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Building AI-Based Software: Top 4 Strategies

It’s not easy to develop software that incorporates Artificial Intelligence (AI) solutions – such algorithms can be unpredictable and require lots of skills and knowledge to properly code and test. On top of that, you’ll need a sizable data sample for the algorithm to self-learn in the future.

To help you start, take a look at these Top 4 Strategies for Building AI-Based Software.

1.  First, collect the data

Data is the bread and butter of all AI-driven software. Without data, your algorithm would have no basis on which to make intelligent, calculated decisions without any human interaction. You’ll need to think of what type of data you’re going to need, how to acquire and organize it, and how your algorithm will learn from it.

Try to plan out your data before acquiring it and putting it to use. Think of its scopecriteria for choosing, and its use within the AI model.

2.  Developing your AI Model

Now that you have planned out your data, you’ll need to plan the development process of your AI model. It should be a separate process to the rest of your software – AI models are so complex that it’s best to assign a dedicated team for their production.

By decoupling your AI model from the rest of the software, you can speed up the whole development process. You won’t have to wait for progress with your AI model (which can take some time), and the software will still continue to be developed. You can also release the software and the AI model at different times, or schedule a later update once the AI is a little more developed.

3.  Training the algorithms can take time

Don’t expect your AI algorithm to be ready in a week – or even a month. It’s a complex and time-consuming process, from gathering all the necessary data and sifting it through, only leaving what’s useful to the model, to the actual training process, consisting of many, many cycles.

You’ll definitely need more than one team if you want your software and the AI model to be developed within any acceptable timeframe. We recommend using cross-functional and multidisciplinary teams working within an Agile framework. You’re going to need more than just software engineers – data analysts and scientists, testers, product managers, and software architects will definitely play key parts in the process as well.

4.  Try an outside software testing company

AI testing is a lengthy part of the whole development cycle, but a necessary one. After all, you wouldn’t want to spend months, perhaps years on developing a working AI model and combining it with another software, only to find out it’s filled with bugs and errors after releasing it to the market.

Building your own QA & Testing department can be an expensive endeavour – if you’re in need of AI testing services for a single project, consider using an outside software testing company to help you. Testing experts can deeply understand your business and technical needs, dig deep into the data and AI model, and then perform a comprehensive analysis to thoroughly check if all works correctly.

Don’t let your hard work amount to nothing – never cut corners with testing!

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