Is your quality management strategy behind the AI curve?

With the explosion of new AI techniques in headlines over the past year, many quality leaders feel behind on their own AI journeys. According to a Gartner survey conducted in 2021, 42% of quality leaders indicated they did not plan to invest in AI. In 2023, when asked the same question, only 2% of quality leaders responded no.

As the benefits of AI on quality management have become better known, leaders have clearly taken note. The benefits can be significant such as helping increase efficiency by exceeding performance goals with fewer resources and reducing overall cost. Such use cases also manage risk when successfully deployed by reducing warranty costs, recalls, yield holds and customer complaints. Some cases even show AI bringing in new revenue streams, if implemented correctly.

Yet despite the uptick in investment plans, there are many assumptions about AI for quality management and confusion about how to start. When it comes to AI’s use in quality management, not all these assumptions are true.

Given the impact of AI use cases on quality management, it’s important to separate fact from fiction. Here are the two most common assumptions:

Assumption 1: Our organization is behind our peers in adopting AI

Many organizations feel pressure to adopt AI. Because of that pressure, they make investment plans and begin exploring use cases, fearing that they are behind their peers. In reality, leaders tend to overestimate the progress that their peers have made with AI.

According to Gartner research, only 27% of quality organizations report fully deploying and using AI across the entire organization. Fully deployed means that the technology is used in a variety of sites across the network, as opposed to just one. This means most peer organizations are still in the nascent stages of AI adoption.

Leaders can breathe a sigh of relief knowing that they aren’t alone if they’re still in the piloting phase with AI. With the seemingly constant reporting of new AI techniques in broader new headlines, it is natural to assume that AI among quality organizations is more common than we think.

Assumption 2: AI will bring instant wins for my organization

There are many benefits to using AI, and quality leaders can often assume that these benefits will be nearly immediate.

In conducting research for prominent AI use cases in quality management, 15 foundational use cases were identified by Gartner. Gartner analysts scored and aggregated these use cases along several dimensions to calculate implementation feasibility and business value. Once scores were finalized, a surprising finding emerged. None of the use cases scored as a “likely win” for organizations—meaning, no use case identified is seen as having both high business value and high feasibility.

The low deployment of AI across organizations makes sense, as there is no such thing as an easy win when it comes to AI in quality management. Therefore, leaders should not go about trying to cherry-pick what may seem like an easier AI use case for a quick win. The fact is, for quality organizations looking to also manage risk and increase efficiencies of their operations, the path to AI will involve significant planning to achieve results.

The reality: Where AI in quality stands today

Organizations that have yet to implement AI should not feel behind or pressured to make a quick investment. For many, urgent issues like supply disruptions, regulations and major financial constraints have taken precedence in recent years over strategic investments, such as AI. Now is the time though to start developing a plan and experimenting.

To begin, implementing AI necessitates effective data management and collaboration across functions at nearly every step of implementation—an area where quality leaders have historically faced difficulties. To overcome these challenges, quality leaders need a blend of talent proficient in handling data, strategic investments, thoughtful planning and a commitment to organizational change.

It’s also crucial to gain an understanding of exactly which use cases to pursue. AI can be embedded in use cases that span regulatory, supplier, design, manufacturing, logistics and postmarket surveillance functions that will drive value. By taking a focused approach, organizations can map a strategy that puts their organization on a path to achieve results with AI.

About the author:

Arnela Iseric, is a research principal on the Quality research team in Gartner's Supply Chain Practice. She works with functional leaders and their teams to conduct in-depth research of their pressing challenges, surfacing evidenced best practices and practical tools practitioners can put to their own use.

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