Multidimensional Data-Driven Artificial Intelligence Innovation.

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Multidimensional Data-Driven Artificial Intelligence Innovation. / Yablonsky, Sergey A. .

In: Technology Innovation Management Review, Vol. 9, No. 12, 12.2019, p. 16-28.

Research output: Contribution to journal › Article › peer-review

Author

Yablonsky, Sergey A. . / Multidimensional Data-Driven Artificial Intelligence Innovation. In: Technology Innovation Management Review. 2019 ; Vol. 9, No. 12. pp. 16-28.

BibTeX

@article{ea0e707a7ee74d1e8868b4f8e93b1d07,

title = "Multidimensional Data-Driven Artificial Intelligence Innovation.",

abstract = "This is a critical time for the development and adoption of Artificial Intelligence (AI). The field has existed since the 1950s and is only now emerging as viable for commercial markets. Many enterprises are placing bets on AI that will determine their future. Today AI innovation becomes useful when it enriches decision-making that is enhanced by applying Big Data (BD) and Advanced Analytics (AA), with some element of human interaction using digital platforms. This research investigates an opportunity for cross-fertilization between AI, BD, and AA with related disciplines. The paper aims to investigate the potential relationship of AI, BD, and AA with digital business platforms. In doing so, it develops a multidimensional BD-driven AI innovation taxonomy framework with an AA/BD/AA innovation value chain, related levels of BD, and analytics maturity improvement. This framework can be used with a focus on data-driven human-machine relationships, and applying AI at different levels of data driven automation maturity.",

author = "Yablonsky, {Sergey A.}",

note = "Yablonsky, S. A. 2019. Multidimensional Data-Driven Artificial Intelligence Innovation. Technology Innovation Management Review, 9(12): 16-28. http://doi.org/10.22215/timreview/1288",

year = "2019",

month = dec,

doi = "10.22215/timreview/1288",

language = "English",

volume = "9",

pages = "16--28",

journal = "Technology Innovation Management Review",

issn = "1927-0321",

publisher = "Talent First Network",

number = "12",

}

RIS

TY - JOUR

T1 - Multidimensional Data-Driven Artificial Intelligence Innovation.

AU - Yablonsky, Sergey A.

N1 - Yablonsky, S. A. 2019. Multidimensional Data-Driven Artificial Intelligence Innovation. Technology Innovation Management Review, 9(12): 16-28. http://doi.org/10.22215/timreview/1288

PY - 2019/12

Y1 - 2019/12

N2 - This is a critical time for the development and adoption of Artificial Intelligence (AI). The field has existed since the 1950s and is only now emerging as viable for commercial markets. Many enterprises are placing bets on AI that will determine their future. Today AI innovation becomes useful when it enriches decision-making that is enhanced by applying Big Data (BD) and Advanced Analytics (AA), with some element of human interaction using digital platforms. This research investigates an opportunity for cross-fertilization between AI, BD, and AA with related disciplines. The paper aims to investigate the potential relationship of AI, BD, and AA with digital business platforms. In doing so, it develops a multidimensional BD-driven AI innovation taxonomy framework with an AA/BD/AA innovation value chain, related levels of BD, and analytics maturity improvement. This framework can be used with a focus on data-driven human-machine relationships, and applying AI at different levels of data driven automation maturity.

AB - This is a critical time for the development and adoption of Artificial Intelligence (AI). The field has existed since the 1950s and is only now emerging as viable for commercial markets. Many enterprises are placing bets on AI that will determine their future. Today AI innovation becomes useful when it enriches decision-making that is enhanced by applying Big Data (BD) and Advanced Analytics (AA), with some element of human interaction using digital platforms. This research investigates an opportunity for cross-fertilization between AI, BD, and AA with related disciplines. The paper aims to investigate the potential relationship of AI, BD, and AA with digital business platforms. In doing so, it develops a multidimensional BD-driven AI innovation taxonomy framework with an AA/BD/AA innovation value chain, related levels of BD, and analytics maturity improvement. This framework can be used with a focus on data-driven human-machine relationships, and applying AI at different levels of data driven automation maturity.

UR - https://timreview.ca/article/1288

UR - http://doi.org/10.22215/timreview/1288

U2 - 10.22215/timreview/1288

DO - 10.22215/timreview/1288

M3 - Article

VL - 9

SP - 16

EP - 28

JO - Technology Innovation Management Review

JF - Technology Innovation Management Review

SN - 1927-0321

IS - 12

ER -

ID: 51755294