Data-Driven Transformation of Technical Pre-Sales Engineering through AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the landscape of technical pre-sales engineering.This research analyzes studies from 2019 to 2025 to determine how AI/ML technologies are transforming pre-sales activities, simplifying deal qualification, improving predictions, and enhancing customer engagements.The literature review highlights three primary ways these technologies are utilizedaugmentation, automation, and transformation-showing evidence that productivity can rise by as much as 30%, sales cycles decrease by 25%, and predictivity rates enhance by more than 20%.Industry-specific quantitative research and case studies indicate strong positive impacts on organizational performance, sales efficiency, and customer satisfaction.Ethical concerns like transparency in data usage, eliminating bias, and responsible data utilization are also addressed.The research concludes with proposed future studies, emphasizing the necessity of achieving a balance between the advantages of AI and human expertise to provide optimal value in technical pre-sales engineering.

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Publication Details

Journal
International Journal of Computer Applications
Published
2025-09-22
DOI
https://doi.org/10.5120/ijca2025925719
Primary Topic
Ruminant Nutrition and Digestive Physiology
Type
article
Field-Weighted Citation Impact
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article

Data-Driven Transformation of Technical Pre-Sales Engineering through AI and Machine Learning

International Journal of Computer Applications
Ruminant Nutrition and Digestive Physiology
article

Data-Driven Transformation of Technical Pre-Sales Engineering through AI and Machine Learning

article en

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the landscape of technical pre-sales engineering.This research analyzes studies from 2019 to 2025 to determine how AI/ML technologies are transforming pre-sales activities, simplifying deal qualification, improving predictions, and enhancing customer engagements.The literature review highlights three primary ways these technologies are utilizedaugmentation, automation, and transformation-showing evidence that productivity can rise by as much as 30%, sales cycles decrease by 25%, and predictivity rates enhance by more than 20%.Industry-specific quantitative research and case studies indicate strong positive impacts on organizational performance, sales efficiency, and customer satisfaction.Ethical concerns like transparency in data usage, eliminating bias, and responsible data utilization are also addressed.The research concludes with proposed future studies, emphasizing the necessity of achieving a balance between the advantages of AI and human expertise to provide optimal value in technical pre-sales engineering.

International Journal of Computer ApplicationsVol. 187(41)
Cisco College (US)
Openalex Percentile: Top 41%
Ruminant Nutrition and Digestive Physiology
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Data-Driven Transformation of Technical Pre-Sales Engineering through AI and Machine Learning · International Journal of Computer Applications (2025) | TGRS Research Map | TGRS