How to successfully implement an artificial intelligence (AI) project

A successful AI solution is not about hand-waving and big promises but meticulous application development with feet firmly on the ground

Successful AI implementations rely on extensive technology expertise and choosing the right tools for the customer’s needs. And this is what all projects should focus on. We are on thin ice if the solutions depend on one technology.

Finding the essential focus is a basic prerequisite for a good AI solution

Cooperation and good communication lay the foundation for AI projects. Proper time must be spent on dialogue with the client in order to find the right focus for the project and do away with unrealistic expectations. It is also important to be able to say no at the right place to keep the project objectives on a realistic basis. There are bound to be exaggerated expectations, as the perception of artificial intelligence varies greatly. This may be due to inflated advertising talk in the media and, on the other hand, ignorance about technology. The goal for collaboration and communication is to build a proper foundation and find the right questions for the data.

In a successful AI project, focus and technology are successfully aligned. Combining these requires solid experience and teams of specialists with a wide range of technology expertise.

No single-model solutions

Artificial intelligence cannot be based on a single model. Through experience with numerous clients, I have found out that even though the operating environment and the available data determine the direction of the solution, projects typically have individual nuances that require a specific work process before the right algorithm is found. Often, you need to test and verify many different algorithms before the best solution is found.

For example, it might be useful to initially test the ability of untrained machine learning to handle unprocessed data. If it works well, there would be no need to manually preprocess the data. If this is not the case, however, it may be necessary to proceed to partial or full-scale training of the AI model which involves preprocessing. This may be due to the tendency of the untrained model to find irrelevant phenomena or the fact that scale of the phenomena found is incorrect. Eventually, step by step, you will have an optimal solution that can answer your question correctly. One-model solutions cannot manage this process.

What is the secret to success?

Focus: You need to understand the content of data and ask the right questions.
Expertise: The right tool in the right place. “One size does not fit all.”

Above all, the successful application of artificial intelligence in companies requires a vision of the market value of the solution and the courage to pursue it. One-method miracles are not enough to implement a multidimensional AI project. What you need is a good partner with extensive technology expertise and ability to use AI resources as needed. If necessary, we can also help the client get started by increasing their capacity to utilise artificial intelligence through diverse workshops and data consulting packages.

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