From Challenges to Solutions: Key Pain Points and Turnarounds in AI Systems

  • Authors

    • Madhurima Kommuru MGR-Tech Prod Delivery at Claritev, USA. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2025PII2R5S

    Published 09-04-2025

  • Artificial Intelligence, Machine Learning, AI Challenges, System Integration, Bias Mitigation, Scalability, AI Optimization, Digital Transformation

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    M. Kommuru, “From Challenges to Solutions: Key Pain Points and Turnarounds in AI Systems”, IJAIDT, vol. 8, no. 2, pp. 01–20, Sep. 2025, doi: 10.67228/30713315/IJAIDT-2025PII2R5S.

  • Abstract

    Artificial Intelligence has moved very quickly from experimental innovation to a foundational driver of transformation across industries including healthcare, banking, insurance and logistics, helping companies automate decision-making, enhance user experiences and discover the latest efficiencies. Still, the road from adoption to lasting success is anything but smooth. The application of Artificial intelligence (AI) systems to real issues worldwide has several challenges. These include data quality and availability, adaptability to large along with dynamic workloads, algorithmic discrimination and influence on impartiality and trustworthiness, and their incorporation into current corporate environments. This work strives to bridge the discrepancy between the theoretical expectations of AI and its practical implementation by tackling critical barriers and proposing realistic, experience-based solutions. The paper is based on a mixed-method strategy combining an industry evaluation with one case investigation with a focus on how organizations deal with hurdles and turn first-time mistakes into potential areas for enhancements and improvements. Practical solutions including improved data governance frameworks, scalable architecture design, bias mitigation strategies as well as optimal integration models to connect AI systems with business processes are discussed in the case study. The results show that successful AI implementation is not only about advanced algorithms but also about a holistic approach that matches technical capabilities with organizational readiness. This paper presents a thorough review of these common challenges in AI systems and recommends concrete methods that practitioners can employ to increase reliability, scalability and ethical outcomes. Ultimately, it stresses that solving these problems is very less about reinventing the technology and more about improving its design, implementation, and maintenance in the actual world.

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