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.
Written by Melanie Wachsman, Editor

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.

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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