AI in Practice and Implementation: Issues and Costs

Mayank Kejriwal
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Mayank Kejriwal: University of Southern California, Information Sciences Institute, Ste 1001

Chapter 2 in Artificial Intelligence for Industries of the Future, 2023, pp 25-45 from Springer

Abstract: Abstract Although the academic community has devised rigorous metrics for understanding what makes an AI system better than another in a specific application, it is not always evident that success on such metrics will translate seamlessly to improvement on business metrics, such as increased revenues, cash flow, and profits. As more (expensive) AI projects continue to be proposed, due to both internal and external pressures, business leaders are faced with the need to measure the return on investment (ROI) of such projects or to conduct valuation of such projects rigorously. In part, the problem arises because AI can be challenging to implement properly, and like many emerging technologies, the benefits may not be felt immediately or directly. In this chapter, we discuss the challenges of implementing practical AI systems, many of which stem from data acquisition and quality issues, followed by a deep dive into guidelines and principles for measuring ROI of, and objectively valuing, AI projects.

Keywords: Return on investment; ROI; Soft ROI; Hard ROI; PricewaterhouseCoopers; PwC; AI implementation challenges; Data acquisition; Data quality; Data quality dimensions; AI quality metrics; Privacy and compliance; AI ROI guidelines; Productivity puzzle; Valuation of AI projects (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-031-19039-1_2

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DOI: 10.1007/978-3-031-19039-1_2

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