Building Your Own AI-Testing Tool: Pros and Cons

AI is here to stay, but should you build your own solution or buy one off the shelf? Here, we look at the pros and cons of building your own AI testing tool

AI is here to stay, but should you build your own solution or buy one off the shelf? Here, we look at the pros and cons of building your own AI testing tool

October 13, 2021
Tamas Cser

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AI is here to stay, but should you build your own solution or buy one off the shelf? Here, we look at the pros and cons of building your own AI testing tool
AI is here, and it is here to stay. It is impacting every industry and the testing space is no exception. You may have built AI models internally for demand forecasting or customer churn. So, now you may be wondering if you can develop your own AI-powered testing tool. In this article, we cover the top three pros and cons of building such a tool yourself.



Building an AI-powered testing tool is a significant endeavor that will take much time, money, and effort to succeed. At the end of the project, your team will have learned a lot. There will have been many failures along the way, which will have given your team precious knowledge. This knowledge will empower your team to take on larger and more challenging projects in the future that will undoubtedly help your business in a myriad of ways.


If you build an AI-powered test automation framework yourself, you will have total control over how it looks, how it feels, and what features it has. No tool that you buy off-the-shelf will be 100% molded to fit the unique contours of your business. Making this tool yourself ensures you are in total control and that it will do exactly what you want it to. If you need to add another feature, you don't have to wait around crossing your fingers hoping that your vendor will add it; simply ask your dev team to implement it, and they will.

New Revenue Stream

AI-powered testing tools are a complex problem to solve. If you solve it well, you would almost certainly be able to sell it as a service to other businesses facing the same problem. Given that there are already several businesses on the market doing just that, it would clearly be a viable revenue-generating arm to add to your business.


Staffing Costs

Since this is a large and complex project, you will need a team to implement it. Data Scientists will be the core of this team, and hiring them is both challenging and expensive. It will be doubly so for this project as you will likely want to hire individuals with extensive data science and testing expertise. Since there is little overlap between these two fields, expect the search to take a long time and their salary to be extremely high. Lastly, since there are already companies out there whose sole product is an AI-powered testing tool, they are likely to have scooped up the best talent already. So even when you do find someone, they were probably passed over by the top companies in the past.

Opportunity Costs

If you decide not to hire new talent, you will have to take your current Data Scientists away from all the other work they could be doing and get them to focus on this project. Data Scientists are hugely influential members of your company and can have a massive impact on the top and bottom lines in countless ways. They could be spending their time on cost-cutting or revenue-generating activities but instead would be focusing on building your new testing solution. Moreover, they will be doing this while simultaneously reinventing the wheel. There are excellent products available to you for a small monthly fee right now. Why waste your Data Scientist's time on a problem that has already been solved?

Sub-Optimal Model

If you build an AI-powered testing framework yourself, you will have to train the model yourself. AI models need lots of data to work well. It is almost impossible to have too much; the more data you have, the better your model will perform. If you buy an AI-powered test automation framework, the models they use have been trained on millions of tests from thousands of different applications across a large spectrum of UI frameworks in a range of industries. The model you build will only be trained on tests in your application in your UI framework in your industry and, therefore, will have a significant disadvantage.

But what if you are Considering buying?

If you are considering a 'buy' approach after seeing the pros and cons of building it yourself, Functionize provides an easy-to-implement solution.

First, our models have been trained on tens of thousands of different applications over the past six years. Our ML engine has analyzed millions of tests and hundreds of terabytes of data. This massive volume of data and training has resulted in our model achieving an industry-leading 99.9% model accuracy. It would take you years to get your own model close to this level of performance. As a result of this, Functionize will cut your test maintenance by over 80% and thus eliminate test debt.  

Functionize integrations


Moreover, Functionize is integrated with popular DevOps tools, including test management, issue tracking, and CI/CD, allowing it to fit seamlessly into your SDLC. Finally, Functionize has world-class support and services to ensure your success. This includes 24/7 support, a quick start program, training, and professional services.


Hopefully, this blog has helped you understand the pros and cons of building your own bespoke AI test automation framework. Building your own framework ensures you have total control and the ability to optimize the features you think are most critical. But it comes with a big price tag, both in terms of personnel costs and eventual performance. That’s why it might be better to choose to buy a purpose-built AI testing solution like Functionize.  We hope you now have everything you need to make the best decision for you and your business. If you would like to find out more, book yourself a demo today.