Before embarking on an AI initiative, companies must understand which technologies perform what types of tasks, and the strengths and limitations of each. Rule-based expert systems and robotic process automation, for example, are transparent in how they do their work, but neither is capable of learning and improving. Deep learning, on the other hand, is great at learning from large volumes of labeled data, but it’s almost impossible to understand how it creates the models it does.
This “black box” issue can be problematic in highly regulated industries such as financial services, in which regulators insist on knowing why decisions are made in a certain way.
We encountered several organizations that wasted time and money pursuing the wrong technology for the job at hand. But if they’re armed with a good understanding of the different technologies, companies are better positioned to determine which might best address specific needs, which vendors to work with, and how quickly a system can be implemented. Acquiring this understanding requires ongoing research and education, usually within IT or an innovation group.
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