“The whole history of software engineering is a story of increasing levels of abstraction,” said Grady Booch, chief software scientist at IBM. If you’ve used ChatGPT, you’ve probably thought about building your query, but not at all about building its infrastructure – servers, databases, networks, or even its large language models (LLMs).
Welcome to the era of abstraction. And the pace of abstraction for CIOs and business departments is accelerating rapidly. So much so that in-depth knowledge of plumbing under apps and devices, or even, increasingly, data science, is no longer required.
Some refer to the emerging constellation of technologies – artificial intelligence, machine learning and robotics (software and physics) – as “new automation” that will support many routine or low-level tasks, but also increasingly complex ones. The problem is that it will take a whole range of skills, currently in short supply, to effectively introduce the “new automation”.
More self-service for everyone
Greater automation also means more self-service for everyone. A recent survey of 439 CIO managers and operators, published by Stonebranch, reveals that self-service automation is on the rise for technologists and non-technologists alike. Almost all (92%) now give end users access to data, cloud computing, development tools via this means. Enough to give business departments the means to execute their own workflows, with their own tools and processes.
Self-service automation “helps people take control of their own processes, reduces manual work, and increases efficiency – for end users and IT staff alike,” say the authors. For those working in technology, the pace is even faster: data teams have seen their self-service usage double and developers quadruple year-over-year.
And AI is already playing a role in managing technology tasks. A survey published by OpsRamp reveals that more than 60% of enterprises are adopting AIOps, which uses AI to monitor and improve IT operations themselves. The biggest IT operations challenge for businesses in 2023 is automating as many operations as possible, cited by 66% of respondents. The main benefits of AIOps seen so far are reduction in open trouble tickets (65%); reducing the average time to find or restore (56%), and automating tedious tasks (52%).
There is still a lot of work for people who deal with plumbing and code
The latest CIO data from Janco Associates also reveals that recent layoffs have mostly affected data center and IT operations staff. For what ? Because business leaders are looking to automate IT processes and reporting. So the apparent trend shows those pursuing careers in tech that they need to look higher up the stack – towards apps and business consulting.
However, there is still a lot of work for people who deal with plumbing and code. Unfortunately, moving to automated abstraction – especially if it involves AI – requires some upfront manual work. Not all automation solutions can bridge the gap between cloud systems, containers, and on-premises systems.
Nearly 40% of respondents in the Stonebranch survey said their automation tools could not connect to certain cloud-based/SaaS technologies or could only connect to them through APIs. “As enterprises grapple with the challenges of hybrid IT, the importance of orchestrating automated IT processes across diverse environments is evident,” say the report’s authors.
It’s hard to find engineers with the skills required for AIOps
The OpsRamp study reveals that it is difficult to find engineers with the skills required for AIOps. A majority of managers, 68%, say it takes more than six months to recruit engineers with the skills required for AIOps. “Recruiting for AIOps takes longer than implementing AIOps,” say the report’s authors. “Organizations should invest in retraining existing ITOps employees for AIOps, where possible. »
Skills that are and will continue to be in high demand for bringing AI and automation into IT processes and business leadership, as Gaurav Tewari, Founder and Managing Partner of Omega Venture Partners, points out in Forbes include training, implementation and integration of AI systems. It takes people “able to build applications to improve business workflows”. It will be necessary to “train the systems so that workers can correctly analyze the data and recognize nuanced patterns”.
Additionally, according to Gaurav Tewari, 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 are already doing.” .
Source: ZDNet.com