 Aligning AI Investments with Business ProblemsRohan Sharma Chapter Chapter 17 in AI and the Boardroom, 2024, pp 215-223 from Springer Abstract: Abstract How can organizations ensure AI investments are truly impactful? Developing an effective AI strategy involves matching the level of investment with the complexity of the business problem. The problem–investment matrix serves as a practical tool for categorizing AI initiatives based on their scope and investment needs, providing a clear roadmap for strategic AI deployment. This chapter introduces four tiers of AI applications: fundamental automation tasks, departmental enhancements, advanced analytical tools, and enterprisewide transformations. By structuring AI initiatives into these categories, businesses can make informed decisions about where to allocate resources for maximum impact. For instance, fundamental AI applications like automating website copywriting require low investments but yield significant efficiency gains, while enterprise-level AI initiatives demand higher investments but offer transformative benefits across the organization. Key takeaway: By understanding the relationship between business needs and AI investment levels, companies can deploy AI effectively and ensure each initiative delivers value. Is your organization ready to optimize AI investments for strategic advantage? Date: 2024 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:979-8-8688-0796-1_17 Ordering information: This item can be ordered from DOI: 10.1007/979-8-8688-0796-1_17 Access Statistics for this chapter More chapters in Springer Books from Springer |
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