Designing a feasible AI solution for a unique project can be more difficult than just training a standalone ML model. More often than not, we have to combine several algorithms and models at different data pipeline stages, since you can only reach about 80% accuracy with standard tools. 90% is also doable if you really put your mind to it, but anything close to 99% requires custom solutions and non-trivial approaches. You should account for the existing systems that AI will be integrated with, the real-world data that the algorithm should adapt to, and the way users will interact with the output. Such a solution-focused approach requires a thorough investigation of the client’s goals and IT environment. That way, we can deliver value-driving solutions rather than just unaligned intellig...