@article {1392,
	title = {AI-Driven Digital Platform Innovation},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {10/2020},
	pages = {4-15},
	publisher = {Talent First Network},
	chapter = {4},
	address = {Ottawa},
	abstract = {Artificial Intelligence (AI) innovation becomes useful today when it enriches decision-making that is enhanced by applications of big data (BD), advanced analytics (AA), and some element of human interaction using digital platforms. This research aims to investigate the potential combination of AI, BD and AA for digital business platforms. In doing so, it develops a multi-dimensional AI-driven platform innovation framework with AI/BD/AA innovation value chain and related levels of AI maturity improvement. The framework can be used with a focus on the data-driven human-machine relationship and the application of AI at different levels of an AI-driven digital platform technology stack.},
	keywords = {Advanced Analytics, AI maturity, AI value chain, AI-driven platform innovation, Artificial Intelligence (AI), big data, enterprise platform},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1392},
	url = {timreview.ca/article/1392},
	author = {Sergey A. Yablonsky}
}
@article {1399,
	title = {Integrated AI and Innovation Management: The Beginning of a Beautiful Friendship},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {11/2020},
	pages = {5-18},
	publisher = {Talent First Network},
	chapter = {5},
	address = {Ottawa},
	abstract = {There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.},
	keywords = {AI innovation, AI maturity, artificial intelligence, IMS ISO 56002, Innovation management, maturity model},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1399},
	url = {timreview.ca/article/1399},
	author = {Nina Bozic Yams and Valerie Richardson and Galina Esther Shubina and Sandor Albrecht and Daniel Gillblad}
}
@article {1288,
	title = {Multidimensional Data-Driven Artificial Intelligence Innovation},
	journal = {Technology Innovation Management Review},
	volume = {9},
	year = {2019},
	month = {12/2019},
	pages = {16-28},
	publisher = {Talent First Network},
	chapter = {16},
	address = {Ottawa},
	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.},
	keywords = {Advanced Analytics, AI maturity, AI value chain, Artificial Intelligence (AI), big data, enterprise platform, innovation},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1288},
	url = {timreview.ca/article/1288},
	author = {Sergey A. Yablonsky}
}
