How AI Engineers Are Changing Workplace Dynamics

It’s easy to think of AI as a time-saver that streamlines workflows and accelerates output. But the deeper opportunity lies in how it’s reshaping the nature of work itself. A new study from Harvard Business School’s Manuel Hoffmann followed more than 50,000 developers over two years, with half using GitHub Copilot. The results were striking: developers shifted away from project management and toward the core work of coding. Not because someone told them to, but because AI made it possible. With less need for coordination, people worked more autonomously. And with time saved, they reinvested in exploration—learning, experimenting, trying new things. What we’re seeing here isn’t just productivity. It’s a shift in how work gets done and who does what. Managers may spend less time supervising and more time contributing directly. Teams become flatter. Hierarchies adapt. This is just one signal of how generative AI is changing our org charts and challenging us to rethink how we structure, support, and lead our teams. The future of work isn’t just faster. It’s more fluid. And if we get this right, it’s a whole lot more human. https://lnkd.in/gaUgXnRY

Over the past year, I have had one consistent realization while speaking with data leaders, founders, and AI teams across conferences and interviews. AI is not just changing how we work. It is quietly creating entirely new job roles inside companies. Curious to know what the community thinks about it? When I started covering AI agents on The Ravit Show (www.theravitshow.com), most conversations were about automation. Faster reports. Smarter copilots. Less manual work. But now, what I see inside real teams is very different. Companies are not asking, “Which tasks can AI replace?” They are asking, “Who will design, supervise, and run these agents?” That shift is creating new roles that did not exist a few years ago. For example, I am now seeing teams actively look for people who can design how agents think and collaborate, not just write prompts. Roles like AI Agent Architects and Prompt-to-System Engineers are emerging because businesses need structured intelligence, not experiments. Future Job Roles Created by Age…. I am also seeing operations leaders move into workflow design roles. Instead of optimizing processes manually, they are turning onboarding, reporting, and customer support into agent-driven pipelines. This is where Agent Workflow Designers are becoming critical. Another big change is happening in production environments. Once agents go live, companies need people to monitor drift, control costs, handle failures, and improve performance continuously. That is where Agent Ops and Human-in-the-Loop Supervisors come in. These roles sit at the intersection of technology, risk, and business judgment. Even analytics teams are evolving. Analysts are no longer just querying data. Many are building agents that pull data, run analysis, generate insights, and draft reports. Their role is shifting from data pullers to decision accelerators. And perhaps the most interesting shift I am seeing is in consulting and product roles. AI Automation Consultants are helping companies find where agents actually deliver ROI. Agent Product Managers are thinking in terms of which agents do what, when, and why. Systems Integrators are becoming the bridge that connects agents to CRMs, databases, and enterprise tools. This is not a future prediction. It is already happening inside modern teams. If you work in data, product, operations, or engineering, the opportunity is not just to use AI. It is to become the person who designs, manages, and scales intelligent systems. I would love to hear from you. Which of these emerging roles do you think will become standard in every company over the next 3 years? #data #ai #agentic #promptengineering #designs #systems #jobs #agents #theravitshow

Anthropic just published one of the more honest looks I’ve seen at how AI is actually changing work — by turning the lens on their own engineers. A few points that stood out: • AI is now a constant collaborator. Nearly all engineers use Claude frequently, but can “fully delegate” only 0–20% of their work. Most value comes from co-piloting—more output, faster iteration, broader scope—rather than full automation. • Work is getting wider, not just faster. Engineers report becoming more “full-stack” and taking on tasks they would have avoided before (front-end, infra, new codebases). About 27% of Claude-assisted work wouldn’t have happened at all without AI—things like refactors, tools, and “papercut fixes” that improve quality of life. There’s real anxiety underneath though. People worry about deep skill atrophy, weaker mentorship and collaboration, and long-term job security—some openly say it can feel like they’re “working themselves out of a job.” This is a preview of what many organizations will face over the next 2–3 years. AI is already boosting productivity and ambition, but it’s also forcing leaders to rethink how we: • Design learning and career paths in an AI-augmented world • Protect deep craft and judgment, not just speed • Keep human collaboration and mentorship at the center Curious for your reactions: 👉 Are your teams mostly using AI to speed up existing work—or is it already changing what work gets done, who does it, and how they learn? #AI #GenerativeAI #FutureOfWork #AIAtWork #EngineeringLeadership https://lnkd.in/erkHxKuw

