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Merging Humans with Enterprise AI and Machine Learning Systems

November 2, 2016   Mobile and Cloud
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 us humans can operate.  The second is to understand what a solution might look like – how humans and computers can best integrate and operate together for business success, and the third is to have a strategy and plan.
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, or read more of his articles on digital transformation strategies here:
  1. In Defense of the Human Experience in a Digital World
  2. Profits that Kill in the Age of Digital Transformation
  3. Competing in Future Time and Digital Transformation
  4. Digital Hope and Redemption in the Digital Age
  5. Digital Transformation and the Role of Faster
  6. Digital Transformation and the Law of Thermodynamics
  7. Jettison the Heavy Baggage and Digitally Tranform
  8. Digital Transformation – The Dark Side
  9. Business is Not as Usual in Digital Transformation
  10. 15 Rules for Winning in Digital Transformation
  11. The End Goal of Digital Transformation
  12. Digital Transformation and the Ignorance Penalty
  13. Surviving the Three Ages of Digital Transformation
  14. From Digital to Hyper-Transformation
  15. Believers, Non-Believers and Digital Transformation
  16. Forces Driving the Digital Transformation Era
  17. Digital Transformation Requires Agility and Energy Measurement
  18. A Doctrine for Digital Transformation is Required
  19. The Advantages of Advantage in Digital Transformation
  20. Digital Transformation and Its Role in Mobility and Competition
  21. Digital Transformation – A Revolution in Precision Through IoT, Analytics and Mobility
  22. Competing in Digital Transformation and Mobility
  23. Ambiguity and Digital Transformation
  24. Digital Transformation and Mobility – Macro-Forces and Timing
  25. Mobile and IoT Technologies are Inside the Curve of Human Time

************************************************************************

Kevin Benedict
Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler
View my profile on LinkedIn
Follow me on Twitter @krbenedict
Subscribe to Kevin’sYouTube Channel
Join the Linkedin Group Strategic Enterprise Mobility
Join the Google+ Community Mobile Enterprise Strategies


***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I am a mobility and digital transformation analyst, consultant and writer. I work with and have worked with many of the companies mentioned in my articles.
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Mobile Enterprise and Digital Strategies

Enterprise, Humans, Learning, Machine, Merging, Systems
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