Why AI investments fail to deliver

According to two recent Gartner reports, 85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from prototypes to production. Yet the same reports indicate little sign of a slowdown in AI investments. Many organizations plan to increase these investments.

Many of these failures are avoidable with a little common-sense business thinking. The drivers to invest are powerful: FOMO (fear of missing out), a frothy VC investment bubble in AI companies with big marketing budgets, and, to some extent, a recognition of the genuine need to harness AI-driven decision-making and move toward a data-driven enterprise.

Instead of thinking of an AI or machine learning project as a one-shot wonder, like upgrading a database or adopting a new CRM system, it’s best to think of AI as an old-fashioned capital investment, similar to how a manufacturer would justify the acquisition of an expensive machine.

The manufacturer wouldn’t be focused on the machine as a shiny new toy, in the same way that many organizations look at AI and machine learning. The purchasing decision would consider floor space, spare parts, maintenance, staff training, product design, and marketing and distribution channels for the new or improved product. Equal thought should go into bringing a new AI or machine learning system into the organization.

Here are six common mistakes organizations make when investing in AI and machine learning.

Putting the cart before the horse

Embarking on an analytics program without knowing what question you are trying to answer is a recipe for disappointment. It is easy to take your eye off the ball when there are so many distractions. Self-driving cars, facial recognition, autonomous drones, and the like are  modern-day wonders, and it’s natural to want those kinds of toys to play with. Don’t lose sight of the core business value that AI and machine learning bring to the table: making better decisions.

Copyright © 2021 IDG Communications, Inc.

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