The objective of this qualitative research study is to learn about the impact of AI on customer relationship management (CRM), specifically in the area of custo-mer service of problem resolution. The focus of this qualitative study is to conduct depth interviews using proper protocols on the relevant informants who have extensive real-world industry experiences with AI-driven CRMs. Since the adoption of AI in the CRM systems is a relatively new phenomenon, the qualita-tive research methodology will enable us to have an in-depth understanding of its behavior and impact and also better interact with the interviewees through an emergent design. In addition, the qualitative research provides an opportunity for us to examine existing theoretical and conceptual foundations in the AI-driven CRM context. The central questions of the study are designed to analyze the general, industry, and firm-specific impact of AI on customer service and problem resolution

1) What are the most remarkable features of AI used in the area of customer service and problem resolution? 

2) How did these AI features come about and what factors drove the implementation of these AI features?

3) What were the key factors that made the AI fea-tures impactful in the area of customer service and problem resolution? What were the key barriers that had to be overcome?

4) What is the nature and pace of the change in customer service and problem resolution that these AI-driven CRMs have produced? Will this pace of change slow down, remain the same, or accelerate over the next decade?

5) What role has the specific respondents firm played in the area and how has it assisted other firms? What key accomplishments and challen-ges have been encountered?

6) What are the specific steps or programs the firm has taken (or helped other firms) to support customer service and problem resolution using AI technologies?  

Also, the main threats posed by AI are job losses as a result (Siau & Wang, 2018; Siau & Yang, 2017) and the skills of these professionals may also shift from a series of hard-selling or simple customer service to soft skills such as the relationship building and emotional connectivity (Singh et al., 2019).  Therefore, this study also aims to learn the impact of AI on customer service job loss and change of job roles. 

1) What is the firm-specific or the industry-level impact of AI on service job loss and change of job roles of customer service personnel? 

2) How would AI change the job roles and required skills of customer service professionals? 

Conceptual Background

General Literature Review of AI in the CRM Space

Recent technological advances in Artificial Intelligence (AI) technologies, especially in the fields of machine learning, deep learning, neural networks and big data (Moreno and Redondo, 2016; Zhang et al., 2018), ubiquitous mobile computing (He et al., 2019) have fueled the growth of the next-generation digital plat-forms (Khalid et al., 2019; Rai et al., 2019; Zhang et al., 2019), which have progressively achieved human (sometimes super-human) level of performance in the various areas including autonomous driving, medical diagnosis (e.g., cancer screening), the robots/ drones, chatbots, virtual assistants, the language translation, the governance monitoring (e.g., copycats, content violation), complex game playing and recommendation systems. AI features embedded in customer relation-ship management (CRM) platforms also create new possibilities for customer experience, with rich insights into customer needs (Kumar Deb et al., 2018). These innovations have been driven by a manifold increase in processing power, lower-cost hardware, and the exploding creation and availability of customer data (Gantz et al., 2017). 

The qualitative depth interview employed by this study is based on McCracken, (1988) four-phase implement-tation framework, which calls for a careful review of “analytical categories and relationships” and “cultural categories and relationships”. This framework (Fig. 1) has been adapted by (Chakravarti & Crabbe, 2019) to fit a variety of ethnographic methods. 

Analytic Categories and Concepts

The analytic categories establish the domain of the investigation & organize the knowledge in the domain. In the specific domain of AI-driven CRM systems, a plethora of technologies, including machine learning (ML), deep learning (DL) or neural network, natural language processing (NLP), voice speech/ recognition, image recognition, and other tools are used to discover insights, identify patterns, answer inquiries, the solve problems and the provide recommendations. These technologies have produced effective applications for customer service & problem resolution, including Next Best Action (NBA) systems based on the predictive analytics, chatbots, virtual agents and agent assist sys-tems. The impacts of these AI technologies on the customer service & problem resolution are multifold, including operational excellence, customer intimacy, service productivity & quality, competitive advantage, service excellence, customer satisfaction, & ultimately, return on investment (ROI). These analytic categories are identified in the formal inter-view protocol as the elaboration probes and are used during the inquiry. The relationships of these analytic categories are the depicted in the following figure and are the further explored in the depth interview. 

