Understanding the Changing Role of Technology

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.

One moment that stood out to me at Stanford University Graduate School of Business was how often discussions on growth, pricing, or org design quickly turned into debates about data foundations, platforms, and AI constraints. Tech wasn’t a sidebar, rather a limiting factor or unlock. The shift felt subtle in real time, but profound in hindsight. More broadly, the role of “tech” inside organizations has evolved significantly, something I’ve seen across consulting, investing, and now B-School. Not long ago, initiatives like #digitization, #cloudification, or platform modernization were framed as good-to-haves that could be sequenced after core business priorities, budget allowing. Today, GenAI, data readiness, cybersecurity, and platform integration sit squarely at the center of exec agendas. Deloitte’s 2025 Tech Exec Survey captures this shift well: 1️⃣ The tech C-suite has expanded. I remember a few years back when orgs had a single CIO/CTO role. Today, many enterprises have distinct roles for CIO, CTO, CISO, Chief Data & Analytics Officer - a reflection of strategic importance and rising complexity. 2️⃣ Technology is increasingly a CEO-level concern. A higher share of CIOs now report directly to the CEO (~65% vs 41% a decade ago), signaling tighter alignment between tech and enterprise strategy. 3️⃣ Tech is no longer just a service function. In many orgs (~52%), technology is explicitly viewed as a revenue enabler, powering new products, faster GTM, and differentiated user experience, vs just operating as a cost center. What does this actually mean? 🎯 Tech teams are more central, visible, and accountable to business outcomes. 🚀 Lines between “business” and “tech” leadership are blurring. It’s no surprise that a growing share of CIOs (~67%) aspire to be CEOs, or that CEOs are spending more time deeply engaged in architectural, data, and AI decisions. 👩💻 For business leaders, this raises the bar on technical fluency. For tech leaders, it demands sharper commercial judgment and ownership of outcomes vs pure execution. The companies that win will be the ones where this interface isn’t a handoff, but a true partnership. #Leadership #TechnologyStrategy #CIO #DigitalTransformation #GenAI #EnterpriseTech #BusinessStrategy #TechLeadership #FutureOfWork #StanfordGSB Picture courtesy: #NanoBanana

𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝗶𝗺𝗲𝘀 𝗮𝘁 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗘𝗻𝗲𝗿𝗴𝘆, 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗔𝗜 These are not times for incremental changes but rather a rethinking and reboot of ideas that push beyond conventional boundaries. Recently, I had the opportunity to interact with a group of Procurement Executives at a nearby nuclear power plant. It's a visit which coincided with growing discussions on AI's insatiable energy demand and the need for pragmatic solutions to address future power gaps. Microsoft's considerations to reactivate one of the Three Mile Island reactors in Pennsylvania and Google's interest to invest in small modular reactors (SMRs) are a noteworthy. Despite concerns and the unresolved challenges of long-term waste storage, Nuclear cannot be disregarded as a reliable and stable part of an energy portfolio. At first, it may appear that AI is the driving force behind this renaissance. But it is more than that. It symbolises a change of perception and a shift in priorities, perhaps a pragmatic approach to bridge for future energy sources with clean technology. The role of Big Tech and Governments to create broad acceptance of rebooting the Nuclear agenda and reduce long-term concerns is crucial to support todays innovations in a sustainable manner. Similarly, in the realm of Procurement & Technology, the driving force for a technological reboot cannot be a trend, hype or AI. From my conversations with various Procurement teams across industries over the last couple of months, it is clear that many are increasingly questioning the future-readiness of their processes and technology stacks. They are reassessing their reliance on existing IT conventions and standards to better shape their strategies with nimbler solution choices infusing innovation and replacing monoliths with modern data and digital architectures. It comes with the realisation that Technology alone without business enablement won't drive long-term change. A change that this time, hopefully leaves people in a better place than before. And talking about people, i can't stress this enough: True transformation, much like the mentioned discourse around nuclear energy and AI, cannot occur without people at its core. In Procurement, it is the leadership teams and stakeholders who need to rally behind a shared commitment to champion the transformation of an integrated business and procurement strategy. Technology's role as a critical transformation enabler is core but alone won't drive the change. It's the commitment, acceptance and trust of people that creates energy and motion and truly powers the change. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗼𝗯𝘀𝗲𝗿𝘃𝗶𝗻𝗴? Are you seeing the same shift of thinking towards long-term outcomes and a re-challenging of conventions? What's your view on how we can address AI's growing thirst for energy, what does this mean for Corporations and their Sustainability agenda.

FCRQ178: Change Leaders Recognise Emerging Technology On 26 December 1982, a magazine cover quietly signalled a turning point in how leaders understood influence, progress, and the forces shaping the future. