Special Feature
Part of a ZDNet Special Feature: Managing AI and ML in the Enterprise

Research: AI/ML projects see growth in business operations

A recent TechRepublic Premium poll shows AI/ML projects have moved out of the pilot project phase and into implementation.

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Enthusiasm for artificial intelligence (AI) and machine learning (ML) remains high for 2020, as evidenced by an uptick in spending, development, and implementation of AI/ML projects across the enterprise. How companies manage those projects was the topic of a recent survey by ZDNet's sister site, TechRepublic Premium.

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Special Feature: Managing AI and ML in the Enterprise

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Many businesses have advanced from evaluating where AI/ML fits in an operation to actually deploying the technology. Likewise, strategizing for such initiatives has moved away from C-level executives and into the hands of middle managers, who are responsible for ensuring project success.

More specifically, survey results showed that AI/ML projects were co-managed by IT and end business for 23% of respondents, 19% said IT managed projects, and data science departments managed AI/ML projects for 11% of respondents. This is a shift from a similar survey in 2019, which reported that 33% of AI/ML projects were managed by IT. 

Another noted difference from 2019 was the steps taken to ensure an AL/ML project's success. In 2019, that meant performing small pilot projects and proofs of concept before proceeding with full implementation for 64% of respondents. While, 14% invested in IT/end-user training, and 9% selected vendors/consultants with AI/ML expertise.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)  

In 2020, these steps for success evolved into working with management to better identify business use cases for AI/ML (52%), preparing/training IT staff (48%), and investing in data preparation, computing, and automation processes (46%).

Concerns about AI/ML project implementation also changed from year to year. In 2019, the three biggest concerns about project implementation included: Users unclear on project expectations (53%), IT lacking the skills needed for implementation and support (47%), and upper management lacking a good understanding of AI/ML (33%). 

In 2020, the biggest concerns were not receiving business results to justify the investment (48%), staff readiness/difficulty finding AI/ML talent (38%), and implementation taking too long (37%).  

Interestingly, in 2020, 54% of respondents said that their upper management is either very or somewhat knowledgeable about AI/ML, again representing a shift from needing  buy-in for projects to actually implementing projects for results. According to survey respondents, 47% were applying AI/ML to business operations, 30% were applying it to marketing/sales, and 27% were applying the technology to engineering and IT. 

The infographic below contains selected details from the research. To read more findings, plus analysis, download the full report: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium subscription required).

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