Harnessing AI & SaaS-Based Enterprise App Development for Business Growth
DOI:
https://doi.org/10.47941/ijce.2349Keywords:
Software-as-a-Service (SaaS), Artificial Intelligence (AI), Enterprise App Development, Business Growth.Abstract
Purpose: This paper aims to explore the transformative impact of artificial intelligence (AI) on enterprise models within the context of Software as a Service (SaaS), highlighting the necessity for organisations to adopt AI-driven strategies to sustain their competitive edge.
Methodology: The study utilises a comprehensive framework that outlines five critical steps for implementing AI in business settings, including executing pilot projects, assembling in-house AI teams, providing extensive training, developing holistic AI strategies, and ensuring effective communication.
Findings: The integration of AI within SaaS not only enhances operational efficiency and process optimisation but also allows for real-time customisation and improved user engagement, thus fostering customer loyalty and driving sustainable growth.
Unique contribution to theory, practice and policy: The paper contributes to the understanding of AI and SaaS as essential components in the evolution of enterprise applications, proposing that organisations adopt a collaborative approach to harness their combined potential, ultimately informing policymakers about the strategic importance of AI in shaping future business landscapes.
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References
A. Prasanth, D. J. Vadakkan, P. Surendran, and B. Thomas, “Role of Artificial Intelligence and Business Decision Making,” Int. J. Adv. Comput. Sci. Appl., 2023, doi: 10.14569/IJACSA.2023.01406103.
C. Wang et al., “An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce,” Heliyon, 2023, doi: 10.1016/j.heliyon.2023.e18349.
G. Liu et al., “Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation,” IEEE Netw., 2024, doi: 10.1109/MNET.2024.3352917.
S. Pandey, “Enhancing HR Enhancing with an AI Search Assistant: Integrating ChatGPT Enterprise and Box,” cience Data Learn. Mach. ,Intelligence Artif. J, vol. 2, no. 1, pp. 1–3, 2024, [Online]. Available: https://urfjournals.org/open-access/enhancing-hr-efficiency-with-an-ai-search-assistant-integrating-chatgpt-enterprise-and-box.pdf
S. Pandey, “Transforming Human Resource Management with Generative AI: The Impact of ChatGPT on Recruitment, Training, and Data Analytics,” Eur. J. Adv. Eng. Technol., vol. 11, no. 6, pp. 81–85, 2024, [Online]. Available: https://ejaet.com/PDF/11-6/EJAET-11-6-81-85.pdf
W. J. Palenstijn, K. J. Batenburg, and J. Sijbers, “Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs),” J. Struct. Biol., 2011, doi: 10.1016/j.jsb.2011.07.017.
D. Chanda and N. C. Debnath, “AI Enabled SaaS Framework for Fashion Designing,” in EPiC Series in Computing, 2023. doi: 10.29007/997h.
N. Yathiraju, “Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System,” Int. J. Electr. Electron. Comput., 2022, doi: 10.22161/eec.72.1.
P. K. Sahil Arora, “Optimizing Software Pricing: AI-driven Strategies for Independent Software Vendors,” Int. Res. J. Eng. Technol., vol. 11, no. 05, pp. 743–753, 2024, [Online]. Available: https://www.irjet.net/archives/V11/i5/IRJET-V11I5105.pdf
P. K. Sahil Arora, “The Role of Machine Learning in Personalizing User Experiences in SaaS Products,” J. Emerg. Technol. Innov. Res., vol. 11, no. 06, pp. 809–821, 2024, [Online]. Available: https://www.jetir.org/papers/JETIR2406297.pdf
S. Pandey, “Transforming Performance Management Through Ai: Advanced Feedback Mechanisms, Predictive Analytics, And Bias Mitigation In The Age Of Workforce Optimization,” Int. J. Bus. Quant. Econ. Appl. Manag. reseacrh, vol. 6, no. 7, pp. 1–10, 2020. (49,58)
T. N. Rieder, B. Hutler, and D. J. H. Mathews, “Artificial Intelligence in Service of Human Needs: Pragmatic First Steps Toward an Ethics for Semi-Autonomous Agents,” AJOB Neurosci., 2020, doi: 10.1080/21507740.2020.1740354.
