Research Article

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

by  Divyanshu Joshi
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Issue 41
Published: September 2025
Authors: Divyanshu Joshi
10.5120/ijca2025925719
PDF

Divyanshu Joshi . Data-Driven Transformation of Technical Pre-Sales Engineering through AI and Machine Learning. International Journal of Computer Applications. 187, 41 (September 2025), 13-20. DOI=10.5120/ijca2025925719

                        @article{ 10.5120/ijca2025925719,
                        author  = { Divyanshu Joshi },
                        title   = { Data-Driven Transformation of Technical Pre-Sales Engineering through AI and Machine Learning },
                        journal = { International Journal of Computer Applications },
                        year    = { 2025 },
                        volume  = { 187 },
                        number  = { 41 },
                        pages   = { 13-20 },
                        doi     = { 10.5120/ijca2025925719 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2025
                        %A Divyanshu Joshi
                        %T Data-Driven Transformation of Technical Pre-Sales Engineering through AI and Machine Learning%T 
                        %J International Journal of Computer Applications
                        %V 187
                        %N 41
                        %P 13-20
                        %R 10.5120/ijca2025925719
                        %I Foundation of Computer Science (FCS), NY, USA
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 utilized—augmentation, 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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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Artificial Intelligence Machine Learning Technical Sales Engineering Pre-Sales Digital Transformation Sales Process Automation Predictive Analytics Customer Engagement Sales Forecasting Natural Language Processing

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