Optimizing Small-Business Sales Performance with AI-Driven Adaptive Learning Platforms

By Anh Khuc, Vietnam

Abstract:

This study investigates the effectiveness of AI-driven adaptive learning platforms in enhancing sales skills amongst Vietnamese small and medium-sized enterprises (SMEs). Despite SMEs making up the majority of Vietnam’s economy, they struggle to obtain a skilled workforce due to traditional training methods. AI offers personalized training that fosters engagement and enhances productivity, however, its impact on SME sales remains underexplored. Hence, using a six-month quasi-experimental design with 50 SMEs, this study compares AI-based training to traditional methods by measuring sales outcomes and user feedback. The findings aim to provide businesses and policymakers with critical insights into enhancing workforce skills and boosting competitiveness in Vietnam’s evolving market.

Introduction:

As 98% of Vietnamese enterprises are small and medium-sized enterprises (SMEs), they contribute significantly to the national economic growth. However, they encounter challenges in acquiring a skilled workforce because the educational system prioritizes memorization of theories rather than application of skills, leading to a mismatch between training and labor market needs. This critical issue hinders Vietnam’s SMEs’ ability to utilize their workforce efficiently, thus suboptimal sales outcomes. Meanwhile, AI adaptive learning platforms experience growth as they personalize training curriculum based on the knowledge gaps of the individual, hence offering a potential solution. However, there is limited empirical research on the extent of the effectiveness of these tools in real-life SMEs in developing markets such as Vietnam.

Literature Review:

The effectiveness of AI adaptive learning in education has been well documented in various studies. In education, a meta-analysis of 45 independent studies concluded that AI adaptive learning brings a medium to larger positive effect (g = 0.70) on student performance (Wang et al.). Moreover, a study conducted by Knewton reported a “62% increase in test scores of students” implementing AI learning. In the corporate training settings, AI-adaptive learning showcased positive results in increasing employee engagement and knowledge retention- as indicated by IBM, indicate reported a 46% increase in employee productivity and findings from ValueSelling Associates and Aberdeen Strategy & Research reported that enterprises experience a “3.3 times greater year-over-year growth in sales” with AI programs.

​

These enhancements in workforce satisfaction and productivity translate directly into improving sales outcomes and business growth. Despite these promising results, most studies focus on academic or general professional development contexts, leaving a knowledge gap about their impact on sales-skills training, particularly within small and medium-sized enterprises (SMEs) in emerging markets like Vietnam. Despite Vietnam’s young and growing workforce, only about 28% of workers have specialized degrees as of mid-2023, leaving nearly 38 million employees without formal training (Author A, Hiếu; Author B: VietNamNet News). This major skills gap hinders growth and innovation, as businesses struggle to equip employees with the qualifications for knowledge-intensive roles. Hence, the notable lack of research on the application of AI adaptive learning for sales-skills development for small businesses in Vietnam presents a critical challenge to revolutionize Human Resources training programs, to upskill Vietnam’s workforce. This challenge justifies the need for empirical research to measure the effectiveness of AI-adaptive training on scalable values. Furthermore, addressing this issue now is urgent as SMEs are undergoing pressure to digitalize and compete globally to provide critical insights for businesses, policymakers, in possibly transform workforce development to gain economic competitiveness.

​

Research Question:

To what extent does a six-month AI-driven adaptive learning experience influence monthly sales and customer acquisition cost in retail SMEs ( > 100 employees, annual revenue < US$ $1 million) in Vietnam?

Methodology:​ 

Building on evidence in the literature review on the effectiveness of AI-adaptive learning platforms, this quasi-experimental design, consisting of a 3-month baseline, 6-month testing, and 1-month follow-up, will measure the impact of AI-adaptive learning platforms on sales skills performance for Vietnamese SMEs qualitatively and quantitatively.

First, a purposive sample of 50 Vietnamese SMEs in the retail sector will be recruited and randomly selected to be assigned to either a test group or a control group. In this experiment, SMEs would be characterized as firms with less than 100 employees with an annual revenue of less than $1 million. Participating enterprises will be matched by size and industry, then assigned randomly to either group. The tested group would be implementing an AI-driven adaptive training platform, whereas the control group would continue traditional sales training, covering the same curriculum and total training hours.

The length of this test would be over six months as both groups would be assessed and measured based on monthly key performance metrics, including monthly sales velocity (VND/ per person) and monthly revenue growth (%). These metrics are selected to reflect on the real-world outcomes of the AI-driven training program, as this factor is lacking in the literature. These numerals will be utilized to compare the two groups using independent samples t-tests, aiming to assess the differences in average performance between the two groups.

The qualitative insights will be collected through structured surveys and follow-up interviews with selected SME owners, sales managers, and staff. Surveys will include scale questions (from 1-10) to collect individuals’ perceptions of the effectiveness of their training, user satisfaction, and skill improvement. The interview responses would be summarized to identify recurring ideas and challenges across participants. This approach ensures a comprehensive understanding of user experience and the feasibility of implementing this technology.

​

Ethical Consideration :
Due to the nature of a quasi-experiment, ethical considerations include transparency and informed consent, as the participants are well aware of the nature of the experiment. Furthermore, participants are volunteers and have the right to withdraw from the study at any point in time. The data should be anonymized to protect the privacy of the participants.

​​

References :

1. Business Forum 2025 Driving Institutional Reforms to Facilitate Business Development. 22 Apr. 2025, en.vcci.com.vn/business-forum-2025-driving-institutional-reforms-to-facilitate-busine ss-development.

2. Hiếu Dũng. “Challenges From Labor Quality.” Nhịp Sống Kinh Tế Việt Nam & Thế Giới, 11 Aug. 2024, vneconomy.vn/challenges-from-labor-quality.htm.

3. “Knewton Personalizes Learning With the Power of AI - Digital Innovation and Transformation.” Digital Innovation and Transformation, 19 Apr. 2021, d3.harvard.edu/platform-digit/submission/knewton-personalizes-learning-with-the-po wer-of-ai.

4. OECD (2021), SME and Entrepreneurship Policy in Viet Nam, OECD Studies on SMEs and Entrepreneurship, OECD Publishing, Paris, https://doi.org/10.1787/30c79519-en.

5. Le, Dahlia. “Characteristics of Vietnam's Workforce 2025: Job Market &Amp; Advice.” InCorp Vietnam, Vietnam.incorp.asia/characteristics-of-vietnams-workforce.

6. Sari, Herva Emilda, et al. “Improving Educational Outcomes Through Adaptive Learning Systems Using AI.” International Transactions on Artificial Intelligence (ITALIC), vol. 3, no. 1, Nov. 2024, pp. 21–31. https://doi.org/10.33050/italic.v3i1.647.

7. VietNamNet News. “Vietnam Heading Towards Skills-based Labor Market.” VietNamNet News, vietnamnet.vn/en/vietnam-heading-towards-skills-based-labor-market-2138638.html# :~:text=over%2038%20million%20workers.

8. Wang, Xiaoman, et al. “The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis.” Journal of Educational Computing Research, vol. 62, no. 6, May 2024, pp. 1568–603. https://doi.org/10.1177/07356331241240459.