Disruptive Technologies – A 2021 Update
first introduced to the space. In this section, we examine the current state of the various disruptive technologies and use cases to which they are being applied. This is in no way intended to
A ComTechAdvisory Report
be an exhaustive listing of each vendor or product that might fall under each technology category but is more intended to provide a brief snapshot of the state of play for each and provide a glimpse into how they are being applied and a sense for their future potential.
Artificial Intelligence and Machine Learning An excellent definition of AI can be found at IBM’s website1 where AI is described as “enable(ing) computers and machines to mimic the perception, learning, problem-solving, and decision-making capabilities of the human mind.” Machine learning is also defined there as a branch of AI that “focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.” Deep Learning is a type of ML in which the application teaches itself to perform a task with increasing accuracy without human intervention. AI and ML use across the commodities complex seems to have expanded rapidly in the last couple of years or so. Often combined with workflow or automation, AI
and ML can form a later component of the digitalization activities taking place across the industry or, in an ad hoc mode, to help optimize specific activities, increase efficiencies and reduce costs. In some instances, it is used as a first pass attempt to automate routine tasks or in data reconciliations, leaving exceptions for human intervention. The move to this form of exception management has obvious benefits in time savings and improved process efficiencies, as well as making better use of staff’s time. Although by no means an exhaustive list, the following use cases are among the most common examples of the deployment of AI in commodities that we are currently aware of.
Short-term market forecasting In fact, demand and price forecasting is rapidly becoming an overcrowded market with suppliers of AI and ML enhanced short-term market data seemingly cropping up everywhere. These vendors, by using various sensing imagery such as arial and/or satellite 1
images, and bringing together other data including macroeconomic, econometric, production and inventories data, and even social media data, can offer AI enhanced price forecasts across several classes of commodities, ranging from agriculture to electric
https://www.ibm.com/cloud/learn/what-is-artificial-intelligence
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