Ryan Roslansky’s Post

This resonates but I’ll be honest, it’s only half the story. I’ve lived in that “messy middle” you’re describing. Applying. Interviewing. Watching roles pause, disappear, or get reshaped mid-process. What’s becoming very real at the leadership level is this: It’s not just that work is changing, it’s that access to the work is tightening. White-collar leadership roles aren’t just evolving, they’re consolidating. Fewer seats. Broader scope. Higher expectations. Longer decision cycles. So while I agree the opportunity is in judgment, context, and decision-making, the gap right now isn’t awareness or willingness to adapt - It’s time, access, and runway. That said, where I fully align is this shift: AI isn’t replacing leaders. It’s exposing which leaders actually create value. Not through activity. Through clarity, decisions, and the ability to move people and outcomes forward in uncertainty. That’s where I’ve been leaning in. Building, even without the “official” role. If the front door is narrowing, you don’t wait, you find another way to show the work. The question I keep coming back to is this: If opportunity is becoming more self-created than assigned… how do we make sure the people who can do the work actually get seen?

This is such an important reframe. The shift from speed to judgment isn’t just about productivity, it’s about direction. What’s showing up across many searches is a quiet divergence. Some use AI to do more. Others use it to think better, ask better questions, and step into work that requires context and care. At first, it looks the same. Over time, it changes everything. One piece that often goes unspoken: value only compounds when it’s visible. Judgment matters, but it also has to be translated in a way others can recognize and act on. I’ve seen leaders take back hours in their week and use that space to reconnect dots others missed patterns across teams, signals beneath the data. That’s where momentum starts to build. The risk isn’t the technology. It’s drifting into efficiency without intention and slowly becoming interchangeable. The opportunity is more human than technical: deciding what your time is now for. For those navigating this shift, the real challenge isn’t effort, it’s clarity. When time opens up, where are you choosing to reinvest it so your work becomes not just faster but more meaningful and harder to replace?

Strong perspective. What stands out to me from a personal lens is this: AI is not reducing the importance of payroll. It is exposing where it was never truly under control. Many organizations will use the time gained to produce more output. But the real shift is elsewhere. The leaders who step forward are the ones who use that space to build clarity on ownership, structure in decision making and true board level control. Because in international payroll, speed is not the bottleneck. Uncertainty is. And AI does not remove that. It amplifies it.

The real shift isn't about getting faster at what you already do. It's about using the time that gets freed up to invest in the parts of work that can't be automated. Judgment, context, the specific human perspective that no tool has because no tool has lived what you've lived. The organizations that figure out how to build around that are going to come out ahead. The ones that just produce more of the same output faster are going to wonder why they're still stuck.

Sehr spannend. Neben „mehr tun“ und „anders tun“ gibt es aus meiner Sicht noch eine dritte Ebene KI als Spiegel für das eigene Denken. Wenn man seine Gedanken auslagert, sieht man plötzlich: wie viel davon strukturiert ist – und wie viel nur konstruiert wirkt. Das ist weniger Produktivität – und mehr Metakognition. Und genau da könnte der größte Unterschied entstehen.

The way you frame a “new math of work” where roles are being broken down into tasks and continuously recombined really landed for me. Shifting from hiring for titles to assembling around skills and tasks changes the responsibility on both sides: companies need better internal talent maps, and individuals need a much clearer skills narrative than just a CV. One thing I’m curious about is how you see managers practically adapting to this: most are still trained to think in headcount and job descriptions, not in fluid projects and evolving skill stacks.

The effort-to-value shift is exactly right — but assumes organizations reward judgment investment over output volume. Most performance management systems still measure productivity by quantity metrics automation amplifies, not contextual judgment automation can't replicate. The strategic constraint isn't workers recognizing judgment matters; it's whether compensation and promotion decisions actually value unmeasurable judgment over measurable output. Without explicit incentive redesign, workers rationally optimize for volume AI accelerates rather than judgment development organizations claim to want but don't systematically reward.

This really lands. It feels like the pace of change isn’t just about learning new tools, but about making sense of what’s actually changing underneath them. A lot of people aren’t struggling because they can’t keep up — they’re struggling because things aren’t always clear. When there’s clarity, people adapt. When there isn’t, it just feels like constant noise. That’s where a lot of the tension comes from.

The idea of the “new math of work” is powerful, Ryan Roslansky, because it reflects a shift many of us are personally navigating. Artificial Intelligence is making tasks faster, but the real transformation is happening in how we define value. What matters most now is judgment, context, and the lived experience that guides decisions in uncertain environments. For professionals building the next stage of their careers, this shift feels demanding and energising at the same time. It demands continuous learning, adaptability, and the willingness to reexamine our own assumptions. It is energising because it creates new ways to contribute and be recognised, even outside traditional pathways. What stayed with me most from your message is the reminder that progress is not only about adopting new tools. It is about using the time created by those tools to think more clearly, make better decisions, and invest in skills that cannot be automated. Thank you for framing this transition with such clarity. Conversations like this help many of us understand not only how work is changing, but also how we can grow with it.

AI isn’t about doing more—it’s about doing what matters. Ryan Roslansky’s "New Math" highlights the strategic pivot we are seeing across the Oil & Gas and Industrial sectors: Efficiency is now a commodity. If you only use AI for speed, you’re playing a short game. Judgment is the new moat. Long-term differentiation comes from reinvesting saved time into high-stakes decision-making and contextual awareness. At A to Z Professional Academy, we see this as a shift from "digital adoption" to capability building. In complex technical markets, the real gap isn't a lack of tools—it’s the ability to translate deep field experience into higher-value human judgment. The winners won't just produce faster; they will learn smarter and decide better. The leadership question is simple: Are you using AI to accelerate your output, or to elevate your people? Aneesh Raman. LinkedIn, A to Z Professional Academy