Explainable AI (XAI) and enterprise Quality management systems

Adoption of AI across enterprises, brings in a significant need to deploy trustworthy AI-led solutions has become paramount. Country-based regulations are constantly unfolding with stringent guardrails around AI solutions. While the enthusiasm continues around the AI outputs, key is to ascertain reliable and dependable results. As relevant to humans, AI also needs to make the workings transparent to instill User and customer confidence. With every passing day embracing the application of AI use cases, probability of undesirable results is multiplying leading to unimaginable impact on the business and society. Therefore, it is inevitable to embrace all-encompassing framework to demonstrate the quality of AI solutions.

Explainable AI – An imperative

Explainable AI (XAI) ensures trustworthy, ethical AI by enhancing transparency, fairness, and accountability. Frameworks like LIME, SHAP, and AIX360 help clarify AI decisions, support compliance, and build stakeholder confidence. Using such frameworks leads to high quality AI solutions not only catering to a set of applicable guardrails but also building much needed confidence to all types of relevant stakeholders.
Quality management system – A differentiating enabler

For an enterprise to be successful, key aspects include consistency, cohesion, collaboration, and comprehensiveness. Systematic and structured methods lead to consistent outcomes. To ensure stable performance outcomes, it is essential for organisations to expend concentrated efforts to build high-quality capabilities around their core competency areas. Companies can leverage Quality management system to devise delivery and execution approach to make their customers successful in their business.
Explainable AI+QMS – A unique compelling combo

While Explainable AI drives AI-led solutions to a space of understandable, interpretable white box paradigm, the Quality management system institutionalises the inclusive quality across the enterprise. The combination can be extremely enriching and value adding. Enterprise Quality management system need to be designed to embrace Explainable AI tenets to promulgate high-quality and reliable AI solutions. While organisations uses AI as an enabler or a driver for solutionisation, it is critical to apply necessary guardrails to validate the explainability quotient.
Tying the knots

The convergence of XAI and QMS is not merely an option but an essential strategic imperative for organisations aiming to develop and deploy high-quality and reliable AI solutions. By embedding explainability into every stage of the AI lifecycle within a disciplined quality management system, enterprise can enhance trusted adoption, improved accountability, ensure ethical and fair AI, accelerating debugging, regulatory compliance and drive continual innovation.