P. K. and S. Arora, “Predicting Customer Churn in SaaS Products using Machine Learning,” Int. Res. J. Eng. Technol., vol. 11, no. 05, pp. 754–764, 2024, [Online]. Available: https://www.irjet.net/archives/V11/i5/IRJET-V11I5106.pdf
A. Mahendra, “Product-Market Validation for AI-First SaaS,” in AI Startup Strategy, 2023. doi: 10.1007/978-1-4842-9502-1_3.
Y. Wang and B. Jin, “The application of SaaS model in network education-take Google Apps for example,” ICETC 2010 - 2010 2nd Int. Conf. Educ. Technol. Comput., 2010, doi: 10.1109/ICETC.2010.5529703.
S. Walraven, E. Truyen, and W. Joosen, “Comparing PaaS offerings in light of SaaS development: A comparison of PaaS platforms based on a practical case study,” Computing, 2014, doi: 10.1007/s00607-013-0346-9.
O. Gassmann, K. Frankenberger, and R. Sauer, “A Primer on Theoretically Exploring the Field of Business Model Innovation,” Eur. Bus. Rev. January, 2017.
L. Serafini and A. D. A. Garcez, “Logic tensor networks: Deep learning and logical reasoning from data and knowledge,” in CEUR Workshop Proceedings, 2016.
P. Khare, “Data-driven product marketing strategies: An in-depth analysis of machine learning applications,” Int. J. Sci. Res. Arch., vol. 10, no. 02, pp. 1185–1197, 2023, doi: https://doi.org/10.30574/ijsra.2023.10.2.0933.
R. Goyal, “The Role Of Business Analysts In Information Management Projects,” Int. J. Core Eng. Manag., vol. 6, no. 9, pp. 76–86, 2020.
Andrew Ng, “AI Transformation Playbook,” Acta Astronaut., 2019.
S. Mishra and R. Tripathi, “AI business model: an integrative business approach,” J. Innov. Entrep., vol. 10, Jul. 2021, doi: 10.1186/s13731-021-00157-5.
I. Palmer, R. Dunford, and D. A. Buchanan, Managing Organizational Change: A Multiple Perspectives Approach. 2017.
M. R. S. and P. K. Vishwakarma, “THE ASSESSMENTS OF FINANCIAL RISK BASED ON RENEWABLE ENERGY INDUSTRY,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 06, no. 09, pp. 758–770, 2024, [Online]. Available: https://www.irjmets.com/uploadedfiles/paper//issue_9_september_2024/61473/final/fin_irjmets1726058754.pdf
V. K. Yarlagadda, S. Kumar, R. Anumandla, S. Charan, R. Vennapusa, and C. Wholesale, “HARNESSING KALI LINUX FOR ADVANCED PENETRATION TESTING AND CYBERSECURITY THREAT MITIGATION,” J. Comput. Digit. Technol., no. July, 2024.
N. G. Abhinav Parashar, “Asset Master Data Management: Ensuring Accuracy and Consistency in Industrial Operations,” IJNRD - Int. J. Nov. Res. Dev., vol. 9, no. 9, pp. 861-a867, 2024.
K. Patel, “Quality Assurance In The Age Of Data Analytics: Innovations And Challenges,” Int. J. Creat. Res. Thoughts, vol. 9, no. 12, pp. f573–f578, 2021.
E. Ries and J. Euchner, “Conversations: What Large Companies Can Learn from Start-ups: An Interview with Eric Ries,” Res. Manag., 2013, doi: 10.5437/08956308X5604003.
A. P. A. S. and N. Gameti, “Digital Twins in Manufacturing: A Survey of Current Practices and Future Trends,” Int. J. Sci. Res. Arch., vol. 13, no. 1, pp. 1240–1250, 2024, [Online]. Available: https://ijsra.net/content/digital-twins-manufacturing-survey-current-practices-and-future-trends
R. Goyal, “Software Development Life Cycle Models: A Review Of Their Impact On Project Management,” Int. J. Core Eng. Manag., vol. 7, no. 2, pp. 78–87, 2022.
S. G. Jubin Thomas, Kirti Vinod Vedi, “Artificial Intelligence And Big Data Analytics For Supply Chain Management,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 06, no. 09, 2024, doi: DOI : https://www.doi.org/10.56726/IRJMETS61488.
