Anyone who works in technology knows that AI is not a new concept. But whether you love it or hate it, now that the AI era is fully upon us, AI and automation will continue to play integral roles in the way that organizations and individuals operate, innovate, and grow.There are undoubtedly some risks that AI poses (there’s a reason why 71% of executives are worried about security risks associated with AI). But AI also brings with it exciting new opportunities, many of which we’ll continue to discover as the technology increasingly goes mainstream.In my role as NinjaOne’s SVP of Data and AI, I oversee the research, development, and deployment of secure AI technology practices inside the company and partner closely with our broader team on AI-related product development. I’ve seen firsthand how AI is reshaping work and the tools we use, and I’ve had to make the same kinds of decisions many IT and technology leaders are facing today.Especially for the next crop of IT leaders, AI presents clear and significant opportunities to enhance and up-level the technical workforce, particularly for those who can discern where to apply it practically and safely.Here’s how AI can be leveraged for good.The True Value of AIWhat could you, your team, and your organization do if every employee got back 15-20% of their day by automating repetitive tasks, or preventing them from hitting stack overflow? That’s where the true value of AI lies today.Engineers are often tasked with mundane tasks — tasks like modeling, data collection, and analysis — that can easily be automated with AI. This would allow those employees to focus more time and resources on efforts that move the organization forward (boosting efficiency and productivity in the process), instead of spending hours on data logging or IT troubleshooting. This extra time also frees up more engineers and IT teams to hone their skills and advance in their careers.AI can help employees research, brainstorm, and refine their work, making them not only more efficient but also more creative and impactful.With the right tools, AI can help someone earlier in their career operate at higher levels. Especially for those who studied AI in school or had an existing interest in technology, there’s a real appetite for these kinds of resources that fuel their growth. It’s a big part of why we’re seeing enterprise demand for new AI tools and resources continue to grow.Employees at all levels will come to expect the ability to use AI at work, making them more efficient and helping them advance their professional development.Where Generative AI Fails: Critical Use Cases to AvoidAt the same time, knowing and understanding where to use AI is just as important as knowing how to best use it. For the time being, there are certainly places where it doesn’t make sense to use generative AI, at least not entirely on its own.Right now, relying solely on generative AI for engineering tasks or development processes that require reasoning, code consistency, or strict adherence to specific methods is risky. For security, compliance, and reliability reasons, organizations still need and benefit from having a human in the mix.In areas like scripting, while large language models (LLMs) are getting better, there’s a lot of risks posed by an erroneous script directly affecting end users or compromising code bases. Also, as AI regulation firms up, oversights like these can have critical ramifications on organizations. Knowing and recognizing limitations, especially as AI use cases and best practices evolve, is an essential part of optimizing the technology for good.Security-First AI: Avoiding Common Development RisksLastly, the mass adoption of generative AI has caused many to think more critically about where and how we adopt, operate, and optimize parts of our organizations — and specifically how security factors into ensuring that AI delivers on its long-term promise. Having a security-first mindset in the AI era is no longer nice to have. It’s essential.While in the past, security was something solely regulated to security teams, now organizations, engineers, and IT teams of all levels must think critically about security when it comes to experimenting with and employing AI.For example, while AI-driven automation can accelerate scripting and code output, allowing AI to operate without guardrails poses serious security risks. A more strategic approach is to use AI to enhance human decision-making, integrating it into developer environments or processes like patch management. In these cases, AI can surface relevant insights by analyzing known vulnerabilities and public sentiment, helping engineers prioritize actions while ensuring final decisions remain in human hands.In the end, new technologies like AI pose an exciting opportunity and challenge for all of us to learn and grow. As organizations and individuals continue to leverage AI, exploring its applications within their organizations to advance their missions and careers, it’s crucial to use generative AI safely and effectively.Those who understand where to optimize AI, while also partnering with the technology to drive innovative and secure outcomes for their organization, will be the ones who see the most significant advancement opportunities in the years ahead.The post How AI Automation Reclaims Developer’s Time appeared first on The New Stack.