If Your Company’s QA Maturity is Low, Forget about AI

Quality assurance (QA) generally (testing specifically ), plays a very important role in AI platform adoption. AI platform testing is complicated for the following reasons:
  1. Testing AI platform demands intelligent procedures, virtualized cloud tools, technical skills and AI-enabled tools.
  2. While AI platform vendors typically work towards rapid innovation and automatic updates of the products, the rate of business testing to accept product upgrades should be both fast.
  3. AI platform products generally lack transparency and interpretability. They are not easily explainable. Consequently, it’s hard to trust testing.

Modern QA shifts the use of testing from flaw detection to prevention. Additionally, the standard of AI is very much determined by the quality of the training models and the data used for training. Accordingly, unlike traditional SaaS testing versions that only concentrate on cloud tools, logic, interfaces and user configurations, AI testing must also cover areas like instruction, learning, reasoning, perceptions, manipulations, etc., based on the AI solution circumstance.

Within an AI-as-a-Service version, the AI algorithm is provided by a platform vendor; IT enterprises configure it by creating interfaces and providing data for training to increase end-customer trust of AI-based smart applications. Consequently, AI testing should cover the basic elements of data, algorithm, integration and user experiences.

Second, testing should validate the operational fitment of this solution within enterprise IT. It should certify the setup of an AI platform merchandise in the company ecosystem, such that the business purpose of AI adoption is fulfilled. It also needs to confirm the training model used to elevate the solution within an organizational construct.

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Thirdly, the approach that AI algorithm adapts — such as statistical procedures, computational intelligence, soft computing, symbolic AI etc. –must be addressed in the algorithm validation procedure. Although the solution is a black box, necessary coverage ought to be established to certify its validity.

Ultimately, the tools that AI logic applies — such as searchengine optimization, probability, economic models etc. — should be addressed in the functional validation procedure.

It’s crucial to apply the nuances of AI to each test component. By way of instance, data assurance strategy should deal with the complexities that AI solution would present concerning volume, velocity, variability, variety, and value of information. Below figure presents a sensible list of test attributes for test design factors.

QA maturity is critically important for a company to pick an AI platform solution. A high amount of test automation is important to success, without which enterprises can’t manage regular releases of the AI platform (as well as the goods inside ) as well as ongoing internal application releases.

The only way for you to cope up with the system changes which are happening within your business on the interfacing applications and information, and through the changes your AI vendor makes is to establish a constant (delivery and integration ) environment; the methodology issues . Hence check if your company has the necessary level of QA maturity. If you do not have it, fix it first before you think about AI.