An Inquiry Of Practical Applications, Challenges And The Future
by W. Frazier Pruitt
Quality management (QM) is a critical aspect of any organization that strives to provide high-quality products or services to its customers. QM ensures that an organization’s products or services consistently meet or exceed customer expectations, and it involves various processes, tools and techniques to achieve this goal.
With advancing technology, artificial intelligence (AI) and natural language processing (NLP), algorithms have emerged as potential tools to help QM become more efficient and effective, leading to increased customer satisfaction and loyalty.
Like any new technology, however, there also are challenges and potential pitfalls that must be addressed for successful implementation. Here, we explore how AI and NLP can be used to improve QM, challenges in implementing AI and NLP in QM, and the future of AI and NLP in QM.
Case study
One example of applying AI and NLP in QM is the analysis of customer feedback and complaints. Traditionally, customer feedback and complaints have been analyzed manually, which can be time-consuming and prone to errors. With AI and NLP algorithms, however, customer feedback and complaints can be processed quickly and accurately, identifying common issues and pain points from data sources.1 This approach lets organizations to develop targeted solutions to address issues and improve the overall customer experience.
If customers complain about a product feature, for example, AI and NLP algorithms can identify this trend, giving the organization an opportunity to develop a solution to improve that feature, leading to increased customer satisfaction.
Moreover, this approach can lead to continuous improvement because organizations can use AI and NLP algorithms to analyze customer feedback and complaints over time, identifying emerging trends and issues. By addressing these issues promptly, organizations can prevent them from becoming major problems that could affect aggregate customer satisfaction and loyalty.
Another example of using AI and NLP in QM is the use of AI-powered chatbots for customer support and assistance. Chatbots can understand natural language queries and provide relevant responses, improving the customer experience. Such implementations already are being applied at scale.2
To date, the most prominent example is OpenAI’s NLP, ChatGPT, being incorporated into Microsoft’s Bing search engine. Applied to customer support, this approach can lead to increased efficiency because chatbots can handle simple queries and requests, freeing customer service representatives to focus on more complex issues. Chatbots can be available 24/7 to provide continuous customer support.
In addition to chatbots, AI and NLP can be used to improve the root cause corrective action process. When an incident occurs, AI and NLP algorithms can analyze incident reports and identify the problem’s root cause based on historical data sets or other relevant data sets.3 While AI systems may not replace all need for human problem solving, particularly in novel situations, these AI systems may prove to be effective in identifying and summarizing similar incidents.
By identifying the root cause, organizations can develop targeted solutions to prevent the incident from recurring, leading to continuous improvement and increased efficiency. Furthermore, AI algorithms can identify common causes of incidents across different incidents, helping organizations to address those issues proactively.
Challenges to address
As organizations continue to seek ways to improve their QM processes, the potential benefits of AI and NLP algorithms have become increasingly attractive. While AI and NLP have the potential to revolutionize QM, there are several challenges that must be addressed. Among these challenges are issues of fairness and bias, data quality and interpretability of decisions being made by AI-based systems.
The foremost challenge may be ensuring the algorithms used are fair and unbiased. Biased algorithms could lead to unfair decisions that could negatively affect customers. Bias can be introduced in AI and NLP algorithms if the algorithms are not properly developed and tested.
For instance, if a data set is not representative, the AI system may develop biases that affect the accuracy of its results. Organizations must be aware of this potential issue and take steps to ensure the fairness and accuracy of their algorithms.
Another challenge is ensuring data quality because AI and NLP algorithms rely on accurate and representative data to be effective. Accurate and representative data are critical to the success of AI and NLP algorithms. Data quality can be compromised, however, by many factors including data sources, data entry errors, as well as incomplete or outdated data. Organizations must have high-quality data so their AI and NLP algorithms produce accurate results.
Interpretability of AI-based systems also can become an issue because it can be challenging to understand how decisions are being made by AI-based systems. Understanding how decisions are being made, and identifying and addressing any issues can be challenging. Organizations must have the necessary tools and expertise to interpret and understand the results produced by their AI and NLP algorithms to control the risk of externalities.
Potential to revolutionize
Despite these and other challenges, the future of AI and NLP in QM is expected to be increasingly important. Organizations wanting to incorporate AI and NLP into their quality systems must develop a clear strategy for implementation. This includes identifying potential benefits and challenges of incorporating AI and NLP into QM.
The future benefit of incorporating AI and NLP into QM is continuous improvement and increased efficiency. Organizations can use AI and NLP algorithms to identify patterns and trends that may not be readily apparent, enabling them to develop targeted solutions to improve customer experience, increase efficiency and reduce errors. While successful implementation of AI and NLP in QM has been achieved in various industries such as healthcare, finance and manufacturing, the most impactful future applications may yet be unknown.
Incorporating AI and NLP into QM has the potential to provide organizations with a competitive advantage by improving their quality processes. It is crucial, however, to address the challenges including fairness and bias, data quality and interpretability. Organizations must develop a clear strategy for implementation to ensure the potential benefits are realized while addressing the potential challenges.
With the right approach, AI and NLP have the potential to revolutionize QM processes, leading to continuous improvement, and increased efficiency and customer satisfaction.
REFERENCES
- Sahar Moradizeyveh, “Intent Recognition in Conversational Recommender Systems,” arXiv preprint, Dec. 6, 2022.
- Mohit Jain, Pratyush Kumar, Ramachandra Kota and Shwetak N. Patel, “Evaluating and Informing the Design of Chatbots,” proceedings from the Designing Interactive Systems Conference 2018, Association for Computing Machinery, New York, pp. 895-906.
- Amparo Morant, Per-Olof Larsson-Kråik and Uday Kumar, “Data-Driven Model for Maintenance Decision Support: A Case Study of Railway Signaling Systems,” proceedings from the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit, May 14, 2014, Vol. 230, No. 1, pp. 220-234.
Editor’s Note:
This is an experimental article co-written by ChatGPT and W. Frazier Pruitt. While Pruitt provided engineered prompts and moderated the content, arguments and examples were brainstormed by GPT-3.5, and the article was outlined and drafted with GPT-3.5. References were sourced with GPT-4 using plug-ins that integrate with scholarly sources. As a result, the paper is aligned with Pruitt’s views but may not fully represent foremost research on the subject.
Article originally published in Quality Progress August 2024