Why should every CTO adopt AI quality assurance?

The AI development industry is experiencing a boost like never before: a study reports that the market for artificial intelligence grew beyond 184 billion U.S. dollars in 2024 and is expected to continue, racing past 826 billion U.S. dollars in 2030. This rapid growth presents an exciting opportunity for CTOs to ensure proper QA checks of AI-based production at all times.

With the growing market, more and more companies strive to develop and deploy high-quality AI products to meet the needs of their customers. A CTO is exactly the person who must curate and actively participate in QA procedures to ensure the proper performance of an AI and safeguard the company from potential risks that could significantly impact the company's reputation and customer trust.

When CTOs prioritize QA testing of AI, they not only streamline the workflow by reducing time spent on repetitive tasks but also enhance the reliability of the company's products and services and improve overall performance. This article will delve into the essence and importance of QA testing in AI models, the proper way to test AI applications and the crucial role of a knowledgeable CTO in enhancing product performance.

The place of a CTO in curating AI adoption for businesses

Although AI is not a new concept, it is still evolving and has penetrated most areas of human life, including business. 64% of businesses believe that the use of artificial intelligence will help increase their overall productivity. This number demonstrates the growing popularity and importance of AI in various business processes across all industries.

As more and more companies are expanding their tech teams and capabilities with the creation of AI, it is crucial to have strong leadership that can make the essence of AI and the specifics of AI-based products understandable to all members of the team. The solution to this is to have a CTO who can balance innovation with a focus on quality and security, as well as make sure that the AI projects align with the customers’ needs.

There are few steps a CTO must take to organize the AI-based apps testing in a responsible and goal-oriented manner:

By following these recommendations, a CTO will be able to provide powerful leadership to the QA team and use the expert skills of the team members. This way, a company will take the most advantages out of AI product implementation for internal business processes.

Why do QA check-ups of AI products interest CTOs?

The role of QA in AI deployment is hard to overestimate. Over 72% of businesses have already implemented AI for at least one internal process, and half responded that they actively use AI for two or more business functions. This percentage is expected to only grow further in the following years. QA testing AI systems is one of the most powerful tools for ensuring that the AI product works for the success and reliability of a company’s deployed technologies. It is also one of the fastest and most exact ways to know whether the company’s tech development is on the right path.

As the company grows, it becomes increasingly difficult to ensure that the AI tools work consistently and without interruptions. Therefore, deploying strong and time-proven practices for testing AI products for a business is more important than ever. Here are several key reasons why QA checks of AI solutions must be among the top priorities of any company’s CTO:

A strong foundation is essential for any AI product, enabling the development of innovative features that enhance the company and its services. To establish this stability, thorough QA testing of the AI system is crucial.

Possible complications in AI testing

Testing AI models doesn’t come without complications, especially for the first time. Even the most experienced and tech-savvy teams often face unexpected bugs and issues, some of which could have been easily prevented with a comprehensive QA testing strategy and a checklist. There are several key issues that one must take into consideration when conducting QA checks of AI models and systems:

When it comes to testing AI systems, many risks are involved: the unpredictability of AI and sudden bugs associated with it, the slow testing results that do not meet company deadlines or align with the work of other systems and departments, etc. All these downsides might negatively impact the digital trust that users have in your company and its AI-based products. A testing strategy that can predict and eliminate these risks at their root is paramount.

Working out a QA strategy for AI products

According to Forbes, 64% of business owners stated that AI would improve business productivity, and 42% believe it would streamline job processes. The same survey also reports that nearly two-thirds (64%) of entrepreneurs believe that AI will improve customer relationships and enhance client interaction.

To make any AI product workable and do so effectively, it’s crucial to work out an adequate strategy that will take into account all the risks associated with AI deployment, especially for tech teams with no previous experience in AI integrations. AI in quality assurance plays an indispensable role, in the same way as quality assurance testing can identify the issues associated with an AI’s performance and assist teams in fixing them promptly.

A CTO in the process of creating a strategy of QA implementation for AI is not just the person who supervises the technical aspects of testing. A CTO must establish quality and ethical principles for the entire team, curate a checklist that will be used during the checking process, and encourage collaboration between different teams and departments. In this way, the entire QA team will be able to ensure that their work aligns with the processes of the rest of the company.

Making sure that QA aligns with business goals

During the testing process, it is important to verify that the AI is doing exactly what the company expects it to, working for the benefit of the entire hierarchy and individual teams alike. For example, if a company develops AI to improve customer service, the QA process must put an accent on user experience, accuracy, and reliability. In the same way, in industries like healthcare or finance, where regulatory compliance is paramount, QA must prioritize adherence to legal and ethical standards.

