How AI Is Changing the Way Engineering Teams Work

Krishna Sai, CTO at SolarWinds, joins The Tech Trek to talk about one of the biggest shifts happening inside IT and engineering teams: AI is moving people from operators to orchestrators. The conversation goes beyond faster code and automation. Krishna explains why AI is changing how teams think about systems, governance, validation, observability, and the skills technical leaders will need as work moves from manual execution to higher level oversight. Key Takeaways • AI is raising the level of abstraction for IT and engineering teams. The work is shifting from operating systems manually to designing systems that can increasingly run, adapt, and respond on their own. • AI does not automatically reduce workload. In many teams, it changes the type of work by moving effort from execution into validation, judgment, risk management, and governance. • Code generation is only one part of the delivery system. Without testing, security review, observability, and strong engineering process, faster code can create more problems faster. • The best AI outcomes depend on strong foundations. Clean data, connected systems, clear ownership, and resilient architecture matter more as AI becomes part of core workflows. • Technical professionals will need stronger systems thinking, business context, adaptability, and domain understanding as AI changes the shape of day to day work. Timestamped Highlights 00:00 Krishna Sai joins the show and sets the stage for a conversation about AI, IT responsibility, skill gaps, and the latest SolarWinds IT Trends Report. 02:14 Why IT is moving from operator to orchestrator, and what that means for teams that used to spend most of their time responding to tickets and manually managing systems. 04:54 Krishna explains why AI feels different from prior technology shifts. This is not just infrastructure change. It touches individual workflows, jobs, and decision making. 08:56 The messy middle of AI adoption. Teams are getting faster at some tasks, but the workload has not disappeared. It has moved into validation, review, and oversight. 14:46 How AI may force teams to rethink the software delivery cycle, sprint structure, feedback loops, and the speed at which customer issues can be resolved 24:27 Krishna shares how principles from distributed systems, including loose coupling and high cohesion, can help leaders build AI systems that can change without breaking everything around them. Standout Moment “AI is a multiplier. It does not magically fix all your problems. It multiplies your current state.” Pro Tips • Do not measure AI success only by how much faster a team can generate code or complete a task. • Look at the full system around the work, including testing, review, security, observability, and ownership. • Build AI workflows with enough flexibility to swap tools, models, and processes as the technology changes. • Invest in systems thinking and domain knowledge. Those skills become more valuable as execution becomes easier to automate. Call to Action Subscribe to The Tech Trek for more conversations with technology leaders on how AI, data, engineering, and modern systems are changing the way companies build.