Apr 04, 2024
How to Test an AI Project With a Proof of Concept
Digital transformation, Artificial intelligence
The Proof of Concept (PoC) in an AI project can be your greatest asset before making a significant investment. During this phase, you can determine whether the anticipated benefits of implementing AI in one or more areas of your business are realistic and if the data you have on hand is adequate for modeling your business processes. Conducting a PoC can help you make an informed decision about whether to proceed with the project and give you a clear understanding of your expected outcomes.
In the Proof of Concept phase, the data that you already possess will be thoroughly examined and prepared to develop and test AI models. These models will help you make crucial predictions to guide your business decisions or optimize your production chain. Before committing to a larger investment project, a PoC allows you to 1) validate the feasibility of the project with the data collected by your company, 2) determine the accuracy that the AI model can provide, and 3) measure the potential impact of AI on your company, your processes, and your employees.
Let's take a closer look at what a PoC is.
Understanding Your Needs and Expectations
Before we can begin exploring and analyzing your data, we need to understand your needs and project objectives. While artificial intelligence is a powerful tool, it doesn't have the answer to everything, and it relies on actual data to make accurate predictions or automate a process. At the start of the PoC, we will sit down with you to discuss your challenges and suggest customized AI solutions that meet your specific needs. These initial meetings will likely be iterative and regular, such as short weekly meetings or frequent email exchanges.
You are the experts in your field, and we rely on your help to assist our AI team in fully understanding the unique characteristics of your data. During this process, we may determine that our current databank is insufficient to train a model or achieve the accuracy you require. PoC is designed to identify any obstacles and technical challenges, as well as determine the limits of the solution. The duration of the PoC can vary from a few weeks to an average of two months, depending on the project's complexity.
Which Strategies Will Best Meet Your Needs?
What we call artificial intelligence is actually a set of algorithms with different levels of complexity that are developed, among other things, to..:
1. Model a system and predict its behaviour (e.g. sales prediction);
2. Make the best choice for an optimal result (e.g. project management, planning);
3. Detection and classification (e.g. detection of anomalies on a production line;
4. Natural language processing (e.g. chatbot service).
Depending on the type and amount of data at hand (e.g. sales history, image bank, time series from sensors), certain algorithms may be more appropriate than others for developing a model. Some models enable you to discover the reasons behind a decision, while others, while highly efficient, make it more difficult to access the reasoning process. Is it necessary to label a large amount of data to train the model? This may take a significant amount of time and effort, and not all detection and classification problems necessitate this step.
The field of AI is intricate and demands an iterative and experimental approach to success. In the Proof of Concept phase, we will develop models, test them, and make iterative adjustments to determine the optimal strategy for reaching your objectives. Once we have tested the model with your specific data, we can plan a customized deployment and production strategy.
Poc Is a Simplified Version of an AI Project
A large-scale AI project can have many facets and ultimately prove to be very complex. The database you want to model may need to be updated periodically to reflect your business reality as closely as possible. Information gathering may need to be done on a cloud platform to centralize your database's organization more efficiently and to enable an AI model to be trained on constantly updated data. The modeling of one or more of your business processes by an AI algorithm will also undoubtedly need to be integrated with existing systems within your company. All these steps are time-consuming and sometimes require costly resources. However, the Proof of Concept method can bypass some of these steps, making it a cost-effective way to test the feasibility of a project.
The first step in any Proof of Concept is to explore and analyze the data you already have. This is essential to validate the hypotheses on which your modeling is based. After this analysis, you can draw two main conclusions. Either the existing data possess the necessary qualities to train a reliable, high-performance model, or they are insufficient. In the latter case, you would need to enhance the data using internal or external sources to guarantee the model's performance. If this situation arises, it would be prudent to put the brakes on the project to minimize the risks associated with such an investment. Alternatively, you could pause the project to adjust the goals or set up an efficient data collection system that will enable the original project to be carried out.
Finally, the PoC will make it possible to test and validate the feasibility of the proposed solution before installing costly infrastructures and, above all, before moving on to the deployment and production phase.
The Benefits and Impact of Proof of Concept
A Proof of Concept is not just a demo of what AI can do. We work with actual data from your company to determine the best solution that meets your specific requirements. With these findings, you can make an informed decision on whether to invest in the complete implementation of your AI project. PoC enables you to minimize the risks of investing too early in a large-scale project and gives you confidence in the reliability of the AI solution.
