Artificial intelligence is changing how companies manage IT operations, helping engineers focus on more meaningful work. In this Q&A, Eric Lefebvre, Chief Engineering Officer at JAGGAER, explains how CIOs can use AI-driven observability to improve productivity and retention.
Supply Chain 24/7: You’ve said AI can cut down on engineering toil. What do you mean by that?
Eric Lefebvre: A lot of the work carried out by engineers today is out of necessity rather than the drive to create something new and innovative. That’s not their fault; it’s just a fact that an engineer’s job is mostly made up of security vulnerability protection, remediation management, and defect fixes. These are no fun for anyone, and fortunately, they are the areas where AI is most able to lend a hand in the short term.
Today, the biggest benefit of AI tools on the market is directly linked to productivity. AI can take on repetitive, low-value tasks that hold engineers back – remediation, ticket management, or monitoring, to name a few – effectively removing the grunt work or toil from software engineering.
SC247: What are some examples of the boring or routine work that AI can handle for engineers?
EL: Typical routine work that AI can handle for engineers is: aggregating metrics into dashboards, sending scheduled status reports, highlighting trends or anomalies without needing manual digging, patching, updating, and validating system software, monitoring logs and alerts, then flagging anomalies. Within the realm of reporting, AI can assist in creating or updating runbooks, wikis, API docs, and onboarding guides; summarizing incident reports or meeting notes; and translating tickets or chat logs into structured documentation. AI can also help spot syntax errors, outdated dependencies, or insecure patterns. It can be used to auto-triage tickets and assign priorities, flagging items that require attention by human staff.
SC247: Why is this kind of work such a problem when it comes to keeping engineers happy?
Eric Lefebvre is CEO at JAGGAER.
EL: I like to say that engineers are like water; I mean that they tend to take the most fluid path and get creative. As of now, too much of our engineers’ time is spent on keeping the lights on and ensuring that there are no security breaches. It’s not a surprise that this type of patching up and policing isn’t attractive to top talent. As the competition for star performers heats up, offering dull, repetitive tasks won’t make the cut when the bulk of work feels like toil, motivation and satisfaction inevitably suffer. Now that the market is abuzz with so much AI-driven, big-ticket headhunting, it’s critical to remain competitive in the talent wars.
SC247: You’ve compared engineers to water. Can you explain what you mean by that?
EL: When faced with barriers such as repetitive work, they’ll flow around them. Good engineers will learn the process, the barriers, and find ways around them. The trick is to ensure they don’t leave for a company that lets them focus on more stimulating work or to bypass a process that could open the firm to risk! Remove the barriers in their way, and they’ll channel their creativity into things that add value.
SC247: How does taking away routine tasks make a difference in keeping and hiring great engineers?
EL: Implementing AI to free engineers from routine burdens not only means accelerating delivery and improving reliability, but also cultivating a culture where they feel their expertise is valued. This approach can become a critical differentiator in recruitment and retention, as top engineers want to be free to innovate without having to “babysit” systems. At the same time, there is a real rush for top talent at the moment, with big players pulling out all the stops and offering highly competitive packages. Unless you have a stash of bottomless cash, focusing on the quality of work can make a big difference.
SC247: What could happen to companies that don’t move quickly to use AI in their IT teams?
“I like to say that engineers are like water; I mean that they tend to take the most fluid path and get creative. As of now, too much of our engineers’ time is spent on keeping the lights on and ensuring that there are no security breaches. It’s not a surprise that this type of patching up and policing isn’t attractive to top talent.”
EL: They risk falling behind in two ways: productivity and talent. With top players splashing out millions to hire top AI talent, companies that can’t afford to compete on salaries must differentiate with meaningful work. If they don’t adopt AI to reduce toil, they’ll lose engineers to those who do, and their IT will remain stuck in reactive mode while competitors move to proactive, autonomous, and adaptive ecosystems.
SC247: What’s your main advice for CIOs who want to be ready for AI?
EL: Invest in data preparedness. Preparing business data for AI starts with breaking down silos and ensuring systems like ERP, CRM, HR, and IT platforms can communicate through standardized formats, APIs, or data integration layers. Without clean, connected data, AI cannot live up to its promise, meaning CIOs need to invest now in data governance and interoperability so AI can actually deliver. Next is to get a code gen tool that works in your environment, get it in the hands of your engineers and drive adoption with measurable outcomes. Conventional wisdom assumes younger/newer engineers will take to Ai code gen tools, yet we’re finding that our most senior engineers, those with experience and domain knowledge, are the one that are most effective in using these tools.
SC247: Besides reducing toil, where else do you see AI helping engineers?
EL: In a relatively near future, AI could power self-healing infrastructure where servers, networks, and databases automatically detect and repair faults and zero-click support that resolves issues before users even notice them. In the future, AI could also be used to simulate complex systems with digital twins, design and evolve applications autonomously, and even act as a compliance co-pilot that tracks global regulations in real time. We are still early in the lifecycle of this capability, and new use cases will continue to present themselves. Those who are ready to exploit those opportunities will see higher job satisfaction and stability for the long term.
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