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Aligning AI Investments with Business Problems

In: AI and the Boardroom

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  • Rohan Sharma

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?

Suggested Citation

  • Rohan Sharma, 2024. "Aligning AI Investments with Business Problems," Springer Books, in: AI and the Boardroom, chapter 0, pages 215-223, Springer.
    • Handle: RePEc:spr:sprchp:979-8-8688-0796-1_17
    DOI: 10.1007/979-8-8688-0796-1_17
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