Published: 2025-04-01
Development of Applications with Artificial Intelligence: Expert Perspectives and Recommendations
DOI: 10.35870/ijsecs.v5i1.3888
Downloads
Abstract
Artificial intelligence (AI) applications are accelerating significantly, supported by three pillars: core technologies, cost efficiency, and strategic direction. A comparative analysis reveals critical contributions from three technologies: (1) Machine Learning (ML) enhances user engagement by 35% through personalized recommendation systems on e-commerce platforms; (2) Natural Language Processing (NLP) reduces customer service operational costs by 47% via intelligent chatbots in the banking sector; and (3) predictive analytics improves cardiovascular disease diagnosis accuracy by 27% based on multicenter clinical data. Estimated AI application development costs range from $50,000 to $250,000, depending on algorithm complexity and computational infrastructure requirements. Future AI development will be shaped by two trends: (1) Edge AI, which reduces data processing latency by 60% through local computation, and (2) Explainable AI (XAI), which enhances algorithm transparency to comply with GDPR and ISO/IEC 23894 regulations. The study underscores that successful AI implementation requires multidisciplinary integration among data scientists, software engineers, and business stakeholders. Strategic recommendations include allocating 15–20% of R&D budgets for continuous learning, establishing an AI ethics committee aligned with OECD principles, and adopting an agile development model for market responsiveness
Keywords
Artificial Intelligence ; Machine Learning ; Natural Language Processing ; Edge AI ; Explainable AI
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 5 No. 1 (2025)
-
Section: Articles
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/ijsecs.v5i1.3888
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Julien Florkin
AI Technology Consultant, TechInnovate Solutions, Liège, Walloon Region, Belgium
-
Gartner. (2023). Market guide for AI technologies.
-
IBM. (2022). Watson Health: Transforming oncology with AI.
-
Statista. (2024). Global AI market revenue forecast.
-
Dikshit, S., Atiq, A., Shahid, M., Dwivedi, V., & Thusu, A. (2023). The use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areas. EAI Endorsed Transactions on Energy Web, 10. https://doi.org/10.4108/ew.4613
-
Umoga, U., Sodiya, E., Ugwuanyi, E., Jacks, B., Lottu, O., Daraojimba, O., ... & Obaigbena, A. (2024). Exploring the potential of AI-driven optimization in enhancing network performance and efficiency. Magna Scientia Advanced Research and Reviews, 10(1), 368-378. https://doi.org/10.30574/msarr.2024.10.1.0028
-
Gan, I., & Moussawi, S. (2022). A value sensitive design perspective on AI biases. Proceedings of the 55th Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2022.676
-
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), Article 3. https://doi.org/10.1186/s41239-022-00372-4
-
Elufioye, O., Ike, C., Odeyemi, O., Usman, F., & Mhlongo, N. (2024). AI-driven predictive analytics in agricultural supply chains: A review: Assessing the benefits and challenges of AI in forecasting demand and optimizing supply in agriculture. Computer Science & IT Research Journal, 5(2), 473-497. https://doi.org/10.51594/csitrj.v5i2.817
-
Akgün, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in k-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7
-
Umbrello, S. (2019). Beneficial artificial intelligence coordination by means of a value sensitive design approach. Big Data and Cognitive Computing, 3(1), Article 5. https://doi.org/10.3390/bdcc3010005
-
Williams, M. (2024). Future visions of AI enhancing industries and navigating ethical landscapes. International Journal of Science and Research Archive, 12(2), 1259-1266. https://doi.org/10.30574/ijsra.2024.12.2.1343
-
Cob-Parro, A., Lalangui, Y., & Lazcano, R. (2024). Fostering agricultural transformation through AI: An open-source AI architecture exploiting the MLOps paradigm. Agronomy, 14(2), 259. https://doi.org/10.3390/agronomy14020259
-
Adanma, U., & Ogunbiyi, E. (2024). Artificial intelligence in environmental conservation: Evaluating cyber risks and opportunities for sustainable practices. Computer Science & IT Research Journal, 5(5), 1178-1209. https://doi.org/10.51594/csitrj.v5i5.1156
-
Agapie, E., et al. (2020). Trends in artificial intelligence: A review of recent developments and future directions. Journal of Artificial Intelligence Research, 68, 1-32.
-
Bhatia, S., & Sun, Y. (2021). Machine learning in industry: Transforming business practices. IEEE Transactions on Automation Science and Engineering, 18(1), 239-245.
-
Luo, Y., & Xu, X. (2022). AI and ethics: A review of frameworks for responsible AI. AI and Ethics, 1(2), 29-44.
-
Johnson, M., et al. (2023). MLOps: Operationalizing machine learning. Data Mining and Knowledge Discovery, 37(4), 1501-1520.
-
Smith, T., & Brown, R. (2023). Case studies of AI implementation in agriculture: Lessons learned. Agricultural Systems, 199, 103378.
-
Taylor, K. (2022). AI in healthcare: Transformative applications and challenges. Health Informatics Journal, 28(1), 12-24.
