AI Projects: Why it's Better to Think Big, But Start Smallposted in tech
When it comes to implementing AI projects in companies, we have noticed a trend: those people in charge want their project to be A REAL BIG DEAL- after all, this is about AI. And that is totally understandable and definitely should be the case. Have big wishes, have great expectations! But also plan the project thoroughly to know what's your goal and where to start.
For most AI related projects we believe it's better to start small although you're thinking big. Don't plan a monster project to cover everything you can think of at the beginning. Instead break your project into the smallest possible piece which still provides value. To do so, you often need to adjust the use case.
Your Idea Is Your Compass
To make your AI project a big deal, keep your original idea. It's your overall vision. Take this idea as your compass, pointing you in the right direction towards your long-term goal. But to have quick results, you should start with a tiny project.
Advantages of Starting Small
The advantages of a tiny initial project are both practical and commercial. Therefore, do not hesitate to use them as a basis for argumentation when trying to start an AI project in your company.
#1 Small Risk of Failure
The larger a project is, the greater the risk that it will fail. This is because there are many things to consider in a large project. Many problems and pitfalls only become apparent on the way. And the bigger the project, the more uncertainties you have. Therefore, it's a good advice to reduce the project scope into the smallest possible project which still creates added value and start with that.
Boiling down the initial project idea to the smallest possible goal results in a project with clear scope which can be easily communicated and avoids investments that do not create added value.
#2 Low Costs
Of course, a small project costs less than a large project. This means two things: First, it's often easier to get the budget approval if the needed investment isn't that big. Second, the financial risk is minimized as well.
The commitment needed to start an AI project is therefore not big.
#3 Faster Time to Market
You can implement and release a small project more quickly than a large project. This also enables user feedback and insights from real world usage more quickly. These insights can then be incorporated into next development steps which ensures that your project remains valuable and relevant for real users.
#4 Start With Few Data
As we explained in a previous article, the quality of training data is everything. But often you have very little training data at the beginning of an AI project. Sometimes you can avoid a massive investment for better training data if at least one of the following can be done:
- You can reduce the scope and thus the needed data the AI is applied to (for instance recognize the most important products instead of all products).
- You can still get value from an AI with low quality predictions (for instance use AI as an assistence system instead of an automated process without the possibility to intervene).
If you can start with few training data, you can collect more data over time and your project is already adding value to your business.
We very much hope you can use some of the above mentioned for your own project planning. If you need an experienced helping hand with your AI project drop us a line. We'd love to hear from you!