Myths Hold Enterprises Back From AI Success
June 16, 2021
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:
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