Predicting the ROI of AI – Pitfalls to AI Adoption in the Enterprise (Part 3 of 3)

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading 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 3 of 3 950×540

In the final installment of the "Pitfalls to AI Adoption" series, we talk about predicting the ROI of AI. There are a lot of misconceptions running rampant around the ability to gauge the return on investment of artificial intelligence. In this article, we talk about what can and can't be done when it comes to investing in artificial intelligence and predicting what the return might be.

How much will an AI project cost?
How long will the integration take before the software is ready to use?
How will it be able to be used to drive value?
Will it be able to do what you want?
D0 you know for sure that the software is going to achieve the outcome that you set out to achieve?

None of these questions are easy to answer. We'll lay out exactly why this is and go into some of the critical reasons why it's very hard to gauge the cost of an AI project, the time to completion for an AI project, and whether or not the project is going to work out.
Pitfall 3: Projecting the ROI ...

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