Structuring AI Teams for Success: Models for Scaling AI Operations

Rohan Sharma

Chapter Chapter 9 in AI and the Boardroom, 2024, pp 105-118 from Springer

Abstract: Abstract An effective organizational structure is vital for deploying and scaling AI successfully. This chapter explores various models for structuring AI teams—Functional, Centralized, Decentralized, Factory, Center of Excellence (CoE), and Consulting—each with its own strengths and challenges. Selecting the right model depends on factors such as organizational size, industry, strategic goals, and resource availability. To optimize AI deployment, organizations must choose a structure that aligns with strategic objectives, supports collaboration, and balances technical and business priorities. Establishing clear roles, governance frameworks, and cross-functional collaboration, along with continuous training, helps in scaling AI initiatives effectively. A well-chosen AI team structure can enable organizations to transform AI from isolated projects into integral drivers of business growth.

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_9

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

DOI: 10.1007/979-8-8688-0796-1_9

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 ().