Table 1 explains the analytic categories and concepts of AI technologies most relevant to the customer service and problem resolution context. Some of these key technical terms appeared frequently in the answers from various informants during the interviews. 

Table 1: Analytic Categories and Concepts.

Cultural Categories and Concepts

The cultural categories call for a detailed inventory of the key features of the researchers experience with the focal topic (Chakravarti & Crabbe, 2019, p. 75). This also allows recognition and admittance of matches and mismatches with other cultural categories which may merge from the respondents subjective experience (McCracken, 1988). As a former practitioner in the management and IT consulting industry, my personal experience tells us that it is crucially important to assess the cultural categories from the industry and functional area perspectives. In the domain of AI-driven CRMs, the following are the cultural categories identified based on the major stakeholders in the CRM ecosystem (the functional cultures) for this specific study. The depth interview questions and probes are designed to analyze the relationships among these entities. 

1) AI leaders conduct fundamental AI research and development, e.g., Google, Microsoft, Amazon, and IBM.

2) CRM vendors produce the AI-driven applications utilizing AI leaders frameworks or the research work, e.g., Salesforce, NetSuite, and SAP.

3) CRM implementers are the usually IT consulting firms partnered with multiple CRM vendors, e.g., Accenture, Deloitte & Touche, and Perficient.

4) CRM Facilitators are usually technology evange-lists from AI leaders, CRM vendors, & imple-menters, who coach and train customers and advocate the adoption of AI-driven CRMs, e.g., Microsoft IT evangelists, IBM Watson consul-tants, Salesforce Einstein Analytics & Discovery consultants, and Perficient AI consultants.

5) CRM Followers adopt the AI frameworks or applications from the leaders and vendors once the tools are well established. Both CRM imple-menters & customers can fall under this category. 

6) CRM customers/clients are companies of various sizes that use CRM systems to serve their own users. 

Qualitative Study Design

Available Methods

Compared with other research methods in which res-earchers hypotheses and the procedures are predeter-mined, the research design in qualitative research remains flexible both before and throughout the actual research (Marshall & Rossman, 2014). It encompasses a variety of accepted methods and structures, including four major types which are the most commonly used. They are summarized in Table 2.

Table 2: Qualitative Study Design Methods, Adapted from Astalin, (2013, pp. 120-122).

Chosen Method

This qualitative study aims to the conduct personal interviews with four industry experts to gain insights into the impact of AI technologies on customer service and the problem resolution. Therefore, the proposed research method for this study is an interactive depth interview which is a commonly used ethnographic data collection technique. Moreover, following Brinkmann, (2016) interview guidelines, this study has chosen the semi-structured interview method among the spectrum of different types of the interviews (i.e., structured, unstructured, & semi-structured). The semi-structured interview is defined as “an interview with the purpose of descriptions of life world of the interviewee in order to interpret the meaning of described phenomena” (Brinkmann & Kvale, 2015, p. 6). Compared with the structured and unstructured interviews, the semi-structured interview has the following characteristics (Cohen & Crabtree, 2006), which fit the requirements of this study well. 

1) The interviewer and respondents engage in a formal interview. 

2) The interviewer designs and uses a formal interview protocol, which is a list of questions and topics that need to be covered during the conversation, usually in a particular order. 

3) The interviewer follows the protocol but is able to follow topical trajectories in the conversation that may stray from the guide when he or she feels this is appropriate.

Interview protocol

Preliminary Draft

The preliminary interview protocol was the developed based on the central research questions of this study, a careful literature review of the use of AI in the space of customer service and problem resolution, and an assessment of the professional backgrounds of the res-pondents so that the scope of topics are appropriate for the interactive depth interview. In addition, elaboration probes are developed to the facilitate data collection according to the proposed analytic and the cultural categories in the section 3. The preliminary inter-view protocol was initially tested with a senior product manager with the Coupa Software and responsible for delivering a roadmap for the search and shopping module within the larger Coupa platform suite. Based on the initial interview observations and the expert feedback, we made the following adjustments to the preliminary interview protocol.

