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@article{182314,
        author = {Amit Ojha},
        title = {AI-Powered Product Intelligence: From Attributes to Action},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1998-2006},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182314},
        abstract = {AI has come a long way fast and totally changed how companies think about product intelligence. Basically, it’s shifted how they gather info, make sense of it, and use it to take action. This review just walks through how these AI systems have developed over the past decade, touching on stuff like NLP, computer vision, graph neural networks, and knowledge graphs that have driven a lot of this change. Although all this progress has added depth to analysis and helped bring more structure to messy product catalogs, plenty of challenges are still hanging around. When you look closely at real-world applications and research, you keep running into the same issues—like inconsistent data, a lack of shared standards, and the opaque, black-box nature of many AI decisions.
To tackle these persistent problems, the article suggests a more flexible, multi-modal framework that could help product intelligence systems work better in practice. It also points out a few important directions for future research—like making models easier to interpret, expanding their use across industries, and designing them with sustainability in mind—to keep up with the growing complexity of today’s product ecosystems.},
        keywords = {Artificial Intelligence; Product Intelligence, Attribute Extraction, Graph Neural Networks},
        month = {July},
        }