Many of us can all remember back to 2010 when “Big Data” was the buzzword and the “Vs of Big Data” helped characterize the ways business value was extracted from big data: volume, variety, velocity, and veracity. These dimensions provided an ontology that could be understood and more easily applied than the new and abstract concepts being introduced during Big Data’s earliest days.
We find ourselves in a similar hype cycle and edge of opportunity with Artificial Intelligence. Where we are in 2018 is not dissimilar from to the said inflection point of 2010… that breaking down the ways business can be extracted from AI will facilitate both adoption and disciplined application.
From thoughts gathered at the INFORMS Annual Meeting and across collaboration and conversation with other colleagues and friends in O.R., I suggest the following “7 As of AI” might be as follow:
- APPLIED – that APPLIED AI incorporates and applies methods by different fields such as machine learning, optimization, simulation, game theory, data analysis, statistics, data engineering, and computer science. Thus, APPLIED AI is a combination of Data Science and specialized knowledge.
- ASSISTED – that ASSISTED AI refers to remediation or controls presented to a human or decision-making system that presents scenarios, options, and richer context by a computer.
- AUGMENTED – that AUGMENTED AI superimposes information from multiple organic and non-organic systems to evaluate and depict information in a composite view.
- ACCELERATED – that ACCELERATED AI facilitates the automation, efficient designs, and technology levers to progress decision-making information more rapidly through processes.
- ADAPTED – that ADAPTED AI incorporates and applies methods to adjust strategy, technology, and other elements to satisfy unique requirements or evolving need.
- ACTIONED – that ACTIONED AI cues or launches an action based on the presentation of information. The bias for action supports the user’s preferred outcome.
- ADVANCED – that ADVANCED AI is a qualification of the system according to its examination and use of data and methods using sophisticated techniques, methods, tools, patterns, sensors, and routines to discover deeper insights, make predictions and generate recommendations.
From these, we see that the purpose of applying AI to any problem/opportunity is like what we purport and aspire to do in our fields of research and business: deliver value through the science of better.