8/ 1 more reason: businesses today largely have structured or text data, not images, voice. NLU is the least far along of those 3 AI domains
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9/ Of course, companies can (and should) certainly set out to capture more voice/images and progress NLP research
10/ This of course doesn't apply to some industries that DO rely heavily on images (e.g. radiology). But today that's a smallish subset
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11/ RPA is NOT AI. It's fragile, hard to deploy, rules-based process automation tech based on GUI-level integration. More on this later...
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12/ Most execs have very little understanding of how to "apply AI" today
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12/ This is unsurprising: they are caught between the tech industry's "magic cognitive AI does everything" hype marketing...
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13/... and of course not having the technical understanding to identify labeled datasets/decisioning opportunities for supervised learning
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I think bigger than these is: As an {insert title here} in a enterprise, I have to be able to explain to an auditor why decision X was made
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Most of the AI solutions out there cannot provide that traceability (and *should* have models complex enough where that's not feasible)
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Not a factor in AI-boosted consumer experiences because, as a consumer, I don't give a crap
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I'm a true believer in the power of ML. But it's still seen as a bunch of hand-wavy-mumbo-jumbo in large enterprises
I think we're still on the climb toward #hypecycle peak expectations. Trough of disillusionment lies ahead of us, not behind