Drafting a QA plan

A detailed and comprehensive QA plan is like a universal blueprint to which the entire test team will adhere. Therefore, it is important to make information precise, clear, and comprehensible for everyone, without compromising on quality. The plan must include such sections as a list of testing phases with short descriptions of each, the scope of QA activities required for the AI product, and a clear hierarchy with defined responsibilities for each team member, such as the team lead, data scientists, QA engineers, and project managers.

Integrating QA into the development process

By using the QA plan as a universal manual, you can naturally integrate QA testing into the natural development process of an AI product. First of all, the QA process must be continuous, with automated tests going at all times and verifying that code changes do not cause any gaps or bugs in the performance of an AI. Secondly, you can refer to AI to automate some especially demanding or repetitive parts of the QA process so that the team members can concentrate on other tasks that require more precision and a human touch.

Finally, it is crucial to constantly exchange feedback between the QA and development teams to ensure that everyone stays on the same page and that the AI product is being constantly improved.

Getting the skills needed for QA tests of AI solutions

In order to test AI models properly, a CTO must know how to attract a specific skill set optimal for testing AI-based solutions of any complexity. In addition to sufficient technical knowledge, it is also important to have previous experience with AI-based product checks. This experience will demonstrate how skilled and expert is in working with different kinds of software. Therefore, as you are choosing among the candidates for your own dedicated QA team, go through the following list of skills needed for proper AI model testing.

Ultimately, businesses have the option to build their QA team in-house by recruiting professionals directly or to partner with companies like DeviQA, where you can access a pool of highly skilled QA experts ready to meet your needs.

Utilizing the power of QA outsourcing vendors comes in various ways, including QA outsourcing, dedicated QA team, or QA consulting. Each approach has its advantages and can be tailored to fit the specific needs of your project.

Technical know-how

Any credible AI QA tester must have enough tech knowledge about the fundamentals of AI and the way it works in different contexts and for different purposes. In addition to that, it is important to use different QA testing tools properly, such as automation testing tools like Selenium, Appium, or TestComplete, performance testing tools like JMeter or LoadRunner, API tools like Postman or SoapUI, continuous integration tools like Jenkins or CircleCI, and bug tracking tools like Jira or TestRail.

Working with data

Data is an integral part of any AI product, and a QA tester must know how to collect, process, and safeguard it properly. It is vital to be proficient in gathering data, evaluating it, and properly using it for the AI product. Some companies might require their AI products to deal with large sets of data so the tester must be familiar with the technology needed to deal with big amounts of data at the same time without negatively impacting the performance of an AI.

Knowledge of standards and laws

Apart from technical expertise, a good AI tester must be aware of the AI testing standards and requirements for a particular industry or niche. In addition to that, a confident knowledge of regulatory and ethical rules for AI deployment on national or supranational levels is required. This will help ensure that the AI product is not just working properly but also aligns with worldwide standards and legal expectations.

Strategic testing approach

In many cases, it might be required to work with custom testing strategies for AI, as every product is different and is created for various business purposes. Dynamic testing is a complex process with many nuances, and the knowledge of different frameworks is paramount. In addition, before launching any testing process, a team must draft a sufficient plan that will involve all the steps of the procedure and a list of must-have tools.

Performance under stress

When an AI product gets deployed for the first time, the checking process is particularly tedious and dynamic, with requirements changing and possible bugs occurring at any step of the procedure. Therefore, a QA tester must be able to respond to any issues promptly, troubleshoot effectively, and know how to detect the root cause of the problems.

Soft skills

Like any other part of the development process, QA testing requires a number of soft skills the lack of which can complicate the process for all members of the team alike. It is important to be resilient, open to feedback and criticism, and know how to communicate with fellow team members and other departments of the company efficiently. Even in the most automated processes, human impact is still paramount, and harmonious collaboration with different members of the hierarchy is what will drive the testing process to success.

Conclusion: The future of AI in QA

All in all, QA for AI systems is critical, as it ensures the functionality, reliability, and compliance of AI products for businesses. Given the rapid growth of the AI market — expected to exceed $826 billion by 2030 — and its increasing adoption across industries, CTOs with their QA teams must prioritize effective QA strategies for verifying the quality of their AI models before they go live.

AI has become a universal helping hand across multiple spheres of human activity — however, this complex system is yet to show its potential. Testing performed by skilled QA specialists with cross-sector experience will shape your AI product to its best possible form, ensuring that all its components work as programmed, contributing to the success of your business procedures.

DeviQA is a vast team of experts in AI quality assurance, with experience in testing different kinds of AI systems. If you are on the path to deploying your own AI business product, reach out for a consultation on the best testing practices tailored to your project plan.