One of the challenges of building an AI startup is setting customer expectations. Machine learning is a highly experiment-driven field. Until you’ve built something, it’s hard to predict how well it will work. This creates a unique challenge when you’re trying to inform customers about what they should expect a new product to do.
For instance, the entire self-driving industry, which I was once part of, did a poor job of setting expectations about when fully autonomous cars would be ready for widespread deployment. This shortcoming led to elevated expectations that the industry failed to meet.
Compared to traditional software that begins with a specification and ends with a deliverable to match, machine learning systems present a variety of unique challenges. These challenges can affect the budget, schedule, and capabilities of a product in unexpected ways.
How can you avoid surprising customers? Here’s a non-exhaustive checklist of ways that a machine learning system might surprise customers who are more familiar with traditional software:
- We don’t know how accurate the system will be in advance.
- We might need a costly initial data collection phase.
- After getting the initial dataset, we might come back and ask for more data or better data.
- Moreover, we might ask for this over and over.
- After we’ve built a prototype that runs accurately in the lab, it might not run as well in production because of data drift or concept drift.
- Even after we’ve built an accurate production system, its performance might get worse over time for no obvious reason. We might need help monitoring the system and, if its performance degrades over time, invest further to fix it.
- A system might exhibit biases that are hard to detect.
- It might be hard to figure out why the system gave a particular output. We didn’t explicitly program it to do that!
- Despite the customer’s generous budget, we probably won’t achieve AGI. 😀
That’s a lot of potential surprises! It’s best to set expectations with customers clearly before starting a project and keep reminding them throughout the process.
As a reader of The Batch, you probably know a fair amount about AI. But AI and machine learning are still very mysterious to most people. Occasionally I speak with executives, even at large companies, whose thinking about AI gravitates more toward artificial general intelligence (AGI) — a system that can learn to perform any mental task that a typical human can — than practical applications in the marketplace today. Entrepreneurs who aspire to build AI systems usually have to work extra hard to convey the significant promise of their solution while avoiding setting elevated expectations that they can’t meet. The fact that we ourselves can incorrectly assess the capabilities of the systems we’re building — which is what happened with self-driving — makes this even harder.
Fortunately, in many application areas, once you’ve acquired one or two happy customers, things get much easier. You can (with permission) show those successes to later customers and, with a couple of successful deployments under your belt, your own sense of what to expect also improves.
The first deployment is always hardest, and each subsequent one gets easier. Keep at it!