DEVOPSdigest

Global adoption of artificial intelligence (AI) has skyrocketed, with 72% of companies incorporating the technology. IT teams have been leading this transformation, using AI to not only solve problems, but to accelerate innovation. And with the global AI market expected to reach $184 billion by the year's end, businesses that embrace this shift will be the ones defining the future.

As part of this shift, we've seen AI technologies become a powerful asset for DevOps teams. Organizations are under pressure to ramp up their software delivery cycles toward faster, more efficient, releases. And those using this technology are reaping its impact. In a recent AI-augmented DevOps trends report we conducted, we found that DevOps teams leveraging AI are 30% more likely to rate their performance as either extremely or very effective.

It is evident that AI is already addressing key challenges experienced by DevOps teams. From improving productivity and closing skills gaps to cutting costs and refining software quality. Furthermore, AI copilots are gaining traction, offering valuable support in planning, code development, and software testing. The potential of AI is no secret, but implementing this technology is easier said than done. To ensure successful AI adoption, it is vital to equip development and testing teams with the necessary skills to collaborate with AI systems effectively.

Empowering DevOps with AI

Testing stands out as the area yielding the highest ROI from AI within the software development lifecycle (SDLC), with almost two-thirds (60%) of DevOps practitioners highlighting it to us as the most valuable investment. AI is employed to enhance a variety of testing activities, from creating testing plans and generating test cases to analyzing outcomes and assessing the risks of code changes. This approach allows quality assurance (QA) teams to prioritize the most error-prone areas, focusing their efforts where it matters most.

AI has notably impacted both coding and security, with DevOps practitioners ranking these areas just behind testing. In security, AI tools are proving highly effective at proactively identifying and addressing vulnerabilities, boosting threat detection capabilities, and automating responses to emerging risks. Nonetheless, significant potential for AI remains in phases such as release management, deployment, platform engineering, and planning. These stages, which are crucial for ensuring software stability and scalability, could greatly benefit from AI's predictive abilities, resource optimization, and the streamlining of operational and maintenance processes.

Propelling AI Integration: Expertise and Confidence as Key Drivers

While generative AI and AI copilots have been instrumental in driving adoption of this technology, there remains a major shortage of AI expertise within DevOps. This gap is significant, especially given that humans remain deeply involved in the process, with over two-thirds of our respondents indicating they manually review AI-generated outputs at least half the time.

To address these challenges, organizations should devise specialized training courses to properly equip their DevOps teams with the skills to leverage AI tools. Whether through industry-recognized courses or internal programs, encouraging certification can enhance technical expertise significantly.

Upskilling is also essential, as AI requires human oversight for strategic decision-making, particularly in areas where contextual understanding is key. Additionally, establishing clear governance frameworks is critical to building trust and confidence in AI's contribution to software development. Balancing these factors will ensure AI becomes a valuable asset, driving efficiency while maintaining accountability.

American government officials have begun developing AI guidance, such as the White House's AI Bill of Rights blueprint, which envisions a national framework focused on safeguarding privacy, transparency, and fairness in AI applications. Overlapping state and federal laws create a complex regulatory landscape. Organizations that keep pace with both guidelines can ensure their AI systems are ethical, compliant, and trusted by their employees and the public.

Fueling Sustainable Team Growth for Lasting Impact

Organizations have tremendous potential to enhance DevOps practices through AI integration. However, as I mentioned earlier, successful AI adoption is only possible when development and testing teams are equipped with the skills needed in order to collaborate effectively with AI systems. Establishing trust in AI outputs will also require transparent governance and regulations, helping build confidence in the technology's capabilities.

Teams investing in upskilling and fostering trust in AI will be well-prepared to balance AI generation with human oversight. This approach ensures they can leverage AI as a powerful tool to boost efficiency, speed up time to market, and uphold high software quality standards.