Three Key Factors for Successful AI Projects

Summit Track: 
Business Insights

AI is transforming the products we build and the way we do business. AI using images and video is already at work in our smart home devices, our smartphones, and many of our vehicles. But despite some clear successes, AI projects fail 85% of the time, according to Gartner. Fortunately, the industry is rapidly accumulating experience that we can learn from in order to find ways to reduce risk in AI projects. In this talk, we present three key factors for successful AI projects, especially those using visual data. The first factor is incorporating domain expertise into the design of the AI solution, rather than relying purely on brute-force application of generalized algorithms. The second success factor is obtaining the right quantities and types of data at each stage of the development process. The third factor is creating AI workflows that enable repeatable and deployable results. We illustrate the importance of these factors using case studies from diverse applications including automated driving, crop harvesting and tunnel excavation.


Bruce Tannenbaum

Manager, Technical Marketing, AI Applications, MathWorks

Bruce is the manager of technical marketing at MathWorks for AI applications. He has 15 years of experience at MathWorks, which included the creation and launch of Computer Vision Toolbox. Earlier in his career, Bruce worked on digital cameras, image and video compression, and computer vision. He also participated in standardization efforts for MPEG-4 and JPEG-2000. Bruce holds an MBA from Babson College, an MSE from University of Michigan and a BSEE from Pennsylvania State University.

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