AI Knowledge Retention in the Enterprise – Making the Most of Lessons Learned

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.

AI Knowledge Retention in the Enterprise - Making the Most of Lessons Learned 950x540

Novice AI project leaders measure projects entirely by (unrealistic) near-term financial benchmarks.

Relatively experienced AI project leaders understand that AI initiatives must be measured by well-considered measurable benchmarks along with relevant progress on larger strategic goals for the organization.

Many expert enterprise AI project leaders would argue that deploying initial AI projects is more about the ROI of learning than it is about the ROI of any first AI application itself.

We might think about a variation of the "teach a man to fish" proverb:
Implement a successful AI initiaive with an enterprise team, and they'll derive near-term value.

Level up the AI fluency and understand of an enterprise team, and they'll have the ability to discover and deliver AI projects into the future.
Firms that retain AI knowledge can spin the flywheel of innovation faster than their competition. Digitally native tech firms like Google or Amazon aren't just more nimble at deploy...

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