JAMES RYSEFF, BRANDON DE BRUHL, SYDNE J. NEWBERRY
The Root Causes of Failure
for Artificial Intelligence
Projects and How They
Can Succeed
Avoiding the Anti-Patterns of AI
A
rtificial intelligence (AI) is widely recognized as technology with the potential to have a
transformative effect on organizations.
1
Although AI was once reserved for advanced tech-
nology companies with the ability to hire top talent and spend millions of dollars, all types
of organizations are adopting AI today. Private-sector investment in AI increased 18-fold
from 2013 to 2022,
2
and one survey found that 58 percent of midsize corporations
3
had deployed
at least one AI model to production.
4
Similarly, the U.S. Department of Defense (DoD) is spending
$1.8billion each year on military applications for AI, and DoD leaders have identified AI as one of
the most crucial technologies to the future of warfare.
5
AI is already making impacts across a wide variety of industries. Pharmaceutical companies are
using it to accelerate the pace and success rate of drug development.
6
Retailers, such as Walmart, are
deploying AI for predictive analytics so that they know when to restock inventory and how to optimize
their end-to-end supply chains.
7
Finally, in the defense realm, AI is piloting fighter jets,
8
detecting
enemy submarines,
9
and improving commanders’ awareness of the battlefield.
10
These examples dem-
onstrate the relevance of AI to organizations in a variety of industries and for a variety of use cases.
However, despite the promise and hype around AI, many organizations are struggling to
deliver working AI applications. One survey found that only 14 percent of organizations responded
that they were fully ready to adopt AI, even though 84 percent of business leaders reported that
they believe that AI will have a significant impact on their business.
11
Managers and directors find
themselves under enormous pressure to do something—anything—with AI to demonstrate to their
superiors that they are keeping up with the rapid advance of technology.
12
But too many managers
have little understanding of how to translate this desire into action. By some estimates, more than
80 percent of AI projects fail.
13
This is twice the already-high rate of failure in corporate information
technology (IT) projects that do not involve AI.
14
Research Report
