Avoid AI Novelty – Pitfalls to AI Adoption in the Enterprise (Part 1 of 3)

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

Pitfalls to AI Adoption in the Enterprise 1 of 3 950x540

What's harder than training an algorithm to detect images or automate a process? Collecting and cleaning the data in the first place.

And what's even harder than that? Integrating AI systems into old technology, old processes, and old skillsets that exist in most enterprises and midsize businesses today.

This sort of cultural shift is arguably vastly more challenging than the technical problems of AI. Computer vision and different kinds of NLP are rather proven use cases. They provide great value when you have the right data and the right setup, but baking that into an existing business is where the challenges arise.

This is what the vendor landscape of AI companies are trying to solve. They're all trying to become more accessible.

Because of this accessibility issue, enterprises often jump the gun and make poor decisions, and there are factors that they simply don't consider or factors that they often over consider. We talked to vendors, we talked to consultants and we tal...

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