AI integration into DevOps is changing the entire software development at an unprecedented pace. The market is set to grow from $13.4 billion in 2024 to $36.0 billion by 2029.
More and more companies are investing in AI solutions that promise us a revolution in how software is built, tested and launched.
This change is right in front of us. A recent survey of 504 DevOps teams revealed that 33% have already integrated AI into their processes, while the other 42% are actively preparing for AI adoption. This is a fundamental change in how development teams handle their daily operations.
One of the most impactful AI applications that has already been implemented by Google, is predictive analytics, which makes systems more reliable by watching patterns and detecting issues before they have any impact operations. Tech giant reports that AI has helped them reduce unnecessary system shutdowns by 35%.
Another capability that AI brings to the table is real-time system monitoring, meaning that it can constantly watch infrastructure and catch the issues as soon as they appear. Instead of just sending alerts, AI systems actually tell what is going wrong and how to fix it – making it much easier for DevOps to solve problems.
National Australia Bank is a good example of using this in practice, as they analyse millions of security event logs with Microsoft’s Security Copilot, and their engineers can focus on more critical tasks rather than routine log analysis.
Real-time monitoring is just one example of how ML and AI are changing DevOps. The speed and efficiency that they bring are also reflected in how teams review codes. If before, quality standards relied solely on human reviewers, who might have missed issues after hours of checking code, now software development companies are using AI tools to quickly analyse entire codebases and catch potential problems more effectively.
AI is accelerating the software deployment process and it is done by optimising so-called CI/CD pipelines. So, how does AI help here? It studies past deployments and learns which tests to run first based on what’s most likely to catch issues. The result is that DevOps delivers updates and new features more frequently. This is especially valuable for companies working on multiple client projects.
So, the adoption of AI, whether in DevOps or in other industries, is not just an experiment, but a necessity just to stay competitive, and the the numbers back this up – research shows that for every $1 invested in generative AI, companies see $3.7x return on investment.
And how is Australia responding to this global AI transformation? According to a CSIRO report: 68% of Australian businesses have already implemented AI technologies and 23% are planning to do so.
The efficiency, that these technologies bring, expands beyond software development: Australian companies report 30% time savings on average, across AI-related activities.
What can this adoption mean for Australia’s tech? With such effectiveness and fast implementation pace, we will see an increased demand for professionals who can develop, manage and optimise these new AI systems. And the numbers support this prediction. The Tech Council of Australia forecasts AI can create up to 200,000 new local jobs by 2030.
According to the same report:
‘We’ve seen rapid growth in the AI workforce in recent years, with around 4,000% growth over the last decade, reaching just over 33,000 workers in 2023. Currently, the largest employers are Education and Training, Technology and Financial Services. To meet future demand, we’ll need to sustain growth at approximately 500% between now and 2030.’
As you can see, the future ahead is full of both challenges and opportunities, and with thoughtful preparation, we can ensure our tech is well-positioned for this AI evolution.
