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Enterprise AI and Machine Learning | @ExpoDX #IoT #AI #MachineLearning #ArtificialIntelligence #DigitalTransformation
Optimizing business process improvements with AI and machine learning
By: Kevin Benedict
May. 23, 2019 02:30 PM
Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them. Often this means massive amounts of learning can be accomplished every second without stop 24x7x365. Code doesn't need to take weekends off, holidays, or sick time. Code doesn't get tired. It can recognize complex patterns, areas of potential improvement and problems in real-time (aka digital-time). Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time.
I believe the first step is to admit we have a problem - the problem is a difference in the speed that computers can operate, and the speeds in which us humans can process information and act upon it. The second is to understand what a solution might look like - how humans and computers can best integrate for business success, and the third is to have a plan to remedy it.
Imagine a scenario where you arrive at your desk on a Monday morning and find hundreds of recommendations for business process improvements from your AI and machine learning systems. Each of the recommendations might take weeks to socialize across the organization and implement. Now, imagine that happens every day of the year. We humans would be completely overwhelmed! In this scenario, humans are the weak links in business process optimization.
How are we then to best utilize AI and machine learning in a manner that benefits humans, and human powered organizations? I propose the answer lies in developing a powerful AI and machine learning platform that understands our goals and aspirations, can filter findings, run and test simulations, and then present a few select prioritized recommendations to human decision-makers. Simply feeding leaders with unlimited numbers of recommendations may actually paralyze, rather than empower them. What are needed are systems that won't overwhelm humans, but rather augment them.
In order for any of these AI and machine learning systems to actually work, the data that fuels these systems must first be available, accurate, normalized and timely - for many that is the essence of digital transformation. A digital transformation initiative often includes upgrading IT systems to become "optimized information logistics system" (OILS). Systems that ensure the right data is collected, available, analyzed and its meaning and context understood and utilized.
Once an organization has an OILS in place and the data available, then leaders must ensure their organizational structures and business processes are capable of responding at an operational tempo sufficient to capture the benefits and competitive advantages available from the insights derived from AI and machine learning. That is no small feat. It does no good, as we pointed out, to have recommendations that cannot be implemented.
Massive volumes of new data from sensors, mobile devices, embedded computers and online activities means there are enormous opportunities to learn and gain competitive advantages as a result of it, but only if our leaders, IT and business systems, and our organizations are capable of responding fast enough to capture the value. Understanding how to create OILS, how to utilize AI and machine learning to effectively augment our leaders, and then knowing how to create an organization able to respond fast enough to capture the competitive advantages in the data are the monumental tasks before us today.
Follow Kevin Benedict on Twitter @krbenedict
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