Three Key Success Factors for Achieving Maximum Business Value with Artificial Intelligence

Author: Steve Gustafson
Three Key Success Factors for Achieving Maximum Business Value with Artificial Intelligence

Artificial intelligence is already making a tremendous impact in the world.  Some of that impact is indirect, coming from the anticipation of things that may sound like science fiction now, and has led to massive investments in ideas and people.  Much of the impact is direct, coming from the application of existing AI capabilities to current processes, in order to improve customer satisfaction, decision-making, and productivity of people and supply chains.  In both cases, there is still a lot of confusion about what AI is and what it takes to use.   In recent Forbes.com articles, I discuss what I believe are three major steps toward achieving impact with AI.

First, to leverage AI successfully, you need to be able to measure the system or process you hope to create or improve with AI.  Without good measurement, it will be difficult to understand the return on investment (ROI) from AI. Also, it is possible that your solutions will not be as good as they could be because that measurement is not driving improvements in the AI technology.  Read more about AI and measurement.

Second, subject-matter experts are critical to the success of AI applications.  Data, compute, and analytic technologies are important, but experts know the domain best and can identify where value is found.  Experts can determine the good data and tell you how to make it better.  Experts will use the recommendations and provide feedback to the AI system to make it better.   AI technology is already poised to work with experts, so start working on AI applications that incorporate experts into human-in-the-loop value chains.  Read more about how experts are important to AI.

Third, to make the most of AI technology and institutionalize it in your domain requires understanding the core differentiation of an AI solution: knowledge capture and re-use, and more precisely, a computer representation of knowledge.  AI can sometimes look like magic because it does things that people do when it captures and applies knowledge.  For example, AI can recommend books or products that complement recent purchases because it knows that these things go together, just like an employee at a bookstore would know.   Enabling people to interact, manage, and improve AI systems will require them to work with knowledge, so understanding how the computer (AI) comprehends knowledge is critical.  But knowledge—and knowledge representation in the AI system— do not need to be confusing.   Read more about knowledge representation.



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