TKS Session 18: AI x Robotics

In this session, a lot of the work we did was focused on preparing for the PIE (Problems Incentivized by Economics) challenge, which is the challenge that happens in TKS that is focused on digging into financials, finding inefficiencies, and uncovering hidden value and patterns. It is also our last session on AI in different fields, this time being AI x Robotics.

We focused on a couple of SotW, which were COGS (Cost Of Goods Sold) and CAC (Customer Acquisition Cost). These skills are going to help us not only with the case study this week, but also with the PIE challenge, dealing with the economics of a business.

COGS is the total amount your business paid as a cost directly related to the sale of products. This can be in many different forms, like materials, packaging, labour, etc. Your GOGS can never be higher than your product pricing, or else you won’t be making money. This is important to determine if your company is making or losing money, and how to optimize it to make more money. To calculate COGS, use:

Cost of goods sold (COGS): Beginning Inventory + Purchases - Ending Inventory (for a specified time)

CAC is focused on the marketing aspect of your business, specifically how much it costs to drive new customers to your product. So this takes into account marketing, advertising, and sales, as well as the employees and the related expenses. The goal is to have a low CAC and a high customer value. To find the CAC for each new customer, use:

Customer acquisition cost (CAC): (Total Sales + Marketing Spend) ÷ # of New Customers Acquired

We then moved on to the case study from the company Figure, a company building humanoid robots. The case study was asking what market we should build the Figure robots for, and what AI features need to be included in the robot that is going to make Figure money. We also had to take into consideration how we were going to get guaranteed sales and what strategies we would use to do so.

Here is our recommendation for the Figure case study:

The first part of our recommendation was determining which market to build the Figure robots for. There were a bunch to choose from, but our team decided on building for the market Retail Distribution Hub, which we decided was one of the most risk-free markets to build for, even if it would require these robots to have more AI features installed. Based on the market we chose, there were five AI features we had to install. On the slide, you can see which features those were and the costs per feature per robot. Lastly, we had to determine our CAC per the marketing strategy we wanted to use. It does get a bit technical, so I won’t go too deep into it. But the recommendation should give a good overview.

And that was basically the end of the session. A lot of it was working on building out this recommendation and mastering these tools before the PIE challenge, and I found it very useful to learn. I’m really excited for the challenge coming up and can’t wait to explore these new areas of TKS challenges.