A CEO friend asked me, “Will AI ever be able to offer an ROI for enterprises?” I thought the answer through and wrote it down. Then, I realized that this information could be beneficial and valuable to many people, so here’s my answer, based on my experience in the artificial intelligence (AI) and cybersecurity training industry.
Let’s begin by defining where we stand and ask the same question about IT projects in general: “Will IT ever be able to offer an ROI for enterprises?” Here, the statistics are known to be about a 30% success rate. So, if AI, with a current success rate of about 15%, can reach the IT project success rate, we can consider the mission accomplished.
What are the best practices for achieving success with AI? First, the quality of the AI answers should be “good enough.” The measure of “good enough” is called the “error rate.” For example, IBM’s translation in the 2010s was based on rules extracted from translations of Canadian parliament speeches. This rule-based approach was fragile and one had to add a manual check. So, an AI project based on the IBM technology of the 2010 timeframe would not deliver an ROI. Today, the accuracy of Google Translate reaches the 94% range. This quality is good enough, and people find it satisfactory.
In this instance, by “people,” I mean lawyers who are using it to translate the documents in their cases. Why am I choosing lawyers for my use case? Lawyers have a low tolerance for mistakes. So, when an AI algorithm offers its services, they demand precision from it.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2021/09/17/will-ai-ever-be-able-to-offer-an-roi-for-enterprises/?sh=2bf72bde2025
Let’s begin by defining where we stand and ask the same question about IT projects in general: “Will IT ever be able to offer an ROI for enterprises?” Here, the statistics are known to be about a 30% success rate. So, if AI, with a current success rate of about 15%, can reach the IT project success rate, we can consider the mission accomplished.
What are the best practices for achieving success with AI? First, the quality of the AI answers should be “good enough.” The measure of “good enough” is called the “error rate.” For example, IBM’s translation in the 2010s was based on rules extracted from translations of Canadian parliament speeches. This rule-based approach was fragile and one had to add a manual check. So, an AI project based on the IBM technology of the 2010 timeframe would not deliver an ROI. Today, the accuracy of Google Translate reaches the 94% range. This quality is good enough, and people find it satisfactory.
In this instance, by “people,” I mean lawyers who are using it to translate the documents in their cases. Why am I choosing lawyers for my use case? Lawyers have a low tolerance for mistakes. So, when an AI algorithm offers its services, they demand precision from it.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2021/09/17/will-ai-ever-be-able-to-offer-an-roi-for-enterprises/?sh=2bf72bde2025
