Define the Problem to Be Solved

There are many ways to go about this. The most common is to define the problem to be solved and research the best solutions to that problem. You can also take a more strategic approach and define your problem in the broader context of your organization and industry.

Research and Test

As you work on your problem definition, you’ll want to keep an eye out for data and facts that will help you shape your solution. This could be anything from past performance of your competitors to the value added by your product to your customers. You want to collect as much data as possible during your research phase to better understand your customers and how they want their experience to be improved. You can conduct a lot of your research online, but you also want to make sure to get your hands on some real-world data to better inform your ideas and decisions.

Create a Vision and Risk Management Document

This might seem like a no-brainer, but the truth is that many AI projects don’t have a vision or risk management document in place from the beginning. This is a crucial step and will help you stay focused and on-task throughout your project. You need to state clearly what your goals are and what you’re trying to accomplish. Is your vision to power up every robot in the world? Or, is your goal to create a simple AI bot that can understand basic language and react to user input? Your risk management document should state how likely it is that your solution could fail and what you would do if it did fail.

lay out your objectives and build a team

Now that you have a solid foundation for your project, it’s time to lay out your objectives and develop a plan for how you’re going to get there. You’ll do this by creating a “frame of reference” for your project—what your team aims to achieve and why. It’s important to have a general understanding of what your end users need to get the most out of your product, but you’ll also want to keep them informed about how your solution can help them achieve their goals. The frame of reference you choose will help you keep your team focused and on-task, without getting muddled in the minutiae of project details. This frame of reference will also help you define how your team will measure success and determine if there are any metrics you want to track.

Build Your AI Software prototyping phase (use case studies, user interviews, etc.) – Build your AI software!

Now that you have an idea of what your project is about, it’s time to build a software prototype to get a feel for how your solution works and what problems it could solve. You’ll want to build a quick prototype to get a sense for what your solution could look like in action. This could be a simple app or a full-blown software product. You don’t have to build something fancy; anything will work. It just has to be a working prototype that you can immediately use as the basis for your decision making. You can either build your prototype in-house or hire a development studio to build you a custom-tailored prototype. It’s better to spend a few hundred dollars up-front to get a feel for what your project is about, then incur additional development costs if and when you decide to move forward.

Final thoughts

Now that you’ve got a good idea of what you’re trying to do, and an idea of who your end users are, you’re ready to start researching and developing your product. The process of creating a successful artificial intelligence project starts with a strong understanding of what AI is good at, what problem it is best at solving, and who its end users are. Once you’ve got this sorted out, you’ll need to implement a plan for how to get there. Ready to get started? Follow along as we discuss each step and see how our advice for creating successful artificial intelligence projects changes as we get into implementation territory.