1) For Q1, the time frame was specified to be “the last 10 years” of the AI implementation in CRMs to avoid any confusion about earlier AI features used in legacy systems; the scope of implement-tation was specified to be the general application space for various businesses to avoid potential confusion with firm-specific implementation. 

2) For Q2, the question was made more concise by removing “to the best of your knowledge", which was a given condition. 

3) For Q5, the question was modified to distinguish between the firm-specific versus industry-level impacts on jobs and change of job roles.

4) The time checks were also adjusted accordingly based on the initial test observations. 

Final Interview Protocol

The final interview protocol (Appendix A) is com-prised of six questions in a particular order to facilitate the flow of the interview and also standardize the data to be collected from the respondents. Elaboration probes are developed in case topical trajectories in the conversation stray from the inter-view guide. Standard introduction and closing statements are also used to ensure the formality and consistency of the interview. Following Brinkmanns, (2016) guidelines, the final interview questions are designed with a purpose and refined to invite respondents to give descriptions of their experiences of and insights (lifeworld) into the AI-driven CRM space and seek to obtain data for interpretation of meaning. The key strength of this formal interview protocol is that it has adhered to the guidelines and best practices of the semi-structured interview. This allows the interviewer to be prepared and appear competent during the interview while giving the informants the freedom to express their views on their own terms (Cohen & Crabtree, 2006). With a standardized set of questions, it can provide reliable, consistent, and comparable qualitative data from respondents across different functional domains and various professional levels. In the meantime, the interactive elaboration probes offer flexibility in discovering analytic and cultural categories and an opportunity for identifying new ways of seeing and understanding the topic at hand. Besides the strengths mentioned above, there are also several weaknesses of this interview protocol. First, the open-ended questions are difficult to analyze due to the varied responses from respondents. Second, the flexibility of using the elaboration probes in the interview may reduce reli-ability, as respondents may not receive the same probe questions and it would be hard to compare answers. Third, in order to get enough data from respondents during the one-hour-long interview, the questions are designed to include subparts, which makes it cumber-some for respondents to remember to answer all parts of a specific question. 

Informant Selection Process/Criteria

In order to obtain reliable and useful data for this qualitative study, the informants of this study are the selected based on the following criteria.

1) Who has relevant and useful information? Infor-mants should have extensive experience and knowledge in the AI-driven CRM space. 

2) Who is interested and available? Informants should be interested in the research topic and be available for a one-hour-long interview.  

3) Who is willing to provide reliable information? Informants should have a good professional reputation and be willing to share their honest answers to the interview questions. 

Based on these three key criteria, we selected four industry professionals for the final interview from my own professional network, which represents a good mix of professional levels (2 executive levels and 2 middle management levels), cultural domains (3 from the U.S. and 1 from China), functional sectors (3 from consulting firms, 1 from an e-commerce company). All four respondents have agreed to share their names in the study. 

Informant Description

Respondent 1 

Respondent 1 is the Chief Strategist of the Customer Experience Platforms at Perficient, Inc. He has more than 35 years of the strategic technology advising experience. He and his team build great customer, partner, and employee experiences. His primary focus is on the actual technology used in a customer ex-perience environment and takes into account his experience with digital platforms such as web content management, portals, search systems, the marketing platforms, and the marketing systems. Looking to the future, he and his team are expanding their focus to understand how emerging technologies, such as the artificial intelligence (AI), relate to those customer experience platforms. Its quite a broad range of the technology, but the common thread is they all connect with the customer. His primary role is to help his clients understand the technology that they need to have in place, why its important, and how it integrates and affects all parts of their business. He has a B.S. in Computer Science from Purdue University and an M.B.A. from the Cleveland State University (Adapted from respondents Perficient profile and respondent-supplied information, 2019).

Respondent 2

Respondent 2 is the practice leader and Chief Strategist for artificial intelligence (AI) at Perficient, Inc. She brings a background in the analytics and unstructured information management to design and deliver trans-formative AI solutions. Her engineering background aids in her analysis of complex business problems and her ability to the develop innovative cognitive appli-cations. She and her team help clients uncover hidden insights, the create differentiated experiences, identify trends and anomalies, enhance existing applications, and scale their knowledge in AI. She created an award-winning center of excellence for AI and participated in a panel with IBM CEO Ginni Rometty to share her knowledge on the AI solutions and strategy. She also frequently demonstrates thought leadership through speaking engagements at various industry and techno-logy conferences, such as the AI Summit. She has two engineering degrees, including a B.S. in Engineering Physics from the Taylor University and an M.S. in Mechanical Engineering from the Columbia University (Adapted from respondents Perficient profile, 2019). 

