Accelerating the Enterprise Uptake of AI
Accelerating the Enterprise Uptake of AI


We are witnessing a great awakening of AI, and many companies are eager to embrace AI for competitive advantages. However, the adoption of AI for production use has been slow. Only a very small portion of companies have revenue-generating AI systems running today, and progressing from lab prototypes to production AI systems imposes many challenges. One fundamental reason for the slow adoption of AI is that AI applications are cognitive systems that learn from big data, which represents a completely different computing paradigm from traditional, programmable systems. While software engineering is a mature discipline that governs the development of traditional software, we don’t yet have clearly defined engineering processes that allow the development of AI applications in an agile, repeatable and cost-efficient manner. In addition, building production AI solutions involves a lot more than just preparing data and training and deploying machine learning models. There are many pragmatic concerns that need to be coped with, which should be part of standard AI engineering processes. In this talk, I will discuss the issues companies will have to address in order to productionize AI and realize its full benefits. I will make a case for an AI solution builder that isolates many complexities from the solution team and accelerates solution development and operations. I will also outline the capabilities such a solution builder should provide.


Hui Lei is Vice President and CTO of Cloud and Big Data at Futurewei Technologies. Previously he was Director and CTO of Watson Health Cloud at IBM, an IBM Distinguished Engineer, and an IBM Master Inventor. He is a Fellow of the IEEE, a past Editor-in-Chief of the IEEE Transactions on Cloud Computing, a past Chair of the IEEE Technical Committee on Business Informatics and Systems, and an author of over 80 patents. He has been recognized with the prestigious IEEE Computer Society Technical Achievement Award for his pioneering work on big data engineering. He holds a Ph.D. in Computer Science from Columbia University.

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