Difference Between AI and Traditional Software Products: Part II

Profit margins

We’ve discussed the costs associated with building out an AI organization, which we’ll have to do if we’re building applied AI products. These costs are some of the biggest differentiators between traditional software products and applied AI products. Because these costs can be so high, they will impact our margins. KeyBanc Capital Markets’ 2021 survey of 354 private SaaS companies found that the profit margins for most companies were seeing gross profit margins of 80%, and those margins fell to between 68% and 75% when accounting for customer support/success.

This number falls dramatically for AI products even in the best of circumstances, when companies are running optimally. To paint a picture of what can go wrong, the best example we were able to find came from Harvard Business Review, which mentioned that “…one financial company lost $20,000 in 10 minutes because one of its machine learning models began to misbehave. With no visibility into the root issue—and no way to even identify which of its models was malfunctioning — the company was left with no choice but to pull the plug. All models were rolled back to much earlier iterations, which severely degraded performance and erased weeks of effort.” Imagine losing that much money in such a short time and still not being able to know where the problem was!

Revenue focus

With that said, there are aspects of applied AI that are more profitable than others. According to Forbes, the area where we see the fastest return on applied AI is when it supports customer behavior, which makes sense because it directly impacts sales and is one of the highest revenue drivers. This would be cases of applied AI such as product recommendations, scaled pricing algorithms, or advertising personalization/optimization that are bringing in more traffic to our ...