Humans: The Real Superheroes of Artificial Intelligence (AI) in Quality Management
Wharton University’s Mack Institute for Innovation Management published an article on the value of bringing in human creativity to derive maximum value from AI investments.
The article highlighted the role of semi-autonomous decision-making, wherein AI and ML-enabled software applications drive efficiency into complex processes, help cull data with ease, and saves time for leaders, but the eventual decision itself will be better with a human-in-the-loop.
AI + Humans = Better, Faster Decision Making
Let’s take, for example, a Complaints Handling specialist working for a medical device company. She receives a note from a customer saying that there has been an electrical malfunction in the medical device. As soon as the complaint comes in, there are several key processes that might need to be completed. These steps include:
- Complaint Intake
- Triaging and Initiation
- Roping in key stakeholders and collaborators
- Regulatory assessments and decision-trees
- Regulatory submissions (if needed)
- Product history review
- Identification of similar complaints from the past
- Investigation and Root cause analysis
- CAPA evaluation
- Next Steps and Actions to be taken
In the above steps, there is tremendous potential to use AI-enabled automation to drive efficiency into the process of handling the complaint. For instance, if the company were to use ComplianceQuest’s Complaints Management Solution, the following will be automated –
Identification of Duplicate Complaints
Often, the same complaint is taken in by multiple agents. If your AI-enabled tool can automatically detect duplicate complaints, you can save time on manual data entry and redundant work.
Spotting Similar Records
There may be similarities between different complaints which can signal a larger risk to product quality or customer safety. In the case of CQ’s Complaints Management Solution, such similar complaints are automatically detected and shown to complaints specialists. In fact, the tool assigns a score on how similar two complaints are. This makes it easy when it comes to figuring out if there is a trend to be noticed and if an investigation is needed.
Automatic Recommendation of Risk Level
With the right AI model, it is possible to provide intelligent recommendations on how quickly the complaint needs to be handled based on the risk level: Is a product recall needed? Or, is it not a low-risk issue? Based on past data, past actions taken and insights, the tool is able to make a recommendation on what risk level should be assigned to new complaints.
The AI-powered platform can also automatically categorize and classify the complaint.
Thanks to all these capabilities, it’s a no-brainer to adopt an EQMS solution with AI-enabled features. It helps save time, especially when quick decisions need to be made. But that’s only half the battle won.
The real value of AI comes in when the right set of decision-makers and stakeholders are able to access these insights, and then take a call on the next steps based on their own experience. This convergence of AI and people holds the key to better and more effective decisions.
In the complaints handling process mentioned above, various stakeholders have to come together to decide if an investigation and further root cause analysis of the complaint is needed.
The questions that need to be asked and answered by key stakeholders are –
- How can we avoid this complaint in the future? What should our CAPA management process look like?
- Should we involve our engineering team in the 5 Why RCA discussion?
- Should we reach out to the supplier whose part may have caused the malfunction?
- Do we need a better supplier quality management process?
- How could we have caught this issue before the product was shipped?
- Does our QMS need an upgrade?
- How can we be more proactive in our efforts to improve quality performance?
There are several similar questions that need to be addressed. With the help of AI-enabled tools, we’re able to get to these questions faster, without wasting time on data entry, data analysis, and other time-consuming processes.
As McKinsey aptly describes in this article ‘Winning in Automation requires a focus on Humans’. AI, ML, and advanced analytics features in the EQMS solution can certainly help quality leaders save time and make their lives easier. But, AI features by themselves will not solve quality problems or elevate quality performance. It is what you do with the insights and findings that matter.
The need of the hour is to implement a next-generation AI-enabled EQMS solution, but also take the efforts to build a culture where time saved by using these technologies can be put to good use and where human ingenuity is brought to the fore. At the end of the day, quality performance matters. Technologies like AI are only enablers. It’s us people who have to make it happen!
To get a deep dive into how ComplianceQuest EQMS leverages the power of AI and humans – in a holistic fashion – request a demo here: https://www.compliancequest.com/lp/applied-ai/