Often, It’s not About the AI
Narrowly focused task and domain specific AI has been applied successfully for more than twenty five years, and has produced immense value in industry and government. It doesn’t lead directly to artificial general intelligence (AGI), but it does have real problem solving value. It is useful to note that many of the reasons why some otherwise meritorious AI applications fail have nothing to do with the AI per se, but rather, with systems engineering and organizational issues. For example: the domain expert is pulled out to work on more critical projects; the application champion rotates out of his/her position; or the sponsor changes priorities. A system may not make it past an initial pilot test for logistical vs. substantive technical reasons. Some embedded AI systems may work well for years on a software platform that is orphaned and porting it would be prohibitively expensive. A system may work well in a pilot test, but it might not scale for huge numbers of users without extensive performance optimization. The core AI system may be great but the user interface could be suboptimal. The delivered application system might work well, but it could be hard to maintain internally. The system may work according to the sponsor’s requirements, but it might not be applied to the part of the problem that delivers the largest economic results; or the system might not produce enough visible organizational benefits to protect it in subsequent budget battles. Alternatively, the documented results may be quite strong, but may not be communicated effectively across organizational boundaries. All software projects are vulnerable to one or more of these problems. The fact that some software projects have a relatively small percentage of their total code in embedded AI methods doesn’t make them an exception. However, knowing about these potential problems could help AI project teams to be proactive about avoiding them whenever possible.
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