There are several prevalent difficulties in determining the return on investment (ROI) of artificial intelligence (AI), including:
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Quality and Accessibility of Data: The assurance of data quality and accessibility is a critical factor for precise ROI prediction models.
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Erroneous or Incomplete Data: The acquisition of correct and comprehensive data can pose a significant hurdle in ROI computation, influencing the profitability evaluation of investments.
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Challenges in Quantifying Intangible Advantages: The task of measuring intangible advantages such as enhanced customer satisfaction presents a difficulty in ROI analysis, affecting the thorough appraisal of investments.
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Interpretability Deficiency: A lack of interpretability in AI algorithms utilized for ROI prediction models can serve as a constraint, impeding the comprehension of forecasts.
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Assessing Each AI Project on its Own Merits: Perceiving AI projects individually rather than collectively can obstruct an all-encompassing assessment of ROI.
To surmount these hurdles, companies should give precedence to data purification, allocate resources to data management tools, contemplate long-term performance tracking, and convey outcomes transparently to stakeholders.
