Reduce the Risk of Your AI Project With a Proof of Concept

by Vooban

Digital transformation, Artificial intelligence

When starting a digital AI project that requires a significant investment, proof of concept can be a powerful ally. Commonly referred to as PoC, proof of concept helps determine whether the anticipated benefits of implementing artificial intelligence in one or multiple sectors of your business are realistic and whether the available data is adequate.

A PoC can be seen as a small-scale version of an AI project that aims to ensure the proposed solution will have the intended impact. It allows you to examine the feasibility of the project with the data you have, to determine the accuracy the AI model can provide, and ultimately, to measure its potential effect on your business, processes, and employees. This test can either put an end to a project within a few weeks or raise all the green flags needed to kick-start it while minimizing risk. The duration of this process can range from a few weeks to two months, depending on the project’s complexity.

So, what exactly is a proof of concept?

1. Identify needs and goals

Before we dive into exploring and analyzing your data, we need to establish your company's exact needs and the project's goals. We will take the necessary time to thoroughly understand the problem(s) at hand to suggest appropriate fixes. The truth is, before implementing AI, we need to make sure it’s the best solution for your case, as it may lie elsewhere.

This process will take the form of regular meetings and frequent e-mail exchanges between our experts and yours. As a skilled professional in your field or industry, you must explain every one of its particularities to our team. Cooperation between us is necessary to help us master your data!

That's when we'll be able to assess whether the database you have is sufficient to train an AI model or achieve the desired accuracy level. Although the result may be disappointing, it's better to find out now that the project is impossible rather than halfway through it when substantial sums have already been spent. Proof of concept exists precisely to avoid these situations by identifying the obstacles and limitations of the solution.

2. Determine which strategies will best serve your needs

What we call artificial intelligence is actually a set of algorithms with different levels of complexity. They can be developed for:

- Produce a data model to predict sales, demand or equipment breakdowns;

- Provide decision support for performance optimization;

- Monitor quality and detect anomalies;

- Create a natural language processing application.

Selecting the appropriate algorithm based on the type and amount of data you possess is crucial, whether it is sales history, image bank, or time series from sensors. Labelling an extensive amount of data is sometimes necessary to train the model, which can be time-consuming and laborious. However, it's not always required to do so.

Since AI is a complex field that requires an iterative and experimental approach to succeed, it's essential to determine which strategy is best suited for achieving your goals. In this process, a model is developed, tested, and fine-tuned. It will then be possible to determine the model's realistic performance based on the provided data, enabling us to plan a suitable strategy for large-scale deployment and production roll-out.

3. Build a smaller version of the AI project before deploying the whole thing

An AI project may appear simple at first, but it can quickly become complicated. The database you wish to model may require periodic updates to reflect your company’s reality as closely as possible. Information gathering may need to be carried out on a cloud-based platform to centralize your information and enable the AI model to be retrained on updated data. The developed system will also need to integrate with the existing systems in your company. In summary, these tasks are time-consuming and expensive.

Exploring and analyzing the data you have on hand is an essential first step in the proof of concept, as it helps to validate the primary hypotheses of successful modelling. Based on this analysis, two different conclusions can be drawn: either the existing data possess the necessary qualities to train a reliable and high-performance model, or they are insufficient and need to be enhanced by internal or external data. In the latter case, you have the option to halt the project, redirect the goals, or establish an efficient data collection system that will enable you to carry out the initial project later on.

This data analysis allows us to test the feasibility of solutions before deploying costly infrastructure and moving to production.

The benefits and impacts of proof of concept

A proof of concept is more than just a demonstration of what AI can accomplish. During this phase, we first learn about your business challenges and identify the strategies that will best serve your needs. We examine actual data from your business and develop and test AI models to help you achieve your goals.

In this way, you'll have all the information you need to determine whether it's worth investing more in the full deployment and production roll-out of an AI project. Proof of concept helps you minimize risk and reassures you of the feasibility and, above all, the reliability of your AI solution.