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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

TECHNICAL ESTIMATION EMPOWERING PROPOSAL EXCELLENCE

Kailas Mesagi Sonawane

Prof. Abhijit G. Bharati

MBA / SAVITRIBAI PHULE PUNE UNIVERSITY PUNE INDIA
ZEAL INSTITUTE OF BUSINESS ADMINISTRATION COMPUTER APPLICATION & RESEARCH (ZIBACAR) PUNE

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

This research aims to examine how AI-driven technical estimation contributes to proposal excellence, analyze its impact on accuracy and efficiency, identify challenges in implementation, and explore its strategic implications for modern enterprises.

This research project explores the transformative role of Artificial Intelligence (AI) in improving the accuracy, efficiency, and strategic value of technical estimation processes used in proposal development. In today’s competitive business environment, organizations must deliver precise, timely, and cost-effective proposals to secure contracts and maintain profitability. Traditional estimation methods, often reliant on manual calculations and historical judgment, are prone to errors, inconsistencies, and delays.

How to Cite this Paper

Sonawane, K. M. (2026). Technical estimation empowering proposal excellence. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.311

Sonawane, Kailas. "Technical estimation empowering proposal excellence." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.311.

Sonawane, Kailas. "Technical estimation empowering proposal excellence." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.311.

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References

[1] Tom M. Mitchell (1997). Machine Learning. New York: McGraw‑Hill.

[2] Stuart Russell & Peter Norvig (2021). Artificial Intelligence: A Modern Approach (4th ed.). London: Pearson.

[3] Roger S. Pressman & Bruce R. Maxim (2019). Software Engineering: A Practitioner’s Approach. New York: McGraw-Hill Education.

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  • •Published on: May 11 2026
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