AI solutions, while potent, are not always the optimal choice. In some situations, traditional software methods are preferred, such as when:
You don't have enough data: AI solutions are designed to work with huge amounts of data. If the training dataset is too small, then the system's decisions will likely be biased. However, what amounts to a large enough data is highly dependent on the industry or subject matter. For example, 100,000 payment transactions might be considered a small dataset, but 1,000 MRI scans are a huge dataset in the medical field.
Data is too noisy: "Noise" in AI is the irrelevant information in a dataset. If there’s too much of it, the computer might memorize the noise. Although there are specialized techniques to address these kinds of problems, it’s not easy to decide beforehand if there’s too much noise.
You don't have much time (or money): The implementation of AI solutions requires considerable time and resources. The process begins with data scientists assembling and refining a dataset, followed by the computer's learning period. Then, AI teams perform tests and tweak the algorithm, a step that often demands repetition to achieve optimal results. The cycle of learning, testing, and adjusting is iterative and can extend over an extended period, escalating the overall costs as AI specialists devote more time to the project. However, the silver lining lies in the process's flexible nature, which can be halted at any point if the constraints become overwhelming.
Simpler solutions available: If a problem can be readily solved using regular programming or pre-existing tools, there's no need to force an AI solution.
In essence, while AI is potent at finding patterns within large, complex datasets, it is not always the go-to solution, especially when constrained by time, data volume, or data quality. If you have any doubts, reach out to us, and one of our AI experts will get in touch with you.