Why Is Quality Assurance important in implementing AI and ML initiatives?

Quality Assurance

The role of Artificial Intelligence and Machine Learning has been growing steadily in business and our daily lives. These technologies are no longer the preserve of science fiction but have been implemented in business operations. You might have a brush with them while shopping on Amazon (by getting recommendations,) receiving ‘likeminded’ feeds on the social media, or asking Siri, Cortana or Google Assistant to play a specific piece of music. In case these appear to be the more visible ways in which AI or ML work, there are many areas where these technologies function in the background. For example, finding out the credit worthiness of bank customers, filtering spam, categorising emails, working as chatbots on websites to resolve customer queries, dynamic price optimization, detecting frauds in online transactions, curating content, pattern and image recognition among many others.

Notwithstanding the adoption of AI and ML technologies, these are still early days. Businesses are not much into the understanding of technical aspects or capabilities of AI/ML but look towards creating a strategy and finetuning the processes for their adoption. Our quest to find smart software to transform the way we work, manufacture, communicate, manage, and entertain among others has meant the leveraging of AI and ML initiatives. Since a lot of critical operations have embraced these technologies, ensuring their quality has become a crucial factor. This is important as the responses of AI/ML-based systems can be unpredictable and differ with the passage of time. Also, testing and validating advanced software have become increasingly important due to their growing usage in running critical functions of businesses. 

Benefits of AI and ML

AI and ML are looked at with caution in certain sectors of the economy as they are predicted to take away a number of low and middle level jobs thanks to innovation and the use of smart software. However, on the positive side, business stakeholders credit these technologies with the capability to improve efficiency, productivity, and the quality of products and service deliverables. Furthermore, these new age technologies have the potential to create new types of skilled jobs not only in management but in every tier of work.

AI-based application testing

Today’s software development processes are needed to deliver a large number of quality products within a short turnaround time. However, bringing out more number of software applications within the constraints of time might end up sacrificing with their quality. This is where AI and ML can complement the Agile-DevOps methodology of SDLC by implementing QA software testing. These can test large datasets and identify the non-conforming ones among the datasets. Also, by studying the patterns of a known process or user behaviour, these technologies can detect glitches as well as predict trends far quicker than any other methodology.

Key areas for implementing AI and ML initiatives

Enhanced customer and employee experience: AI and ML should not be merely aimed at achieving efficiency, increasing revenue and reducing cost. They should be used to enhance the customer and employee experience by identifying glitches and predicting customer needs. The strategies to implement AI and ML should take a human centric view to understand how they can enhance the overall customer experience.

Create governance structures: The management or business stakeholders need to understand the changing market dynamics and customer behaviour. The knowledge is important to formulate strategies in the areas of sales and marketing, administration, technology, human resource etc to drive growth. AI and ML can be used to design effective human machine interfaces to deliver personalised, relevant, accurate and data driven results for customer queries. And to ensure the interface delivers better business outcomes, it should undergo a rigorous quality assurance exercise.

Faster fraud detection: The vertical of financial services has to grapple with the menace of frauds. Banks often employ human resource to manually check thousands of cheques by looking at their scanned images. This modus operandi is plagued with inefficiency and inaccuracy besides being time consuming. On the other hand, AI and ML technologies can be used to automatically validate the accuracy of scanned images of cheques by comparing them with a database containing signs that are deemed to be fraudulent. This way, the system can identify counterfeit cheques in real time thereby giving the opportunity to apprehend the fraudster(s).

Reasons to engage software quality assurance to implement AI and ML initiatives

# Regulatory requirements: The wide scope of implementing AI and ML initiatives has necessitated the processes or software to conform to international quality protocols and regulations. These include IEC, HIPAA, PCI among others. To ensure the compliance of AI and ML-led business processes and software applications with international regulations, the same should be subjected to a rigorous QA software testing exercise. This will help to pre-empt hacking and saving the processes/software from viruses, trojans, and malware.

# User experience: Intense competition has led customers to adopt applications that provide the best user experience. Software quality assurance services ensure the applications using AI and ML work to their optimum and provide a rich customer experience. The areas to check include performance, usability, functionality, and security among others.

# Quality: The AI and ML initiatives are credited with streamlining processes, improving efficiency and reducing waste. However, unless the processes are tested beforehand through the AI-based application testing, the deliverables will not meet the stated business objectives. Thus, the glitches or bugs present in the processes should be eliminated to make the application qualitatively superior.

# Continuous Delivery and ROI: The Agile-DevOps environment requires a quality application to be delivered quickly and kept updated during its entire lifecycle. The AI and ML-based applications should function seamlessly across devices, networks, operating systems, platforms and frameworks leading to a rich customer experience and ROI. Thus, by engaging software quality assurance services, the above objectives can be achieved.

Conclusion

The power of AI and ML technologies in realising a host of business objectives is well established. The adoption of these new age technologies helps to keep a business entity ahead of the competition and secure for its customers a rich experience. Implementing QA testing, thus, has become the sine-qua-non for businesses to achieve ROI.

Diya works for Cigniti Technologies, which is the world’s first Independent Software Testing Company to be appraised at CMMI-SVC v1.3, Maturity Level 5, and is also ISO 9001:2015 & ISO 27001:2013 certified.