Throughout my career spanning mainframe, client-server, ERP, and digital, technology has evolved. But with AI, our approach needs to shift. As Marshall Goldsmith puts it, “What got you here will not get you there.” So what does this shift look like in real terms? Many of us are wired to jump into action the moment we see a problem. That instinct has been built over years, even decades, of hands-on work. Now, engineers must resist the reflex to “just do”. Instead, they need to pause, learn to “think first”, and ask the right questions. Can this be streamlined or automated? Does AI already have the right course plotted? Will it standardize resolution or reduce risks? Slowly, re-wiring these instincts will build an engineering culture fit for AI-first work—one that fosters learning, unlearning, and relearning as the core competencies, not optional extras.

Pilots are trained to fly aircraft manually, yet on most commercial flights, autopilot manages altitude, course corrections, and fuel efficiency. The pilot’s role is no longer to constantly adjust controls, but to oversee the system, validate its actions, and intervene when conditions demand it. For engineers, adopting AI requires a similar reset.

For new engineers entering the workforce today, this adjustment feels natural. They are beginning their careers in an AI-enabled environment. For experienced professionals, it can feel like retraining muscle memory. True adoption does not happen overnight. Shifting from constant action to thoughtful oversight takes intention, practice, and time. It requires leaders who coach teams to pause, validate what AI can do, and then step in with judgment where human expertise matters most.

This change is not about speed alone. It is about consistency, accuracy, and smarter ways of working. Habits built over decades can be reshaped, but only with patience, leadership, and a willingness to rethink. Just as pilots and autopilot systems work together to ensure safe and efficient journeys, engineers and AI can work in tandem to deliver reliable, high-impact outcomes for businesses.

I always ask: What outcomes are we delivering for our clients? What impact are we having on end users? Those questions matter more than tasks, processes, or technologies.

We work in high-stakes environments. These are Fortune 500 clients with distributed infrastructure, complex systems, and business operations that depend on stability and speed. When something breaks, it affects real people. If a retail client can’t get the right inventory to a store, that’s a business impact. If a support engineer alters a pricing parameter in production, the consequences can be significant.

That’s why consistency matters. The job needs to always be done correctly. That’s where AI can help. Engineers can get tired. When they’re working across shifts or handling outages, mistakes can happen. But AI can follow instructions the same way, every time. That level of consistency changes the kind of experience we deliver.

Businesses are asking bigger questions now: “Have you done this before? Can you solve this at scale?” They want to know if the solution works in real environments. We need to engineer systems with the ability to anticipate failure, so we can act before something breaks. That kind of thinking needs to be built into our engineering teams from the start.

So when I lead, I bring that lens. Whether we’re building something new, supporting a platform, or troubleshooting a live issue, the focus is always on the business outcome and what success looks like for our client. That’s how we move beyond tasks and tickets and towards value.

I always look for three qualities in a strong team: accountability, openness to new working methods, and creative, out-of-the-box thinking. Those traits consistently elevate performance.

Transformation in tech is not new. We have seen shifts from mainframes to digital, and embracing change across eras is what keeps teams moving forward. Every team member needs to carry that mindset. Our collective effort must be directed toward a shared goal for the client.

Ascendion’s flat structure enables that collective focus. Anyone can reach leadership directly, without navigating layers of hierarchy. That openness drives speed, alignment, and trust across teams and geographies. When you cultivate accountability, encourage creative thinking, and eliminate unnecessary hierarchy, you build teams that learn, adapt, and deliver real client outcomes faster.

That culture of openness and adaptability also shapes how we think about leadership; it’s not just measured by experience. I’ve seen engineers with two years of experience lead in their space, bringing clarity, taking initiative, and helping others navigate complexity. Experience doesn’t matter as much as the lens someone brings. I get inspired by everyone around me. A new engineer might notice something I haven’t seen. That’s the kind of input that helps us all move forward.

We support the largest Fortune 500 companies. Our teams manage production systems, infrastructure, and critical applications. That level of trust carries weight. But it also brings purpose. Engineers understand that their work shapes outcomes in the real world, from store shelves to customer transactions.

That is the leadership we cultivate. Whether building new capabilities, supporting critical infrastructure, or ensuring operational reliability, clarity of purpose, and the courage to act matter the most. Ascendion values these qualities over tenure. That mindset enables teams to make meaningful impact, deliver trusted outcomes, and continually elevate our work together.

The promise of AI in IT operations is to know where it creates the most value, building trust in its use, and guiding teams to think differently, rather than chase every new tool.

For engineers, that means learning to pause, ask the right questions, and build habits that prioritize consistency and outcomes. For leaders, it means creating environments where accountability, openness, and creativity thrive. Most of all, it means never losing sight of what matters most: the client and their end users.

The organizations that succeed will be those that apply AI with purpose, embed it into culture, and align it to real-world outcomes.

We see AI not as a replacement for people, but as an amplifier of their potential. When people and AI work together with clarity of purpose, the result is resilient systems, consistent execution, and lasting client value.