Artificial intelligence (AI) adoption has tripled in the past 12 months, with one in seven companies implementing the technology. According to a report, AI will “cross the chasm” from “early adopters” to the “early majority” this year.
Nevertheless, tech leaders are cautiously approaching AI and machine learning (ML) projects. This is according to a recent survey by ZDNet’s premium sister site, Tech Pro Research, which also found that enthusiasm for the technologies remains “steady.”
AI and ML challenges
“CXOs are grappling with the challenges of managing AI and ML deployments and determining what benefits they can deliver to the business”, according to a special report from ZDNet and TechRepublic. In fact, Tech Pro Research respondents admitted that they were apprehensive about AI/ML project management and support.
Respondents also cited a lack of staff readiness for implementing an AI/ML system. 38% said that their company employs an “insufficient” number of technical personnel who can develop applications for an AI/ML environment.
Meanwhile, 22% said that business analysts could “use more experience” when defining system requirements and working with end users. 14% also stated that system programmers and architects lacked experience in integrating AI/ML applications with existing infrastructure.
In addition to this, 13% of respondents indicated that training end users and modifying business processes caused concern. As a result, just 8% said that their IT staff were “up to the task of managing AI/ML projects.”
IT leadership is driving AI/ML projects
Despite tech leaders expressing some concerns, IT leadership continues to drive AI and ML projects. Overall, respondents cited project requests originating from the offices of the CEO or other C-suite executives (33%), IT management (25%), and end business management (24%).
Although CEOs and managers will promote AI/ML, IT will reportedly lead the deployment and support of these projects. In order to tackle the aforementioned challenges, over half of the respondents are “performing small pilot projects and proofs of concept before full implementation.”
As a result, organisations can trial the effectiveness of a solution before investing in the project fully. According to the ZDNet/TechRepublic report, “the more comfortable organisations get with AI/ML initiatives the more likely they will pursue additional projects.”
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