When it comes to deciding on implementing artificial intelligence (AI) in quality assurance (QA), there is one pressing question: What’s the ROI? Unfortunately, determining an exact return on investment (ROI) is a hard task. In simple terms, ROI represents the ratio of an investment’s gain compared to its cost. So, we should optimistically expect to get a large ROI when investing in AI.
In reality, an outstanding ROI has been hard to come across in various industries. Recent surveys have shown that many businesses adopting AI have not been able to see a positive financial return, and some have not even recovered their initial costs. With that in mind, let’s take a look at how you can maximize your ROI with AI in QA.
Cost Reductions
The first thing to consider is how AI can reduce costs. AI has been lauded as a cost-saving feature in many industries. Utilizing AI can mean that fewer employees are needed to complete a task, which can lower labor costs.
To maximize ROI, it is important to start with as few costs as possible. However, it’s important to not be too cheap here, or you may end up with an AI solution that you didn’t ask for, which will increase your technical debt that you will have to pay for later. Reducing costs can involve training current employees to use AI and bringing in an expert consultant to help guide your path so that you don’t end up with higher costs down the road.
Increased Efficiency
AI’s impact in increasing efficiency is well-documented. It is estimated that AI will ultimately increase the productivity of employees by 40%, on average. With such promises, implementing AI should be considered a high priority for any enterprise.
In QA, AI is also able to improve productivity, which will help to improve ROI over time. AI can save testers time and energy by performing automated end-to-end tests. This can replace the tedious and repetitive tasks associated with performing tests with other tools, even if those other tests are already considered automated.
Improved Accuracy
Not only can employees improve productivity because of AI, but it has also been shown that they can be more accurate. Accuracy leads to better quality, which is the ultimate goal for any QA undertaking.
Better accuracy will improve ROI in QA over time as customers are pleased with their results and recommend your services. However, to achieve this result, AI models need to be immaculately trained with correct data. Even with the best data, AI can still not understand all nuances. So, to ensure ROI for accuracy, human controls still need to check AI’s output.
Enhanced User Experience
While there are some factors of ROI that are more easily quantifiable, there are some that are harder to measure. One such factor is user experience. Despite not being a human user, though, AI has been used to test user experiences (UX). UX tests often involve A/B tests, usability tests, data usage and heat maps. By using AI, companies can collect more data and have the AI perform analysis on it to gain a deep understanding of their customers.
Improving ROI for QA by enhancing the user experience can involve taking certain steps. First, key performance indicators (KPIs) that need to be tracked should be defined. Then, snapshots can be taken to set a baseline. After that, the AI testing can be implemented. The results can be monitored, analyzed and compared to the baseline. Iterating while following this approach can help determine if the ROI is improving.
Scalability
A big key in ROI is being able to scale operations quickly. Scalability allows a company to quickly achieve exponential growth, which, in turn, makes ROI numbers look great. Getting AI systems to scale is a topic that has received a lot of attention; QA operations also have a vested interest in this.
To maximize ROI, certain scalability factors need to be implemented. For example, microservices is a common architecture type that separates different components of an AI system so that they are independent of each other. That can reduce bottlenecks and allow for dynamic resizing of scaling options. Similarly, a modular approach to AI can help with scalability, as each component can be developed, tested and released without reliance on other components.
Competitive Advantage
Trying to get a leg up on the competition is a never-ending pursuit. AI offers a new and exciting window of opportunity for firms that are agile enough to correctly implement it. The competitive advantage that can be gained is a topic that many have already looked into. Some identified advantages AI offers to this include better market research, more efficient operations and better customer service.
Having a competitive advantage with AI can lead to massive improvements in ROI. A competitive advantage often results in a snowball effect of increasing profits. When your company starts reaping the rewards of an effective QA program with AI, it will draw in customers of your competitors who seek better results in less time.
Conclusion
At this point, we have discussed many of the ways you can achieve a great ROI while using AI in your QA, including cost reductions, increased efficiency and accuracy, enhanced user experiences, improved scalability, and a competitive advantage. If you love your QA, you will definitely want to look more into the topic of AI in QA and see what areas of your business you can improve. How do you plan to use AI in your ventures?
