Tuesday’s Tip: Seven Factors For Precision Decisions In Artificial Intelligence

Published on August 23, 2016 by R "Ray" Wang

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The Rush To Artificial Intelligence Will Enable Augmented Humanity

While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1).  Searching for a competitive advantage and fearful of disruption, board rooms and CXO’s have rushed to artificial intelligence as the next big thing.  The investment in pilots for AI’s subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services.

Figure 1. Digital Systems Ultimately Will Achieve Artificial Intelligence

@rwang0 5 stages AI Cognitive

Early Adopters Realize That Good AI Requires Seven Success Factors

With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists.  What market leaders and fast followers have discovered are seven traits that require nurturing (see Figure 2):

  1. Large corpus of data.  The battle for large data sets has nothing to do with having more data.  The goal is to build the largest graph that maps the connections to data.  More data should improve the precision of insights and allow for more patterns to emerge.
  2. Massive compute power.  Winners will have access or own cheap compute power.  The ultimate metric for AI rests in pricing not by just compute power, but potentially cost per kilowatt hour.  So the cheapest rate of compute power may determine the cost structure for AI smart services.
  3. Time.  There is no substitute for time.  Early adopters gain an advantage of time.  Algorithms need time to improve.  Data set gathering requires time for better precision.  More interactions in the network depend on time.
  4. Awesome math talent.  The discovery of patterns, creation of new algorithms, and the ability to apply human intuition to compute requires great math talent.  People enable artificial intelligence.  Algorithms are only as good as the math talent.  Success will require the hiring of digital artisans – those who can balance right brain and left brain expertise.
  5. Industry specific expertise.  Industry vertical experience will emerge as the key differentiator in AI smart services.   The more advanced and specialized the AI system, the more relevance to the end users.
  6. Natural user interfaces and user experiences.  Expect AI systems to mimic human interaction going forward.  From sensory capabilities, to visualization, to voice, to gesture, the interfaces will improve in human and natural like capabilities.
  7. Recommendation engines.  The output of AI comes to precision decisions.  AI systems augment humanity.  The recommendation engines that emerge will enable choices, accelerate decision making, and ultimately provide filters that deliver situational awareness.

 

Figure 2.  Seven Factors In Good Artificial Intelligence

@rwang0 #AI Market 7 factors

 

The Bottom Line: Expect AI Networks To Emerge

The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.  Why? Access to huge corpus of data and massive compute power fall in the hands of the few.  Expect the internet giants, cloud computing behemoths, and the social networking leaders to have an edge in delivering AI:

  • Alibaba
  • Amazon
  • Apple
  • Baidu
  • Facebook
  • Google
  • Microsoft
  • Oracle
  • QQ
  • Sina
  • Tabao
  • Weibo
  • Yahoo
  • Yandex

Moreover, the value in AI will come from the smart services that emerge through digital transformation projects.  More than just automation, these AI driven smart services will power the future business models.  Thus, winners will secure the seven critical success factors and create the network economies that will dominate their industries.

Your POV.

Are you ready to unlock the powers of Artificial Intelligence?  What’s your entry point? Machine learning, natural language processing, topological data analyses? Would you like to hear what other organizations have embarked on?  Would you like us to present to your boardroom?  Learn how non-digital organizations can apply an AI road map to disrupt digital businesses in the best-selling Harvard Business Review Press book Disrupting Digital. 

Add your comments to the blog or reach me via email: R (at) ConstellationR (dot) com or R (at) SoftwareInsider (dot) org.

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