An AI decision-making framework for business value maximization

被引:2
作者
Gudigantala, Naveen [1 ,4 ]
Madhavaram, Sreedhar [2 ]
Bicen, Pelin [3 ]
机构
[1] Univ Portland, Robert B Pamplin Sch Business Adm, Portland, OR USA
[2] Texas Tech Univ, Jerry S Rawls Coll Business Adm, Lubbock, TX USA
[3] Suffolk Univ, Sawyer Business Sch, Dept Mkt, Boston, MA USA
[4] Univ Portland, Robert B Pamplin Sch Business Adm, Portland, OR 97203 USA
关键词
ARTIFICIAL-INTELLIGENCE; BIG DATA;
D O I
10.1002/aaai.12076
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article addresses a key question of why businesses are failing to maximize business value from their artificial intelligence (AI) investments and proposes a strategic decision-making framework for AI decision-making to address this problem. We suggest that a firm's business strategy must drive AI-driven business outcomes and measurements, which in turn should drive the AI implementation decisions. Very often, we find that businesses fail to successfully cast business problems into AI problems. To bridge this gap, we propose that firms use a performance management system such as objectives and key results (OKRs) to ensure that the business and AI goals & objectives are well defined, tightly aligned, and made transparent across the company, and the AI efforts are approached in an integrated manner by the different parts of a firm. We use McDonald's use of AI initiatives as a business use case to demonstrate support for our AI decision-making framework. We argue that using the business strategy as a primary driver will enable firms to solve the right problems using AI, turning it to be a source of technology innovation and competitive advantage.
引用
收藏
页码:67 / 84
页数:18
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