EconPapers    
Economics at your fingertips  
 

Determining AI Maturity for Your Organization

Rohan Sharma

Chapter Chapter 8 in AI and the Boardroom, 2024, pp 95-104 from Springer

Abstract: Abstract AI maturity refers to an organization’s progress in effectively deploying AI, measured through increased capabilities and ROI. This chapter introduces a maturity model that defines seven levels of AI sophistication—from foundational data preparation to advanced multiagent systems. Each level builds upon the previous, enhancing AI’s impact and aligning it with strategic business goals. At the core of AI maturity is responsible AI practice, emphasizing ethical deployment, data privacy, and compliance. Achieving higher maturity levels requires investments in data infrastructure, fostering crossfunctional collaboration, and adopting a phased approach that balances innovation with responsibility. Key takeaway: Advancing through the AI maturity levels can drive significant business value, but it requires deliberate investments, responsible practices, and strategic leadership to succeed. Is your organization prepared to navigate and lead in this journey of AI maturity?

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:979-8-8688-0796-1_8

Ordering information: This item can be ordered from
http://www.springer.com/9798868807961

DOI: 10.1007/979-8-8688-0796-1_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-23
Handle: RePEc:spr:sprchp:979-8-8688-0796-1_8