Acknowledging the limits of machine learning during AI-enabled transformation
Conclusion: Increasingly, leaders in the field of AI adoption are calling out the limitations of the current machine learning techniques as they relate to knowledge representation and predictive analysis.
Organisations seeking to adopt machine learning as part of their AI-enabled transformation programs should ensure they fully understand these limitations to avoid unproductive investments driven by hype rather than reality by expanding their definitions of machine learning to include the use of graph networks and social physics solutions.
About The Advisor
Sam Higgins was an IBRS advisor between 2017 and 2020 with over 20 years of both tactical and strategic experience in the application of information and communications technology (ICT) to achieve business outcomes from large complex organisations. Through previous roles as a leading ICT executive, strategist, architect, industry analyst, program consultant and advisor, Sam has developed an extensive knowledge of key markets including as-a-Service (Cloud) computing, enterprise architecture (including service-orientation and information management), enterprise applications and development, business intelligence; along with ICT management and governance practices such as ICT planning, strategic sourcing, portfolio and project management. Sam’s knowledge of service-oriented architecture and associated business models is widely recognised, and he was a contributing author on the Paul Allen book Service-orientation: Winning Strategies and Best Practices, released in 2006 by Cambridge University Press. As the former Research Director for Longhaus he undertook the first in depth research into the implications of cloud computing and other “as-a-Service” ICT offerings on the Australian and near shore markets. The 2010 report entitled, Defining cloud computing highlights provider gaps in the Australian ICT market, was widely reported in both the online ICT industry press and mainstream media.