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Globally, AI is being used to drive improvements in the following areas: risk modeling and analytics, customer service automation, predictive analytics for equipment maintenance, marketing automation, and customer segmentation, to name a few.

The ROI for AI initiatives is calculated based on the impact on cost savings or revenue growth. According to the McKinsey survey, the impact on cost savings and improved operational efficiency is directly apparent. Leaders are able to clearly see the value and ROI of their spending on AI, ML, and
intelligent automation. 87% of the leaders who responded to the McKinsey survey said they were able to get at least 10% in cost savings in service operations and manufacturing operations because of AI adoption. 51% believed they were able to get >20% in cost savings in service operations.

The value of AI is applicable in another scenario wherein a task is repetitive and needs to be performed. The ‘next step’ is fairly predictable given certain conditions or constraints. In such cases, it truly makes sense to automate using an AI-enabled tool. For example, from a quality standpoint, let us say a stream of complaints data is coming in. Using AI, it is possible to categorize incoming complaints and feed them into a particular (and relevant) CAPA workflow based on certain pre-defined conditions. Just to be sure, it may need a “human-in-the-loop” for quick approvals but even that can be avoided.

At ComplianceQuest, our AI/ML and data science team have worked closely with quality leaders to understand where AI will drive value. We took the time to carefully understand what slows down quality, safety and regulatory affairs (RA) teams, and whether we can use technology to tackle some of these challenges.

In this whitepaper, we talk about Applied AI use cases and how CQ.AI has improved quality performance across industries.