Respondent 3

Respondent 3 is a management consultant within the Talent & Organization practice at Accenture, servicing clients within the Utilities & Resources industry. His current role at Accenture is Analytics and Business Readiness Lead, responsible for the monitoring and reporting on the progress/state of the overall transfor-mation using various data analytics and the metrics tracking tools. His current project is at a leading US utility company implementing a new customer inter-action software across their regional call centers. Before joining Accenture, He was a senior consultant at Capgemini, where he focused on the digital trans-formations using technologies such as NetSuite and Salesforce. He later shifted to talent and organizational effectiveness work with a heavy emphasis on mergers and integrations, talent learning and development, and change management. He has a B.S. in the Information Systems from Marietta College (Adapted from respon-dent-supplied information, 2019). 

Respondent 4

Respondent 4 is a data analyst from the Chief Cus-tomer Office (CCO) department at the Alibabas head-quarters in Hangzhou, China. Alibaba is a Chinese multinational conglomerate holding company special-izing in e-commerce, retail, Internet, and technology. His current role is focused on improving the customer experience for more than 20 million users of the Ali Express, an online retail service owned by Alibaba. Currently, 70% of employees working at the CCO are computer engineers. He and his team embed artificial intelligence into customer services to the serve both buyers and sellers. This system has helped to reduce more than 100 thousand customer agents for the plat-form. He has a B.S. in the Information Systems from Marietta College and an M.S. in Management Inform-ation Systems from Texas Tech University (Adapted from respondent-supplied information, 2019).  

Nature of Material Collected

The materials collected from respondents are mostly non-numerical, the descriptive data and therefore, not appropriate for quantitative analysis using advanced statistical tools. Most of the descriptive data are fairly current, with only a small number of the references to historical contexts. None of the materials collected are deemed classified, private use only, or confidential. Most of the materials collected from the respondents are the experience-based while some of them are knowledge-based derived from other industry sources (e.g., Gartner, CIO Daily). Some materials are busi-ness-oriented in the nature, especially during the discussions on the demand-side of the AI features. Some materials, on the other hand, are technical in nature with many acronyms used. These technical acronyms are manually noted in the full interview transcripts (Appendix B). 

General Interpretative Approach

Qualitative data analysis entails certain distinct active-ties. The first and most important one is the ongoing discovery, which is about identifying themes and the developing concepts and propositions (Taylor et al., 2015). Following Brinkmann, (2016) guidelines on the semi-structured interviews, this study has adopted a general interpretative approach and conducted a broach thematic analysis on the interview transcripts. Taylor et al. (2015) recommend identifying themes by the thoroughly exploring the data & propose the following steps to maintain focus.

Fig. 3: Developing Themes and Concepts (Taylor et al., 2015).

After reading and rereading data collected from the respondents, we kept track of the hunches, the inter-pretations, and ideas by using the Microsoft OneNote, which allowed us to write down ideas anytime and anywhere. We looked for broad themes and the major topics that emerged from the interview transcripts and developed a coding scheme (Fig. 4) to analyze the data. One key observation that has emerged from the interviews is the dynamic relationship between the demand-side (CRM system customers/clients) and the supply-side (technology providers) of the implement-ation of AI features. Based on the coding template suggested by (Taylor et al., 2015, p. 184), I modeled my coding scheme to the highlight the cultural and analytic categories that frequently appeared in the respondents accounts of their experiences (Fig. 4). The coding scheme is applied using a red font to some sections in the transcripts (Appendix B) to demonstrate the process.

