Paper Details

Name: Shamsuddin N. Ladha
Country: India

Year: 2025
Volume: Special Issue: Volume-12, Issue-1, Year: 2025 (January-June)
Page Number: 117-120
DOI: https://doi.org/10.5281/zenodo.15356822

The rapid adoption of Artificial Intelligence (AI) and Generative AI (GenAI) across
industries has led to a surge in AI-driven solutions. However, a sizable proportion of these
projects fail to deliver on their expected value due to inadequate problem formulation,
unrealistic expectations, and flawed implementation strategies. This paper presents a critical
analysis of the necessity to carefully assess the viability of AI solutions before deployment.
Through case studies spanning diverse applications—including information extraction,
chatbots, and voice agents—we examine key factors that determine AI project success or
failure.
We highlight common pitfalls such as data insufficiency, lack of domain adaptation,
overreliance on black-box models, and the misalignment of AI capabilities with business
needs. Drawing from real-world experiences, we provide a structured approach to evaluating
whether AI is the appropriate solution for a given problem and, if so, how to optimize its
design, deployment, and lifecycle management. We emphasize the importance of rigorous
feasibility assessments, stakeholder alignment, and iterative model improvement to ensure AI
solutions remain effective and sustainable.
By synthesizing insights from multiple domains, this paper provides actionable guidelines for
businesses, researchers, practitioners, and decision-makers aiming to maximize the success
rate of AI initiatives. The findings underscore the need for a pragmatic, evidence-driven
approach to AI adoption, moving beyond the hype to build robust, value-driven solutions.
Keywords: AI project failure, Generative AI, feasibility assessment, information extraction,
chatbots, voice agents, AI deployment strategies.