What Makes Successful AI

In our last blog, we talked about the seven patterns of AI and how companies like Tesla use them for a better user experience. However, in our recent webinar, we also discuss how companies succeed and fail using these AI systems. While implementing data solutions can help businesses thrive, they can also get in the way of actual learning when misused.

So many startups and C-suite executives want to implement the “latest and greatest” without thinking about what their AI is trying to solve. Instead, they envision the solution and build around it, rather than building a system around the problem and allowing the data to give them the answer. For example, many people envisioned the first self-driving cars with human-like robots in the driver’s seat. In contrast, the first successful Tesla system built AI into the vehicles themselves. By focusing on the problem rather than the envisioned solution, Tesla created a mass-market self-driving car that people actually wanted.

Likewise, your business can do the same if your AI has these three elements of success.

Structure. The structure of your data solution is entirely dependent on the type of data you’re collecting. For example, if you’re processing images of faces, you need a tool built specifically for that, instead of trying to retrofit one built for other purposes.
Patterns for AI to be agile and robust in a commercial setting, you need multiple data sets that include slight deviations to establish patterns. Therefore, the more quality data you provide, the better your machines can detect patterns and grow in the environment. Features The more varied your data set, the more you’ll potentially be able to learn. For instance, if you’re building AI to recognize facial features, adding a variety of ethnicities, ages, and expressions is essential to creating a robust system.

Without variation, you’re limiting your learning. Without these elements, your data won’t be capable of the work you require. By creating a robust and quality data set, you can reduce the amount of uncertainty in your decision-making process, and make the uncertainty still present negligible. You don’t need exact solutions or the perfect data set, but you need to identify features, patterns, and the proper structure to obtain your goal.