AI-Assisted Marketing Content Generation for Non-Standard Industrial Automation Solutions

Li Wensi

doi:10.70393/6a6574626d.333937

OPEN ACCESS |Research Article ||18 February 2026

AI-Assisted Marketing Content Generation for Non-Standard Industrial Automation Solutions

Li Wensi

School of Management and Economics, Tianjin University, Tianjin, China

48572556@qq.com

School of Management and Economics, Tianjin University, Tianjin, China, CN, 48572556@qq.com.

* Corresponding Author¹: Li Wensi , E-Mail: 48572556@qq.com

Publication

Accepted 2026 February 15 ; Published 2026 February 18

Journal of Economic Theory and Business Management, 2026, 3(1), 3006-4953.

Abstract

This paper investigates AI-assisted marketing content generation for non-standard (customized) industrial automation solutions, a domain characterized by fragmented orders, long project cycles, intense price competition, and limited content reuse across clients. Because each project is highly tailored to a specific factory context, small and micro-automation firms often struggle to articulate the value of solutions clearly, standardize proposal narratives, and rapidly produce consistent marketing materials—resulting in repetitive communication, increased management overhead, and avoidable errors. To address this practical challenge, the paper proposes an AI-enabled approach that transforms scattered inputs—such as customer needs, process constraints, equipment parameters, and solution descriptions—into structured, coherent, and deliverable marketing content (e.g., solution overviews, value propositions, implementation plans, and differentiated highlights). By improving the speed, consistency, and accuracy of content production, the approach helps non-standard automation providers communicate value more efficiently, reduce low-value back-and-forth, and strengthen competitiveness in a highly fragmented market.

Keywords

Non-standard Industrial Automation , AI-assisted Content Generation , Marketing Automation , Solution Value Communication , SME Manufacturing Services .

Metadata

DOI:

10.70393/6a6574626d.333937

ARK:

ark:/40704/JETBM.v3n1a03

Pages: 18-25

References: 16

Disciplines: Business Analytics

Subjects: Machine Learning Applications

Cite This Article

APA Style

Wensi , L. (2026). Ai-assisted marketing content generation for non-standard industrial automation solutions. Journal of Economic Theory and Business Management, 3(1), 18-25. https://doi.org/10.70393/6a6574626d.333937

Acknowledgments

Not Applicable.

FUNDING

Not Applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not Applicable.

DATA AVAILABILITY STATEMENT

Not Applicable.

INFORMED CONSENT STATEMENT

Not Applicable.

CONFLICT OF INTEREST

Not Applicable.

AUTHOR CONTRIBUTIONS

Not application.

References

1.

Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020). Uniting the tribes: Using text for marketing insight. Journal of Marketing, 84(1), 1–25.

2.

Paschen, J., Wilson, M., & Ferreira, J. J. M. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(6), 863–872.

3.

Pandey, N., Nayal, P., & Rathore, A. S. (2020). Digital marketing for B2B organizations: Structured literature review and future research directions. Journal of Business & Industrial Marketing, 35(7), 1191–1204.

4.

Han, J., Kamber, M., Pei, J., & other authors. (2021). Artificial intelligence in business-to-business marketing: A bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems, 121(11), 2565–2587.

5.

Mikalef, P., Conboy, K., & Krogstie, J. (2021). Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach. Industrial Marketing Management, 98, 80–92.

6.

Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021). Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research. Industrial Marketing Management, 98, 161–178.

7.

Li, S., Peng, G., Xing, F., Zhang, J., & Zhang, B. (2021). Value co-creation in industrial AI: The interactive role of B2B supplier, customer and technology provider. Industrial Marketing Management, 98, 105–114.

8.

Kushwaha, A. K., Kumar, P., & Kar, A. K. (2021). What impacts customer experience for B2B enterprises when using AI-enabled chatbots? Insights from big data analytics. Industrial Marketing Management, 98, 207–221.

9.

Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219.

10.

De Caigny, A., Coussement, K., De Bock, K. W., & Lessmann, S. (2021). Incorporating textual information in uplift modeling for B2B customer churn prediction. Industrial Marketing Management, 99, 28–39.

11.

Reisenbichler, M., Reutterer, T., Schweidel, D. A., & Dan, D. (2022). Frontiers: Supporting content marketing with natural language generation. Marketing Science, 41(3), 441–452.

12.

Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132.

13.

Moradi, M., & Dass, M. (2022). Applications of artificial intelligence in B2B marketing: Challenges and future directions. Industrial Marketing Management, 107, 300–314.

14.

Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269–275.

15.

Brüns, J. D., & Meißner, M. (2024). Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity. Journal of Retailing and Consumer Services, 79, Article 103790.

16.

Abakarim, Y., Lahby, M., & Attioui, A. (2018, October). An efficient real time model for credit card fraud detection based on deep learning. In Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications (pp. 1-7).

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