Articles

Strategic Decision-Making: Implementing Artificial Intelligence for Customer Experience in XYZ Electricity

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This case study outlines the challenges in resolving customer complaints at XYZ electricity provider, where the industry achieves only 89.16% against a 100% service level agreement, leading to poor customer experience (CX). The objective of this paper is not only to identify the root causes of poor CX and validate artificial intelligence (AI)’s potential role as a solution, but also to pioneer the identification of critical success factors (CSFs) and strategic areas for AI implementation, leveraging computational ratings to enhance decision-making processes. This research employs comprehensive data collection methods, including primary data from interviews and workshops involving 300 participants and secondary data from observation and literature studies. It utilizes an integrative strategy framework (ISF) to strategically synthesize internal and external analyses. Additionally, it ranks critical areas for AI implementation using the analytic hierarchy process (AHP) based on pairwise judgment and Likert scale surveys from ten experts. The most significant findings reveal that direct impact on customers, at 28.54%, is the strongest CSF, while customer service, 14,63%, is the most impactful implementation of AI in the XYZ to fix poor CX. A pilot project on customer service can improve CX, revenue, and cost savings. The authors suggests that another researcher implement and evaluate AI in various businesses and specific client categories.

Risk Assessment of Neobank in Indonesia: Case Study of Bank Gembira Indonesia

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This research delves into a comprehensive analysis of the risks encountered by Bank Gembira, a notable neobank in Indonesia. Through combining qualitative and quantitative methodologies, this research study identifies and classifies various types of risk, including credit risk, market risk, liquidity risk, and operational risk. In facilitating the prioritization process, the study makes use of Saaty’s Analytic Hierarchy Process (AHP) as instrument. The study highlights the importance of understanding customer behavior in mitigating risks for neobanks and recommends further research on risk assessment in the neobanking sector. The analysis emphasizes the critical role of credit risk and operational risk for Bank Gembira as a neobank. Through AHP calculations, credit default and cyberattacks are identified as the highest priority risks, underscoring the need for robust risk treatment plans to address these high-level risks effectively. Recommendations are proposed to address these risks, such as enhancing credit scoring for P2P lending partners, improving cybersecurity measures, collaborating with regulators, tracking technology updates, partnering with e-commerce platforms, offering promotional programs, developing digital talent programs, and attracting MSMEs customers. Further research on risk assessment in the neobanking sector is suggested to enhance risk management practices and ensure sustainable growth for neobanks like Bank Gembira.