Towards a capability assessment model for the comprehension and adoption of AI in organisations

Tom Butler, Angelina Espinoza-Limón and Selja Seppälä
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Tom Butler: Department of Business Information Systems, University College Cork, Cork City, Ireland
Angelina Espinoza-Limón: Department of Business Information Systems, University College Cork, Cork City, Ireland
Selja Seppälä: Department of Business Information Systems, University College Cork, Cork City, Ireland

Journal of AI, Robotics & Workplace Automation, 2021, vol. 1, issue 1, 18-33

Abstract: The comprehension and adoption of artificial intelligence (AI) are beset with practical and ethical problems. This paper presents a five-level AI capability assessment model (AI-CAM) and a related AI capabilities matrix (AI-CM) to assist practitioners in AI comprehension and adoption. These practical tools were developed with business executives, technologists and other organisational stakeholders in mind. They are founded on a comprehensive conception of AI compared with those in other AI adoption models and are also open-source artefacts. Thus, the AI-CAM and AI-CM present an accessible resource to help inform organisational decision makers on the capability requirements for: 1) AI-based data analytics use cases based on machine learning technologies; 2) knowledge representation to engineer and represent data, information and knowledge using semantic technologies; and 3) AI-based solutions that seek to emulate human reasoning and decision making. The AI-CAM covers the core capability dimensions (business, data, technology, organisation, AI skills, risks and ethical considerations) required at the five levels of capability maturity to achieve optimal use of AI in organisations. The AI-CM details the related individual and team-level capabilities needed to reach each level in organisational AI capability; it therefore extends and enriches existing perspectives by introducing knowledge and skills requirements at all levels of an organisation. It posits three levels of AI proficiency: 1) basic, for operational users who interact with AI and participate in AI adoption; 2) advanced, for professionals who are charged with comprehending AI and developing related business models and strategies; and 3) expert, for computer engineers, data scientists and knowledge engineers participating in the design and implementation of AI-based technologies to support business use cases. In conclusion, the AI-CAM and AI-CM present a valuable resource for practitioners, businesses and technologists looking to innovate using AI technologies and maximise the return to their organisations.

Keywords: artificial intelligence; AI; capability assessment model; AI adoption; AI skills; AI capabilities; AI literacy (search for similar items in EconPapers)
JEL-codes: G2 M15 (search for similar items in EconPapers)
Date: 2021
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