Emergent Themes 

Cultural Categories

The cultural categories emerged when all four (4) respondents were talking about the key stakeholders in the AI-driven CRM space, especially the AI leaders, CRM vendors, CRM implementers, & CRM customers/ clients. One emergent theme is that CRM customers/ clients want AI features in the CRM system in order to achieve their strategic goals, such as the reducing operational costs and improving customer satisfaction. These are key demand-side factors that drive the implementation of AI features in CRM systems.   From the supply-side perspective, one emergent theme is that CRM vendors (e.g., Salesforce, NetSuite, SAP) are the leveraging the tools and platforms from AI leaders (e.g., Google, Microsoft, Amazon, IBM) that conduct fundamental research & development in AI techno-logies. These tools are increasingly accessible through PaaS (platform as a service). The CRM vendors either embed the AI capabilities in their own applications or integrate with the AI leaders platforms (e.g., IBM Watson). According to respondent 1, the supply side is playing a predominant role in pushing the adoption of AI features in CRM systems. Another emergent theme from the supply side is that CRM implementers (e.g., Accenture, Deloitte and Touche, Perficient), usually partner with multiple CRM vendors and advise and facilitate the adoption of these AI-driven the CRM systems based on the assessment of customers needs. The availability of advanced AI technologies and the promotion and adoption campaigns by CRM vendors and implementers are key supply-side factors that drive the implementation of the AI features in CRM systems. All four respondents predict that due to the dynamic growth of this ecosystem, the pace of change in customer service produced by AI-driven CRMs will accelerate over the next few years. However, she predicts that the acceleration will not continue all the way through a decade, though, because 80% of the adoption will probably come in the next three to five years due to the current high demand for AI features in CRM systems. Following Taylor et al. (2015) guide-lines, I constructed the following analytic diagram (Fig. 5) to illustrate the dynamics of the key cultural categories.

Emergent Themes 

Analytic Categories

The analytic categories establish the domain of the investigation and the organize the knowledge in the domain. In the specific domain of AI-driven CRM systems, a plethora of AI technologies are identified and defined in Fig. 2, which are used in CRM systems to discover insights, identify patterns, answer inquiries, solve problems, and provide recommendations. These analytic categories were used as elaboration probes in the formal interview protocols. The respondents have converged on the several AI technologies which are frequently mentioned in their responses, including the artificial intelligence (AI), machine learning (ML), natural language processing (NLP), voice recognition (VR), image recognition (IR), and big data. Several common emergent themes include

1) Data has fueled the growth of the AI adoptions in the customer service space in recent years. Machine learning (ML) is the engine that powers these AI features. 

2) Voice recognition (VR) and natural language processing (NLP) are among the top AI features adopted in the CRM systems. Virtual agents, chatbots, and agent assist systems are the predo-minant use cases in this space. The major challenge understands user intent & sentiment. 

3) Predictive data analytics are on the rise and Next Best Action (NBA) systems are demanded by CRM customers to utilize the customer data better. 

4) Image recognition (IR) or visual recognition has seen new use cases in the customer service space. 

The respondents have also converged on several themes regarding the impact of AI on customer service and problem resolution, which include

1) Dramatically reducing the number of service personnel answering simple & routine customer inquiries;

2) Understanding each customer individually and providing personalized user experience to the achieve customer intimacy; 

3) Offering fast & accurate answers and efficiently resolving problems; 

4) Achieving higher customer satisfaction and the obtaining competitive advantages over compete-tors; 

5) Ultimately realizing a satisfactory return on the investment (ROI) and maximizing profits;

6) Reducing the number of the low-level customer service personnel and transitioning them to more value-added roles, e.g., virtual agent managers and trainers. The impact is more about the role changes, rather than simple job loss. 

Integrated Interpretation

The descriptive data collected from respondents support the relationships of the cultural and analytic categories proposed in earlier sections. That is, a plethora of AI technologies are becoming increasingly accessible through AI leaders, and are leveraged by CRM vendors and implementers to embed or integrate into CRM systems as useful AI features, which in turn impact customer service and problem resolution.

Summary of Findings

This qualitative research study intends to learn about the impact of the AI on customer relationship man-agement (CRM), specifically in the area of customer service of problem resolution. The following are the key findings based on the qualitative analysis of the data collected through semi-structured interviews with four respondents who have the extensive industry experiences in this space.