AI isn’t just changing tools. It’s rewriting how companies are built. That’s the shift leaders can’t ignore. Here’s what the research shows. 1. Tasks are being redefined. AI is taking on execution, from code to testing to analysis. Humans are shifting toward design, strategy, and oversight. Execution is no longer the center of human work. 2. Talent is evolving. Hybrid skills now matter more than functional silos. AI fluency, systems thinking, and judgment are rising in value. Some companies no longer test coding depth, they test AI fluency. 3. Teams are flattening. Coordination-heavy roles are disappearing as AI takes on execution. Cross functional pods are replacing layered pyramids. Managers are covering four to six times more scope. 4. Entry-level pipelines are under pressure. AI automates routine work that once trained early career hires. New hires are expected to deliver at a higher level day one. The readiness gap between schools and jobs is widening fast. 5. Organizations are diverging. Some are scaling AI into existing workflows. Some are streamlining and collapsing layers. Some are reinventing entire job families around AI-human teaming. The question for leaders is no longer when AI will matter. It’s whether your workforce strategy is evolving as fast as AI itself. Is your organization redesigning for AI maturity or just adopting tools?

I've come to reconsider my perspective on the effects of Agentic development. Initially, I thought it was a conversation about tooling, but I now believe it has evolved into a fundamental reevaluation of job roles and process expectations in engineering. If we look back at other engineering transformations, this one reminds me more of the adoption of social, distributed version control (read GitHub) and cloud computing (read AWS). Social distributed version control provided an excellent example of how technology shifts can fundamentally alter process expectations. The leap from Subversion to Git isn't giant; sure, the tooling has become more complicated, but it has also become more powerful. The sea change came by reorienting around pull requests, feature branch development, and CI/CD. This provided us with tools and conventions for breaking down work, facilitated by agile methodologies, and enabled us to divide work and responsibilities among engineers effectively. The shift to cloud computing and the accompanying DevOps movement have revealed a significant change in job expectations. We observed substantial shifts in our approach from CapEx to OpEx for infrastructure investments, capacity planning, and infrastructure change management. Successful organizations drove infrastructure choices into their engineering organization, reduced deployment timelines, and improved performance and availability. They also took what used to be a job, racking and stacking machines, and reimagined hardware management as software under version control. It took a deep understanding of middleware configuration out of the hands of deep experts and turned it into a self-service API more accessible to developers. The AI transformation we're experiencing today shares the same fundamental characteristics as these previous shifts: it's not just about the tools, but about reimagining how we work. Just as Git transformed collaboration patterns and cloud computing redefined infrastructure ownership, AI is reshaping the very nature of engineering work itself. For engineering leaders, this means our focus must shift from simply rolling out AI tools to fundamentally rethinking our processes, skill development, and team structures. To be successful, you need to recognize AI adoption as a cultural and operational transformation, not merely a technical upgrade. Just as DevOps wasn't really about the tools but about breaking down silos and changing mindsets, successful AI adoption requires us to embrace new ways of thinking about software development itself. The journey we've started from change management through experimentation to process transformation is just the beginning. The real work lies in continuously evolving our practices as these tools mature and in preparing our teams for a future where the line between human and AI contribution becomes increasingly blurred, but human judgment, creativity, and leadership become more valuable than ever.

AI isn’t just transforming how work gets done, but how people grow throughout their careers (https://deloi.tt/44qMFZb).     As foundational tasks become increasingly automated, young professionals are missing out on hands-on experiences that traditionally built confidence and developed proficiency. Meanwhile, tenured professionals are being asked to embrace new technologies that feel unintuitive.     This isn’t just a two-way learning curve, but a huge human capital opportunity.     Evolving our development strategies to meet people where they are means moving beyond traditional mentorship into something more dynamic. More reciprocal. More relevant to today’s rapidly evolving workforce.     Two-way mentorship brings together the AI fluency of early-career professionals and the leadership experience of those with more tenure. As emerging professionals help their senior colleagues build AI literacy, their counterparts can provide general guidance on topics like communication and navigating ambiguity.     This drives more than just individual progress – it creates org-wide workforce transformation. 

