IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/979-8-8688-0796-1_13.html
   My bibliography  Save this book chapter

AI KPIs and OKRs: Measuring Success and Maximizing Impact

In: AI and the Boardroom

Author

Listed:
  • Rohan Sharma

Abstract

How do you determine if your AI investments are truly delivering value? In the fast-paced world of AI, accurately measuring success is crucial for ensuring that initiatives align with business goals and deliver real impact. This chapter delves into the importance of establishing precise metrics and Key Performance Indicators (KPIs) to validate the effectiveness of AI, guide improvements, and justify ongoing investment. We explore the types of metrics essential for evaluating AI success, from efficiency and accuracy to financial impact and customer satisfaction. The chapter also provides insights into selecting the right metrics based on project objectives, ensuring that AI initiatives remain aligned with business priorities. By defining the right measures, organizations can validate ROI, identify areas for enhancement, and maintain strategic alignment. Key takeaway: The right metrics transform AI from a mere experiment to a strategic asset. Are you using the right indicators to track your AI’s success and align it with your broader business vision?

Suggested Citation

  • Rohan Sharma, 2024. "AI KPIs and OKRs: Measuring Success and Maximizing Impact," Springer Books, in: AI and the Boardroom, chapter 0, pages 151-170, Springer.
    • Handle: RePEc:spr:sprchp:979-8-8688-0796-1_13
    DOI: 10.1007/979-8-8688-0796-1_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:979-8-8688-0796-1_13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.