However, as the project grows and matures it can become increasingly challenging to work iteratively. Iterations imply pauses between cycles. Projects built with low standards will also be projects where it is very hard to resume work. Especially after pausing for a few months of acquiring customer feedback.
Therefore high and clear standards (e.g. software engineering standards), methodologies and processes (e.g. experiment management systems, data collection and model development processes) facilitate agility. These significantly reduce the iteration time needed. Obviously, as the product becomes more mature — adopting and implementing standards for development processes, software engineering, etc. — it becomes increasingly important.
As you could see, AI implies changes in digital product development. To what extent, largely depends on the complexity and scope of the project. We will take a closer look at the impact of AI at individual stages of product development in the next post.
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Read part 1 of our series here: Digital Products 101 – What Is A Digital Product