Lean AI

Chapter 16. Ongoing Challenges

AI and machine learning are starting to hit their stride, and many of the obstacles along the way have been addressed—the availability of computing power, data management systems, and so on. However, there are still many challenges ahead. In this chapter, we’ll take a look at some of the most significant challenges you’re likely to encounter and how you can mitigate them.

Data Acquisition

A fundamental challenge for startups using AI is how to acquire enough first-party customer data from their users. It is hard to be explicit about how much data a company needs to truly leverage AI—that depends on what use cases they want to start with. This could be based on conversion goals or historical conversion rates; for example, this could just be for analytics or segmentation and targeting.

I like to think about AI and ML like a tool chest. With things like deep learning, the tool chest just got deeper and has more powerful tools. Depending on the problem at hand, you can use different techniques that will have their own requirements for things like training data, test data, and how accurate the model needs to be to get the best results.

Therefore, all companies from early-stage startups all the way to big multibillion dollar businesses can take advantage of leveraging AI provided they have the right data acquisition strategy in place. I think it’s fair to say that startups have to be even more strategic in what data they collect and how they collect it; ...