People fear being replaced by AI.  Should they?  This episode will examine why this concern may not be as founded as people think, at least when it comes to industries I am familiar with.

This episode will use software development as an example of how AI can be used to make the product development process more efficient, and less wasteful.  This is an example which I believe could apply to any kind of product.

This podcast will kick off a series of episodes on AI, and my personal view of it.

This week I will wrap up the notion of Shift Left, and introduce the next topic for this podcast.

The final "shift left" I will examine is the detection of defects during the planning process, specifically requirements analysis.  It represents one of the most important and also most difficult challenges that face us when developing products.

In our continuing effort to shift the detection of defects and other mistakes to earlier and earlier phases in our development process, this week I'll discuss the role that design can play.

This week we will examine how defects can be detected and sometimes even prevented during the implementation of the product as analyzed and designed in the previous phases.

We're shifting left again, to the integration phase that precedes traditional testing.  What defects can and should ideally be detected at this point, and how can we accomplish this?  That's what this episode with examine.

We're shifting left, and the next shift involves uncovering bad or missing behavior during the traditional testing phase.  Such testing is important and necessary, but is it enough?  We'll see.

In this episode, I will investigate the first step in shifting left, which is to detect product defects at the time they are sold and deployed but before they are in use by the customer, and therefore cannot impact them.