1) Overall, AI adoption by businesses for the various purposes has continued to the gain momentum in recent years. With a focus on the customer ser-vice and problem resolution domain, the pace of adoption will accelerate in the next few years. 

2) The most impactful AI features used in CRM systems include 1) virtual agents, chatbots and agent assist systems, which rely on voice/ speech recognition and natural language processing (NLP); 2) Next Best Action (NBA) systems, which leverage predictive data analytics; and 3) personalization technology, which is powered by advanced machine learning (ML) and the big customer data. The key factors that made these AI features impactful in the area of customer service and problem resolution include 1) cost savings from reducing the number of low-level service personnel; 2) faster and more accurate answers to customer inquiries; 3) personalized customer experience; and 4) overall improved quality of customer service & customer satis-faction. 

3) The factors that drive the implementation of these AI features are mainly two folds – 1) from the demand-side perspective, an increasing the number of companies want AI features in their CRM systems to the obtain both tangible and intangible benefits and achieve their strategic goals; 2) from the supply-side perspective, AI leaders, CRM vendors, implementers, especially, are making the AI technologies and AI-driven CRMs more accessible to companies of various sizes. The key stake holders of AI-driven CRMs have formed an ecosystem, which is pushing the accelerated adoption of AI features in this space. 

4) AI has been replacing the jobs of low-level customer service personnel and the some of the employees have transitioned to the more value-added roles, e.g., virtual agent managers and trainers. The impact is more about changing job roles, rather than simple job loss. 

5) All respondents stated that their firms have formal programs to embrace new technologies (or help other firms), including AI. There is also a formal process to evaluate the success of such programs. 

Limitations & Future Research

This study has several limitations. First, the sample size of this study is very small and the informant selection process is not random, thereby limiting the generalizability of the findings. Second, due to the project time constraints, the four informants were selected from our own professional network. Further research could explore strategies to minimize biases linked to informant selection, ensuring a more objective and comprehensive analysis. Two informants are from the same company and three informants are from the IT consulting industry, thereby increasing the chance of biased responses. A longer vetting process would help in this aspect. Moreover, for conducting an in-depth interview, the researchers personal inter-viewing skills (e.g., proper use of elaboration probes) could affect the quality of data collected. The test interview helped us immensely before conducting the actual interviews. Lastly, the flexibility of using elaboration probes in the interview may reduce reliability, as the respondents did not receive the same probe questions, which made it difficult to compare answers. Future research could increase the sample size and explore strategies to minimize biases linked to informant selection, ensuring a more objective & comprehensive analysis. In addition, further research could delve deeper into the transition of job roles resulting from AI implementation. Analyze how employees are adapting to new roles such as virtual agent managers and trainers, and assess the factors influencing a smooth transition.

In conclusion, this qualitative study sheds light on the transformative impact of AI on CRM, particularly in the domain of customer service & problem resolution. The findings underscore the accelerating pace of AI adoption in businesses, with AI features like virtual agents, Next Best Action systems, and personalized technology leading the charge. These technologies, primarily driven by voice & natural language processing, predictive data analytics, & machine learning, are significantly enhancing customer service by enabling cost savings, faster and more accurate responses to inquiries, personalized experiences, and heightened customer satisfaction. Both demand-side factors, where companies seek AI integration to attain strategic objectives, & supply-side dynamics, wherein AI leaders and CRM vendors facilitate accessibility and integration of AI technologies, contribute to this rapid AI adoption. Job roles in customer service are evolving, with AI replacing low-level service person-nel and necessitating a shift towards higher-value roles. While this studys small sample size and the potential biases due to informant selection underscore its limitations, it serves as a foundational exploration of AIs impact in the CRM landscape. Future research should address these limitations, aiming for a broader and more objective analysis to the further our under-standing of this evolving paradigm.

I express my heartfelt gratitude to the reviewers and editors for their insightful feedback and guidance, which significantly contributed to the refinement of this manuscript. Additionally, I extend our appreciation to all individuals and institutions that supported and facilitated this research. Your contributions were invaluable in bringing this work to fruition. 

I hereby declare that I have no conflicts of interest, financial or otherwise, that could have influenced or biased the work presented in this manuscript.