https://gprjournals.org/journals/index.php/ajt <p><strong>American Journal of Technology</strong> (AJT) is a refereed journal aiming at providing academically robust researches associated with technology-based inventions and innovation. The scope of AJT is Management of Technology including Big Data Analytics, Robotics, Information Technology Developments, Technology Futures, Software Development and Maintenance, etc. AJT is devoted to maintain its credibility amongst scholars, advanced students, reflective practitioners, and readers seeking an update on current experience and future prospects in technology. Manuscripts submitted to this journal are published online and can be printed as hard copies upon author’s request. Papers can be submitted via email to <a href="mailto:journals@gprjournals.org" target="_blank" rel="noopener">journals@gprjournals.org</a> or <a href="https://gprjournals.org/online-submission/">online submission.</a></p> Global Peer Reviewed Journals en-US American Journal of Technology 2958-4094 <p><em>The authors retain the copyright and grant this journal right of first publication. This license allows other people to freely share and adapt the work but must give appropriate credit, provide a link to the license, and indicate if changes were made. They may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.</em></p> https://gprjournals.org/journals/index.php/ajt/article/view/474 <p><strong>Aim: </strong>This paper examines the potential to improve the early-stage performance of pharmaceutical product launches, particularly within the critical first 3 - 6 months, through the use of supervised machine-learning classification models.</p> <p><strong>Methods: </strong>The study employs supervised machine-learning models, including logistic regression, random forests, and neural networks, implemented on the SAP Business Technology Platform (BTP). These models integrate SAP S/4HANA transactional data with external market indicators to optimize launch strategies by improving prescriber targeting, detecting market-access risks, segmenting patient and payer profiles, and enabling real-time decision-making.</p> <p><strong>Results: </strong>The findings indicate that the models decrease forecast variance, improve prescriber reach, and enable earlier corrective interventions during the early launch stage. The models support managerial decision-making by scoring prescriber adoption propensity and categorizing market-access risks to streamline payer strategies.</p> <p><strong>Conclusion: </strong>The study demonstrates that integrating predictive classification models into ERP-based pharmaceutical workflows enhances agility and supports more evidence-based, accurate, and timely decision-making, thereby improving overall launch performance.</p> <p><strong>Recommendation: </strong>The study highlights the need to integrate supervised machine-learning models into pharmaceutical launch workflows while emphasizing the importance of governance systems, privacy, equity, and regulatory considerations to enable proactive changes, improve market accessibility, and optimize resource allocation.</p> Yogesh Chandan Copyright (c) 2026 Yogesh Chandan https://creativecommons.org/licenses/by/4.0 2026-01-31 2026-01-31 5 1 1 25 10.58425/ajt.v5i1.474 https://gprjournals.org/journals/index.php/ajt/article/view/475 <p><strong>Aim: </strong>Dynamic-pricing services rely on good demand indicators, but the retail and logistics sites continue to be affected by price bots. These automated agents interact with traffic volumes and corrupt the input on price optimization. This study aims to develop and evaluate a hybrid artificial intelligence–based bot-detection framework that identifies automated sessions in retail and logistics platforms and provides explainable traffic-quality signals for dynamic pricing systems.</p> <p><strong>Methods: </strong>The study adopts a system-based experimental design that integrates graph-based modeling, sequence learning, Agentic AI-generated synthetic traces, and ensemble classification. Data were drawn from anonymized web logs, honeypot captures, and multi-agent adversarial simulations. Models are tested in terms of precision, recall, AUC, and stability of the pricing engine.</p> <p><strong>Results:</strong> The proposed pipeline achieved high detection performance (AUC ≈ 0.94) and showed that graph metrics and timing distribution characteristics were the most prominent predictors of automated behavior.</p> <p><strong>Conclusion:</strong> The use of AI-based synthetic traces enhanced the model’s strength, aligning with BotChase and other research studies.</p> <p><strong>Recommendation: </strong>This study suggests that a hybrid AI/ML-based bot detection approach can enhance the reliability of demand signals used in dynamic pricing systems, providing a viable alternative to traditional binary blocking methods. &nbsp;This helps significantly in preventing pricing distortion and enhancing market resilience.</p> Rohit Grover Copyright (c) 2026 Rohit Grover https://creativecommons.org/licenses/by/4.0 2026-01-31 2026-01-31 5 1 26 39 10.58425/ajt.v5i1.475