Happy International Workers' Day! It’s a fitting time to reflect on how the nature of our "work" is evolving. This recent BCG Henderson Institute study offers a refreshing, nuanced take on the AI revolution: it’s less about a "job apocalypse" and more about a MASSIVE occupational makeover. Here are a few key insights and data points from the report to help you navigate this transition. 📊 The Big Picture: Reshaping > Replacing The headline takeaway is a shift in perspective: automation doesn't strictly equal job loss. Instead, the "how" of our daily tasks is what will change most. Massive Transformation: Over the next 2–3 years, 50% to 55% of US jobs will be profoundly reshaped by AI. The study categorizes the labor market into segments based on how AI interacts with human tasks: The "Amplified" Role: For roles like Software Engineers, AI acts as a superpower. Because the demand for code is "unbounded" (we always want more software), AI helps engineers build more, faster, rather than replacing them. The "Divergent" Trap: These roles (like Insurance Agents) face a split. Entry-level tasks are easily automated, but senior-level judgment remains vital. The risk here is the "broken ladder"—where do the senior experts come from if junior roles disappear? The "Substitution" Reality: In fields with "bounded demand"—like Call Centers or certain Financial Analysis—productivity gains often lead to headcount reduction because there isn't a need for more "output" once a task is finished. Credential Inflation: Durable roles—those least likely to be automated—typically require higher seniority and specialized credentials. 💡 Top Implications for the Future The Cognitive Load is Increasing: As AI takes over routine "execution," human work will concentrate on high-level problem-solving and decision-making. This means work might become more mentally intense and exhausting. AI Fluency vs. Tenure: We are entering an era where being "good with AI" might be more valuable than having 20 years of experience in a legacy workflow. Junior employees who master AI may leapfrog traditional career paths. The "Human" Escalation Layer: Humans are increasingly moving from "doers" to "supervisors." We will manage the AI agents, handle the complex exceptions they can't solve, and provide the final stamp of accountability. 🚀 Strategies for Leaders & Workers For CEOs: Workforce strategy can no longer be an afterthought. It must be embedded in the core business strategy. Cutting staff too early can lead to a loss of "institutional knowledge" that AI cannot replicate. For Workers: Continuous upskilling is the new permanent state. The goal isn't just to learn a tool, but to evolve your role toward system-level thinking and contextual judgment. Read the full study: The original BCG article contains detailed exhibits on industry-specific adoption and a deep dive into "Agentic AI."

Here’s what the headlines are missing: AI isn’t just taking entry-level jobs. It’s reinventing them. Yes, recent research suggests a decline in some traditional entry-level roles in coding, customer service, and beyond. But that’s not the whole story. We’re also seeing AI create something new: the chance for people to learn faster and step into bigger responsibilities much earlier in their careers. Take Jessica Moran, a young associate at KPMG featured in a recent Washington Post article. A task that once consumed 6–8 hours of her day now takes one minute with AI. More importantly, she's using that reclaimed time for risk assessments, project management, and strategic work—responsibilities that used to take years to reach. When AI is a collaborator from day one, entry-level no longer means “waiting your turn.” It becomes a launchpad for creativity, problem-solving, and leadership opportunities. The future of work isn't about fewer opportunities—it's about redesigning them into better ones.   https://lnkd.in/ekxziz8A

If a young engineer can use an AI agent to produce hundreds of calculations that might have taken weeks before the advent of AI – how do they develop the judgement to know whether that output is any good? I’ve been thinking a lot recently about how we address that at Arup. So it was timely that I got to speak to Andrew Hill at the Financial Times for a feature on how AI is reshaping, rather than eliminating, entry-level work. We are already seeing agents automating repeatable tasks and speeding up workflows, while bespoke AI tools are opening up entirely new design solutions. It’s clear that AI is changing the way we design, but it’s also clear that it does not remove the need for technical expertise. In engineering and design, human judgement is a critical asset. Understanding the problem in its context. Testing assumptions. Interrogating outputs and being curious about what makes a difference. Taking professional responsibility for what gets built and how it performs over decades. AI does not carry that responsibility. People do. Young engineers haven’t had to do manual calculations by hand for decades, but they’ve still learned how to judge the outputs of computers and digital programs that take away much of the hard work. AI is changing the learning journey – as did other technological revolutions of the past - and it has inevitable consequences for how we develop people at Arup. Only a small proportion of professional development for our early career members comes through formal training. Most learning happens on the job, and through collaboration with colleagues around you – the questions asked in a design review, the sketch on a napkin, the quiet word from someone who has seen a similar problem go wrong before. Those are the moments where judgement is formed. While AI is changing some of these moments – it’s important to remember we’ve been in a similar place before. To make sure junior colleagues get the learning they need, we need to give them the opportunity to wrestle with real problems, give them exposure to senior thinking, and help them learn what "good" looks like. We’re past thinking about whether or not AI changes the learning journey – it’s clear that it does. But we can still preserve the experiences that build judgement, while helping people use AI well. You can read about how others are grappling with this in Andrew’s FT article here: https://lnkd.in/exTbGbVG