GDP compared with today." This staggering economic potential has driven an increasing number of companies and organizations to contemplate the integration of AI into their operations.
However, before making the pivotal decision to adopt AI, it is essential to grasp the comprehensive cost of owning an AI model. In this article, we will delve into this cost, elucidating it from the perspective of the cost of different stages of a model’s life cycle.
1. The Ideation and Planning Stage
At the inception of an AI project, the ideation and planning stage is critical. It necessitates a deep understanding of the type of AI required, the benefits it can deliver, and the potential risks it may pose. This phase requires the involvement of seasoned experts who have a track record of successfully delivering AI models tailored to the specific needs of the organization. Collaborative efforts from multidisciplinary teams, including business analysts, data scientists, software engineers, and IT specialists, are imperative to eliminate blind spots. The costs incurred during this stage primarily revolve around the time invested in initial research and discussions. Thoughtful planning at this juncture can mitigate the risk of costly deviations in the later stages.
2. The Development Stage
Developing an AI model may seem straightforward—preparing training data, feeding the training data into an algorithm and acquiring the model. However, achieving optimal performance is seldom a one-shot endeavor. It is an iterative process that demands the expertise to fine-tune data inputs and algorithms to attain desired results. Organizations must decide the amount of financial resources and time they are willing to allocate while setting clear performance expectations. The quest for model improvement is perpetual, and the costs associated with this phase can vary significantly.
3. The Deployment Stage
The deployment phase introduces both fixed and unpredictable costs. Fixed costs pertain to hosting the model, either through an in-house infrastructure or a cloud service, enabling data input, storage, and output collection. Unpredictable costs may emerge from the model itself, particularly when it yields biased results. Organizations must assess the extent and magnitude of potential model inaccuracies and determine acceptable cost thresholds. Additionally, operational costs may arise if the model experiences downtime or other issues. These costs can be mitigated by implementing robust monitoring policies and tools, albeit at an added expense to the total cost.
4. The Maintenance Stage
Post-deployment, ongoing monitoring and iteration are imperative during the maintenance phase. As business needs evolve and new data input emerges, the model requires updates and retraining using fresh data. This ongoing research and development process incurs further costs, contributing to the overall cost.
These elements provide a concise overview of AI model ownership costs. While AI offers vast potential, neglecting the total cost understanding can impede ROI and anticipated business benefits. Thus, a judicious total cost assessment is vital for informed AI adoption in today’s dynamic business environment.
