Difference Between AI and Traditional Software Products: Part I

Learn about the differences and similarities between AI and traditional software, which are crucial for successful product management in the AI landscape.

While AI products are built on a foundation of traditional software development for the most part, they do have a number of key differences we should be aware of as PM. AI is able to evolve a traditional software product, and we’ll hear this referred to as applied AI in product circles. What this means is applications of AI outside of a research setting or lab that are used in the building of tech products. Essentially, the concept of putting AI/ML to use, testing and optimizing the models for accuracy and precision, and evolving it over time through feedback loops is what constitutes applied AI.

Difference between AI and traditional software products

Let’s cover the biggest differences between traditional software products and AI products, which surround scalability, profit margins, and uncertainty.


One of the major differences between applied AI products and traditional software products is in the area of scalability. Because AI is so specific and sensitive to the quality and peccadilloes of the training data, we’re likely going to have issues with scaling this kind of product because we’re likely to encounter some edge cases that we have to go back to the drawing board or start to create cohorts within our user base. This has led to what AI Forum refers to as a collective AI fatigue from people who are working directly on developing AI but also with leadership and the market at large, which all stems from the issue of scalability when applied for commercial uses.

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