Author

Dan Vesset

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We’ve discussed the need
to redefine enterprise intelligence
and what the future of intelligence
will look like for technology and business leaders, and we know that
organizations are paying attention. In fact, enterprises spent $200 billion on
data, analytics, and AI software, hardware, and services last year. That
doesn’t even include the investment in external data and internal labor costs
to further fuel intelligence initiatives.

In October 2018, a Reuters article informed the world that Amazon had scrapped an AIā€“based recruitment application that turned out to be biased against women. Most headlines about this story highlighted the company’s failure in developing an actionable and fair solution for one of the most important processes of the HR team.

However, what this and similar examples of today’s AI “failures” neglect to acknowledge is the complexity of end-to-end process automation based on AI technology. This complexity stems not only from current technical limitations but also from the immaturity of corporate policies, government regulations, and legal systems to deal with machines that automatically analyze, decide, and act.