A Team-Based Workshop to Capture Organizational Knowledge for Identifying AI Proof-of-Value Projects

Francois Blackburn-Grenon; Alain Abran; Michel Rioux; Tony Wong

doi:10.1109/EMR.2021.3063688

A Team-Based Workshop to Capture Organizational Knowledge for Identifying AI Proof-of-Value Projects

Francois Blackburn-Grenon
, Alain Abran
, Michel Rioux
, Tony Wong

École de technologie supérieure

Résultats de recherche: Contribution à un journal › Article publié dans une revue, révisé par les pairs › Revue par des pairs

8 Citations (Scopus)

Résumé

While industrial firms realize the importance of Industry 4.0, many have not yet started implementing the technologies required to harvest the benefits. To enable the adoption of artificial intelligence applications for Industry 4.0 and to address the gap between advances in technologies and their adoption in industry, this article presents a team-based workshop to capture organizational knowledge for identifying relevant artificial intelligence proof-of-value (AI POV) projects based on system engineering knowledge management tools. The aim of this one-day workshop is to identify a potential AI POV within an organization, including its starting point and initial expected savings.

langue originale	Anglais
Numéro d'article	9369031
Pages (de - à)	181-195
Nombre de pages	15
journal	IEEE Engineering Management Review
Volume	49
Numéro de publication	2
Les DOIs	https://doi.org/10.1109/EMR.2021.3063688
état	Publié - 1 avr. 2021

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 9 – Industrie, innovation et infrastructure

Accès au document

10.1109/EMR.2021.3063688

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Contient cette citation

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title = "A Team-Based Workshop to Capture Organizational Knowledge for Identifying AI Proof-of-Value Projects",

abstract = "While industrial firms realize the importance of Industry 4.0, many have not yet started implementing the technologies required to harvest the benefits. To enable the adoption of artificial intelligence applications for Industry 4.0 and to address the gap between advances in technologies and their adoption in industry, this article presents a team-based workshop to capture organizational knowledge for identifying relevant artificial intelligence proof-of-value (AI POV) projects based on system engineering knowledge management tools. The aim of this one-day workshop is to identify a potential AI POV within an organization, including its starting point and initial expected savings.",

keywords = "Artificial intelligence (AI) application, Industry 4.0 (I4.0), industrial and system engineering, innovation",

author = "Francois Blackburn-Grenon and Alain Abran and Michel Rioux and Tony Wong",

note = "Publisher Copyright: {\textcopyright} 1973-2011 IEEE.",

year = "2021",

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doi = "10.1109/EMR.2021.3063688",

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}

A Team-Based Workshop to Capture Organizational Knowledge for Identifying AI Proof-of-Value Projects. / Blackburn-Grenon, Francois; Abran, Alain ; Rioux, Michel et al.
Dans: IEEE Engineering Management Review, Vol 49, Numéro 2, 9369031, 01.04.2021, p. 181-195.

Résultats de recherche: Contribution à un journal › Article publié dans une revue, révisé par les pairs › Revue par des pairs

TY - JOUR

T1 - A Team-Based Workshop to Capture Organizational Knowledge for Identifying AI Proof-of-Value Projects

AU - Blackburn-Grenon, Francois

AU - Abran, Alain

AU - Rioux, Michel

AU - Wong, Tony

PY - 2021/4/1

Y1 - 2021/4/1

N2 - While industrial firms realize the importance of Industry 4.0, many have not yet started implementing the technologies required to harvest the benefits. To enable the adoption of artificial intelligence applications for Industry 4.0 and to address the gap between advances in technologies and their adoption in industry, this article presents a team-based workshop to capture organizational knowledge for identifying relevant artificial intelligence proof-of-value (AI POV) projects based on system engineering knowledge management tools. The aim of this one-day workshop is to identify a potential AI POV within an organization, including its starting point and initial expected savings.

AB - While industrial firms realize the importance of Industry 4.0, many have not yet started implementing the technologies required to harvest the benefits. To enable the adoption of artificial intelligence applications for Industry 4.0 and to address the gap between advances in technologies and their adoption in industry, this article presents a team-based workshop to capture organizational knowledge for identifying relevant artificial intelligence proof-of-value (AI POV) projects based on system engineering knowledge management tools. The aim of this one-day workshop is to identify a potential AI POV within an organization, including its starting point and initial expected savings.

KW - Artificial intelligence (AI) application

KW - Industry 4.0 (I4.0)

KW - industrial and system engineering

KW - innovation

UR - https://www.scopus.com/pages/publications/85102264271

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JO - IEEE Engineering Management Review

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