H. S. Chandu, “Enhancing Manufacturing Efficiency: Predictive Maintenance Models Utilizing IoT Sensor Data,”IJSART, vol. 10, no. 9, 2024, [Online]. Available: https://ijsart.com/Content/PDFDocuments/IJSARTV10I999425.pdf
K. Girotra and S. Netessine, “Four paths to business model innovation,” Harvard Business Review. 2014.
S. R. Thota and S. Arora, “Neurosymbolic AI for Explainable Recommendations in Frontend UI Design-Bridging the Gap between Data-Driven and Rule-Based Approaches,” Int. Res. J. Eng. Technol., no. May, pp. 766–775, 2024.
M. C. León et al., “Designing a Model of a Digital Ecosystem for Healthcare and Wellness Using the Business Model Canvas,” J. Med. Syst., 2016, doi: 10.1007/s10916-016-0488-3.
V. S. Jubin Thomas, Kirti Vinod Vedi, Sandeep Gupta, “A Survey of E-Commerce Integration in Supply Chain Management for Retail and Consumer Goods in Emerging Markets,” J. Emerg. Technol. Innov. Res., vol. 10, no. 12, pp. h730–h736, 2023, [Online]. Available: https://www.jetir.org/papers/JETIR2312789.pdf
V. K. Y. Nicholas Richardson, Rajani Pydipalli, Sai Sirisha Maddula, Sunil Kumar Reddy Anumandla, “Role-Based Access Control in SAS Programming: Enhancing Security and Authorization,” Int. J. Reciprocal Symmetry Theor. Phys., vol. 6, no. 1, pp. 31–42, 2019.
V. K. Yarlagadda and R. Pydipalli, “Secure Programming with SAS: Mitigating Risks and Protecting Data Integrity,” Eng. Int., vol. 6, no. 2, pp. 211–222, Dec. 2018, doi: 10.18034/ei.v6i2.709.
K. Patel, “Exploring the Combined Effort Between Software Testing and Quality Assurance: A Review of Current Practices and Future,” Int. Res. J. Eng. Technol., vol. 11, no. 09, pp. 522–529, 2024.
E. Loukis, M. Janssen, and I. Mintchev, “Determinants of software-as-a-service benefits and impact on firm performance,” Decis. Support Syst., 2019, doi: 10.1016/j.dss.2018.12.005.
B. Alouffi, M. Hasnain, A. Alharbi, W. Alosaimi, H. Alyami, and M. Ayaz, “A Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3073203.
S. G. Ankur Kushwaha, Priya Pathak, “Review of optimize load balancing algorithms in cloud,” Int. J. Distrib. Cloud Comput., vol. 4, no. 2, pp. 1–9, 2016.
J. Thomas, “Enhancing Supply Chain Resilience Through Cloud-Based SCM and Advanced Machine Learning: A Case Study of Logistics,” J. Emerg. Technol. Innov. Res., vol. 8, no. 9, 2021.
J. Thomas, K. V. Vedi, and S. Gupta, “An analysis of sustainable e-commerce logistics in supply chain management,” 2023.
M. Shakir, M. Hammood, and A. Kh. Muttar, “Literature review of security issues in saas for public cloud computing: a meta-analysis,” Int. J. Eng. Technol., 2018, doi: 10.14419/ijet.v7i3.13075.
S. Arora and P. Khare, “The Role of Machine Learning in Personalizing User Experiences in SaaS Products,” J. Emerg. Technol. Innov. Res., vol. 11, pp. c809–c821, 2024.
M. Shaik, “Navigating The Evolution: Unveiling The Transformative Power Of SaaS-Driven Business Models,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 05, Dec. 2023, doi: 10.56726/IRJMETS47606.
R. Seethamraju, “Adoption of Software as a Service (SaaS) Enterprise Resource Planning (ERP) Systems in Small and Medium Sized Enterprises (SMEs),” Inf. Syst. Front., vol. 17, Jun. 2014, doi: 10.1007/s10796-014-9506-5.
S. Lins, K. D. Pandl, H. Teigeler, S. Thiebes, C. Bayer, and A. Sunyaev, “Artificial Intelligence as a Service: Classification and Research Directions,” Bus. Inf. Syst. Eng., vol. 63, no. 4, pp. 441–456, 2021, doi: 10.1007/s12599-021-00708-w.
E. Nichifor et al., “Utilising Artificial Intelligence to Turn Reviews into Business Enhancements through Sentiment Analysis,”Electron., 2023, doi: 10.3390/electronics12214538.
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