Work Intelligently

Pathway to Intelligence Continuum: Evolving from Quality Assurance to Assured AI

  28 May 2024

In the fast-paced realm of digital transformation, the role of software testing is pivotal. It's not just about finding bugs; it's about foreseeing potential challenges and ensuring software not only meets the current demands but also anticipates future needs. At Intellificial, we've pioneered a pathway from basic Quality Assurance (QA) to advanced Assured AI. This progression not only boosts operational efficiency but adheres to our S3 framework—Scalable, Secure, Sustainable—and integrates the principles of Activated Intelligence.


The Intelligence Continuum in Software Testing

The graph attached shows how quality practices change over time—starting with Quality Assurance, moving to Quality Engineering, combining QE with AI, and reaching Assured AI. The horizontal axis shows that as maturity increases, the focus changes from detecting defects to avoiding and forecasting defects. As the organizations have more and more AI powered applications in their ecosystem, they will need to make sure that these AI systems are reliable and produce output that follows the ethical and fairness guidelines adopted by the organisation.


Quality Assurance (QA): Testing Output to Find Defects

Traditional QA is the baseline, focusing on defect identification before products go live. For instance, in the retail industry, QA ensures that e-commerce platforms handle peak traffic during sales without crashing, safeguarding the user experience. However, it's often a reactive measure—finding and fixing issues without enhancing the development process.


Quality Engineering (QE): Testing Process to Prevent Defects

Quality Engineering elevates QA by embedding quality into every stage of the software development lifecycle. This proactive approach doesn't just find defects; it aims to prevent them. For example, a financial services company might implement QE to ensure that their new customer portal not only functions correctly under normal conditions but also maintains data integrity and performance under high demand, such as during tax season or high trading periods. This reduces downtime and operational costs, which are crucial in high-stakes environments.


QE with AI: Testing with Intelligence to Predict Defect and Optimise Resources

Integrating AI with QE transforms the testing landscape by employing machine learning to develop intelligent, adaptive testing processes that reduces the maintenance effort. For instance, in retail, an AI-enhanced testing system could automatically adjust test scripts for checking product inventory levels online, even as new items are added or removed, ensuring accuracy without manual oversight. The focus now shifts to predicting defects based on previous learning and analysis of code changes and helps in directing resources to cover high-risk areas.


Statistics indicate that AI-enhanced testing can improve defect detection rates by up to 45% and reduce manual testing labour by as much as 20% (Source: Pan Industry QA Survey, 2021).


Assured AI: Testing Intelligence to Ensure Responsible AI

Assured AI represents the zenith of the Intelligence Continuum, focusing on ensuring AI systems are not only effective but also ethically aligned and compliant with industry standards. In financial services, Assured AI would ensure that algorithms used for credit scoring are transparent and do not inadvertently discriminate against any group. This level of assurance is critical for maintaining trust and adhering to regulatory requirements.


Integrating the Continuum into Your Business


Assess Current Maturity

Begin by assessing where your business is in the continuum. There is no universal solution here, as different business units or processes within the same organisation may have different levels of QA maturity. This is perfectly acceptable. The goal should be to advance to a more mature state irrespective of the start point. For a retail company, this could mean evaluating how well current QA processes are identifying and resolving customer experience issues during busy online shopping periods.


  1. Define Objectives - Determine what you aim to achieve at each level of maturity. A financial institution may set a goal to reduce its app downtime by 30% through proactive QE practices within the next fiscal year.
  2. Develop a Roadmap - Create a detailed plan that outlines how to move from QA to Assured AI. This could involve phased investments in AI technologies and training for your QA team.
  3. Leverage Technology - Implement the necessary technologies that facilitate this transition. For example, introducing AI tools that can predict and adapt to changes in software environments.
  4. Educate and Train - Ensure your team is trained on the latest advancements in AI and software testing. Continuous learning is crucial for maintaining an edge in technology-driven industries.
  5. Measure and Optimise - Regularly measure the impact of your initiatives and refine strategies as necessary. Metrics might include the rate of defects found and fixed, downtime reductions, and improvements in customer satisfaction scores.


By following this pathway, businesses can ensure they are not merely adapting to changes but are ahead of the curve, driving innovation in their industries. Intellificial is committed to guiding enterprises through this journey, empowering them to harness the full potential of their software testing capabilities and achieve unprecedented growth and efficiency.


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