Three Key Success Factors for Achieving Maximum Business Value with Artificial Intelligence
Artificial intelligence is making tremendous impact already in the world. Some of that impact is indirect and comes from the anticipation of things that may sound like science fiction now, which has led to massive investments in ideas and people. Much of the impact is direct and comes from applying existing AI capabilities to current processes 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.
Firstly, to leverage AI successfully, you need to be able to measure the system or process you hope to improve or create with AI. Without good measurement, it will be difficult to understand the return on investment (ROI) from AI, and it is possible that your solutions is not as good as it could be because that measurement is not driving improvements in the AI technology. Read more about AI and measurement.
Secondly, experts are critical to AI applications. Data, compute and analytic technologies are also important. But experts know the domain the best and can identify where value is found. Experts can identify the good data and tell you how to make it better. Experts ultimately will be using the recommendations and providing 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.
Lastly, 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 interact with knowledge, so understanding how the computer (AI) understands knowledge is critical. But knowledge, and knowledge representation in the AI system, do not need to be confusing things. Read more about knowledge representation.