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Myths Hold Enterprises Back From AI Success

June 16, 2021

A new study explores key myths related to effective AI implementation that permeate today’s enterprises.

According to the study, 67% of decision-makers expect their AI/ML use cases to increase at least slightly over the next 18 to 24 months. However, silos, data challenges and a lack of resources stand in the way. Forrester found that AI decision-makers see operationalizing AI as critical to gaining essential insights about customers and markets to improve business outcomes.

To evaluate the commonly held myths that prevent enterprises from successfully operationalizing AI, Forrester conducted an online survey of 302 U.S.-based application development and delivery decision-makers, as well as three, in-depth live interviews. The research also evaluated how firms could change their perceptions of these myths in order to operationalize AI faster and more effectively.

Key findings identified top challenges including:

Data overload: More than half of decision-makers surveyed say their organizations have too much data to make collaboration efficient, hindering AI project success.

The “black box” problem is real: 64% of decision-makers indicated that it is “critical” or “important” for their organization to defend or prove the efficacy of its digital decisions. However, nearly 60% said it is challenging to do so.

Set it and forget it: Almost one in three organizations surveyed do not routinely monitor and retrain their machine learning models to ensure peak performance.

The research yielded five key recommendations that companies should consider in order to expedite AI success. According to the study, “AI is a critical source of industry competitiveness. The fastest path to AI solutions is to formulate and execute a strategy to scale AI use cases based on reality unencumbered by myths.”

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