-
Patel, S., & Kim, H. (2023). Evaluating the implementation of AI solutions: A case study approach. International Journal of Production Research, 61(10), 1415-1432.
-
Green, J. (2022). Insights from experts: The future of AI technologies. AI & Society, 37(3), 541-556.
-
Choi, C., et al. (2023). Barriers to AI adoption in small and medium enterprises. Journal of Small Business Management, 61(2), 389-407.
-
Li, F., & Mehta, R. (2024). The future of AI: Trends shaping the industry landscape. Technological Forecasting and Social Change, 183, 121928.
-
Zhang, Y., et al. (2023). A comparative evaluation of AI frameworks: TensorFlow vs. PyTorch vs. Azure ML. Journal of Systems and Software, 196, 110470.
-
Rowe, J., & Patel, R. (2024). Scaling AI applications: Integration challenges and solutions. Software: Practice and Experience, 54(1), 224-248.
-
Sunitha, B. (2024). A study on data analytics techniques used by Netflix to personalize recommendations. International Journal of Scientific Research in Engineering and Management, 08(03), 1-5. https://doi.org/10.55041/ijsrem29882
-
Liu, Y., Xu, Y., & Zhou, S. (2024). Enhancing user experience through machine learning-based personalized recommendation systems: Behavior data-driven UI design. Applied and Computational Engineering, 112(1), 42-46. https://doi.org/10.54254/2755-2721/2024.17905
-
Pajkovic, N. (2021). Algorithms and taste-making: Exposing the Netflix recommender system's operational logics. Convergence: The International Journal of Research into New Media Technologies, 28(1), 214-235. https://doi.org/10.1177/13548565211014464
-
Sreerangapuri, A. (2024). AI-driven service transformation: Revolutionizing operational excellence. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(6), 132-140. https://doi.org/10.32628/cseit24106154
-
Rahman, A. (2024). AI and machine learning in business process automation: Innovating ways AI can enhance operational efficiencies or customer experiences in U.S. enterprises. NHJ, 1(01), 41-62. https://doi.org/10.70008/jmldeds.v1i01.41
-
Mays, K., Katz, J., & Groshek, J. (2022). Mediated communication and customer service experiences. Periodica Polytechnica Social and Management Sciences, 30(1), 1-11. https://doi.org/10.3311/ppso.16882
-
Mariani, M., & Borghi, M. (2023). Artificial intelligence in service industries: Customers' assessment of service production and resilient service operations. International Journal of Production Research, 62(15), 5400-5416. https://doi.org/10.1080/00207543.2022.2160027
-
Yao, B. (2023). Assessing the viability and effectiveness of ChatGPT applications in the customer service industry: A study on business models and user experience. Highlights in Business Economics and Management, 21, 843-851. https://doi.org/10.54097/hbem.v21i.14785
-
Shawal, N., Bakhtiar, M., Nurzaman, M., Kedin, N., & Talib, A. (2023). Exploring user acceptance, experience and satisfaction towards chatbots in an online travel agency (OTA). International Journal of Academic Research in Business and Social Sciences, 13(5). https://doi.org/10.6007/ijarbss/v13-i5/17015
-
Chao, M., Trappey, A., & Wu, C. (2021). Emerging technologies of natural language-enabled chatbots: A review and trend forecast using intelligent ontology extraction and patent analytics. Complexity, 2021(1). https://doi.org/10.1155/2021/5511866
-
Ray, A., Bala, P., & Jain, R. (2020). Utilizing emotion scores for improving classifier performance for predicting customer's intended ratings from social media posts. Benchmarking: An International Journal, 28(2), 438-464. https://doi.org/10.1108/bij-01-2020-0004
-
Folorunsho, S., Adenekan, O., Ezeigweneme, C., Somadina, I., & Okeleke, P. (2024). Leveraging technical support experience to implement effective AI solutions and future service improvements. International Journal of Applied Research in Social Sciences, 6(8), 1758-1783. https://doi.org/10.51594/ijarss.v6i8.1425
-
Institute of Medicine. (2000). To err is human: Building a safer health system. National Academies Press. https://doi.org/10.17226/9728
-
Ratanawongsa, N., et al. (2016). Errors in the diagnosis of medical/procedural errors: A cross-sectional study. BMC Medical Informatics and Decision Making, 16, Article 141. https://doi.org/10.1186/s12911-016-0356-3
-
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future - Big data, machine learning, and health care. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1601063
-
Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
-
Li, A. I., et al. (2020). The application of artificial intelligence in the diagnosis and treatment of medical conditions: A review of use and acceptance. Artificial Intelligence in Medicine, 105, 101837. https://doi.org/10.1016/j.artmed.2020.101837
-
Chaudhry, R., et al. (2006). Systematic review of home telemonitoring for chronic diseases: The evidence base. Journal of Telemedicine and Telecare, 12(5), 274-284. https://doi.org/10.1258/135763306778193843
-
Kuperman, G. J., & Gibson, R. (2003). Computerized provider order entry: Benefits and barriers. Health Information Management Journal, 32(2), 2-6. https://doi.org/10.1177/183335830303200202.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.