All IT work to involve AI by 2030, says Gartner

"All work in IT departments will be done with the help of AI by 2030, according to analyst firm Gartner, which thinks massive job losses won't result. Speaking during the keynote address of the firm's Symposium event in Australia today, VP analyst Alicia Mullery said 81 percent of work is currently done by humans acting alone without AI assistance. Five years from now Gartner believes 75 percent of IT work will be human activity augmented by AI, with the remainder performed by bots alone."

"Distinguished VP analyst Daryl Plummer said this shift will mean IT departments gain labor capacity and will need to show they deserve to keep it. "You never want to look like you have too many people," he advised, before suggesting technology leaders consult with peers elsewhere in a business to identify value-adding opportunities IT departments can execute. Plummer said Gartner doesn't foresee an "AI jobs bloodbath" in IT or other industries for at least five years, adding that just one percent of job losses today are attributable to AI."

"The two analysts also forecast that businesses will struggle to implement AI effectively, because the costs of running AI workloads balloon. ERP, Plummer said, has straightforward up-front costs: You pay to license and implement it, then to train people so they can use it. AI needs that same initial investment but few organizations can keep up with AI vendors' pace of innovation. Adopting AI therefore creates a requirement for near-constant exploration of use cases and subsequent retraining."

By 2030 all IT department work will be done with AI assistance. Currently 81 percent of work is performed by humans alone; within five years 75 percent of IT work will be human activity augmented by AI and the remainder by bots. The shift will increase labor capacity, reduce entry-level roles as senior staff absorb junior tasks, and require IT teams to demonstrate value. AI adoption brings rising operational costs, continuous vendor-led innovation, ongoing retraining, acquisition of new datasets, management of multiple models, and the need to use one model to verify others' outputs.

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