What the “new automation” means for tech careers
“The whole history of software engineering is about raising the level of abstraction,” he says Grady BoochIBM’s principal software engineering scientist and leading technology thought leader.
If you’ve used ChatGPT, you’ve probably thought about building your query — but not at all about building the supporting infrastructure — servers, databases, networks, or even large language models.
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Welcome to the age of abstraction. And the pace of abstraction for both technology and business professionals is accelerating just as quickly — to the point where deep knowledge of the underlying plumbing of applications and devices is no longer required — or, increasingly, even data science.
Some refer to the constellation of emerging technologies — artificial intelligence, machine learning, and advanced robotics (both software and physical) — as the “new automation,” which will handle many mental or low-level tasks, but increasingly take on more. complex jobs The point is that a range of skills, which are currently in short supply, will be required to effectively introduce the “new automation”.
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Greater automation means greater self-service for everyone. A recent survey of 439 IT managers and operators, published Stonebranch is growing self-service automation for techies and non-techies alike. Almost all, 92%, empower end users from data, cloud, development and line-of-business teams to run their own workflows, tools and processes.
Self-service automation “helps people control their processes, reduces manual work and increases efficiency, for the end user and the IT team,” according to the authors of the survey report. On the technology front, the pace is even deeper: data teams have grown twice in self-service and developers have grown fourfold year over year.
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AI is already playing a role in managing technology tasks. A poll More than 60% of companies released by OpsRamp adopt AIOps, which applies AI to control and improve IT operations. The biggest challenge for enterprise IT operations in 2023 was automating as many operations as possible, cited by 66% of respondents. The top AIOps benefits seen to date include a reduction in open incident tickets (65%); reduction in average time to detection or recovery (56%), and automation of tedious tasks (52%).
Last IT staff data Janco Associates’ recent layoffs have impacted data center and operations staff as business leaders look to automate IT processes and reporting. The trend seems to be that those pursuing careers in technology need to look higher — into applications and business consulting.
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However, there is still a lot of work for people who work with plumbing and code. Unfortunately, getting to automation-driven abstraction — especially if it involves AI — requires some manual work first. Not all automation solutions can bridge the gap between cloud, container and on-premises systems. Nearly 40% of respondents to Stonebranch’s survey said their automation tools are unable to connect or only connect to some cloud/SaaS-based technologies via API. “As organizations face the challenges of hybrid IT, the importance of orchestrating automated IT processes across multiple environments is becoming apparent,” the report’s authors said.
OpsRamp exams make it difficult to find engineers with the right skill sets for AIOps. Most managers, 68%, said it takes more than six months to hire engineers with the right skill sets for AIOps. “Recruiting AIOps takes longer than implementing AIOps,” say the report’s authors. “Organizations should reinvest existing ITOps staff for AIOps whenever possible.”
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Skills that are and will continue to be in high demand to bring AI and advanced automation to IT and business processes, as noted. To Gaurav TewariFounder and Managing Partner of Omega Venture Partners, in ForbesIncluding training, implementing and integrating AI systems: It requires “people who can build applications to augment business workflows. That means teaching systems so employees can properly analyze data and learn about nuanced patterns.”
In addition, Tewan says, managing AI systems “will require cross-functional leadership, coordination, change management, and the ability to manage AI systems in a way that complements what employees already do.”