Authors

Eman Ibrahim Abdel Fattah Harb

Department of Business Administration, Applied College, Jazan University, Jazan 45142, Kingdom of Saudi Arabia

Abstract

This study examines Artificial Intelligence (AI) in companies’ profit transformation, demonstrating how strategic AI adoption drives measurable financial gains. Through longitudinal financial analysis of companies (2020–2025), we found that firms progressing through AI adoption phases—Exploration to Optimization—reduced ROI timeframes by 80% (from 15 to 3 months) while increasing profit margins from 3.2% to 34.5%. Sector-specific impacts varied significantly, with retail achieving 19% revenue growth from AI-powered dynamic pricing, while manufacturing reduced downtime costs by 32% through predictive maintenance. The research highlights AI’s role as a scalable profitability lever, particularly when aligned with industry-specific value drivers.

Despite these benefits, challenges in AI adoption persist, including high implementation costs, data silos, and workforce resistance. Our three-phase research design combined quantitative financial analysis with qualitative deep dives into implementation strategies. Phase 1 revealed that only 15% of companies successfully scaled AI beyond pilot projects, often due to misaligned ROI expectations. Phase 2 identified that cross-functional integration and quick-win use cases (e.g., chatbots, inventory optimization) were critical for overcoming adoption barriers. Phase 3’s future-readiness assessment showed that firms with flexible AI infrastructure achieved 6.8x higher ROI than those at the Awareness stage. These findings underscore the importance of structured implementation frameworks to mitigate risks and maximize returns.

The study also explores emerging trends, including generative AI and edge computing, which are poised to redefine profitability strategies. Notably, 89% of firms achieved ROI within 18 months, debunking the myth that AI requires multi-year gestation periods. Our research contributes a validated maturity model for AI adoption, linking productivity gains (61% at Transformation stage) to long-term profit growth (34.5% margins). By bridging theoretical insights with empirical data, this study provides a practical roadmap for companies to harness AI’s full potential, emphasizing phased implementation, sector-specific applications, and continuous performance tracking to sustain competitive advantage.

Keywords

Artificial Intelligence (AI) Profitability Transformation AI Adoption Challenges ROI Acceleration Maturity-Stage Performance Sector-Specific AI Impact 18-Month Profitability Threshold

Citation of this Article

Eman Ibrahim Abdel Fattah Harb. (2025). Artificial Intelligence and Its Role in Increasing Companies' Profitability. Journal of Artificial Intelligence and Emerging Technologies. 2(4), 16-24. Article DOI: https://doi.org/10.47001/JAIET/2025.204004

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. M. Chui et al., "The state of AI in 2023," McKinsey Global Survey, 2023.
  2. K. Panetta, "Gartner top 10 strategic technology trends for 2024," Gartner, 2024.
  3. E. Brynjolfsson and A. McAfee, "The business of artificial intelligence," Harvard Business Review, vol. 98, no. 1, pp. 110-120, 2022.
  4. S. Kinkel et al., "AI in manufacturing," Springer, 2022.
  5. L. Chen et al., "Dynamic pricing using AI," Journal of Business Analytics, vol. 5, no. 2, pp. 45-60, 2021.
  6. T. H. Davenport et al., "The AI advantage," MIT Press, 2021.
  7. E. W. T. Ngai et al., "AI in fraud detection," Decision Support Systems, vol. 112, pp. 23-35, 2021.
  8. Deloitte, "The AI imperative: Growth and challenges," Deloitte Insights, 2023.
  9. J. Bughin et al., "The economics of AI adoption," MIT Sloan Management Review, vol. 62, no. 3, pp. 34-42, 2021.
  10. N. Mehrabi et al., "A survey on bias and fairness in machine learning," ACM Computing Surveys, vol. 54, no. 6, pp. 1-35, 2021.
  11. H. J. Wilson and P. R. Daugherty, "Human + machine: Reimagining work in the age of AI," Harvard Business Press, 2021.
  12. R. Bommasani et al., "On the opportunities and risks of foundation models," Stanford University, 2023.
  13. W. Shi et al., "Edge computing: Vision and challenges," IEEE IoT Journal, vol. 3, no. 5, pp. 637-646, 2021.
  14. Gartner, "Top strategic technology trends for 2024," Gartner, 2024.
  15. J. Bughin and B. McCarthy, "The ROI from AI: Evidence from Global Firms," McKinsey Quarterly, vol. 4, pp. 1-15, 2024. [Online]. Available: https://www.mckinsey.com/quarterly/the-roi-from-ai
  16. E. Brynjolfsson et al., "The Business Value of AI in the Post-Pandemic Era," NBER Working Paper 29955, 2024. [Online]. Available: https://www.nber.org/papers/w29955
  17. A.Tambe et al., "Measuring AI's Impact on Firm Performance," Management Science, vol. 68, no. 5, pp. 3324-3342, 2022.
  18. MIT Sloan CISR, "The AI-Ready Organization Framework," Research Briefing, vol. XXII, no. 6, 2023. [Online]. Available: https://cisr.mit.edu/publications
  19. Boston Consulting Group, "The State of AI Implementation 2025," BCG Gamma Report, 2025. [Online]. Available: https://www.bcg.com/ai-implementation-2025
  20. D. Autor et al., "New Methods for Assessing AI Business Impact," American Economic Review, vol. 113, no. 5, pp. 1285-1320, 2023.