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AI and equipment, ridiculous!


Saying artificial intelligence without context is as precise as mentioning that you understand "the cloud". I, like many of you, see a tremendous amount of potential when it comes to IoT and leveraging this information to provide greater value for our customers.  For me, AI and equipment mean the following:



Artificial intelligence (AI)

This is the most exciting area, it engages an industry which has been around for many years, data sciences.  Algorithms are at the nucleus of artificial intelligence, you may think of these as logical routines which have been programmed to understand inputs and provide some form of output.

  • For field service the first of these algorithms, and there are many types to meet different requirements, are machine learning. Machine learning, which is gaining a tremendous amount of popularity, is an algorithm which can learn over time.   Adjusting its patterns to achieve the greatest outcomes.
  • Deep learning allows us to scientifically understand rich content such as images and video.
  • Finally, and the most sophisticated, would be neural based networks; those that imitate the brain.

Artificial intelligence is certainly evolving and has a way to go to emulate the brain, one significant gap is the ability for AI to understand empathy.



Condition based

What is the intersection then of AI and equipment? In my mind, it should do with our ability to become a purely condition based service environment. Waiting for myriad conditions to trigger events which require our attention. When I think about it I consider:

  • Business conditions, our ability to make money, retaining and extend the terms of our contracts, etc.
  • Client impact, if they're uncomfortable or unhappy.
  • Proximity, where are people and what is the most valuable way to leverage those valuable resources.



Predictive modeling

It is tough to talk about predictive modeling at the beginning of your journey with artificial intelligence. The simple fact is that algorithms, those tools which will drive predictive modeling, need data samples over time. These algorithms and corresponding models require massive amounts of information, and this is generally where things get very tricky.



So possibly I've teased you into thinking that AI and equipment may be something you would like to explore in the future?  One tip for now; establish mechanisms to access data, both internal and external to your organization.



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Next post:  people business, can I really get objective measurements?

See all of the "last mile worker" posts here:  http://lastmileworker.com

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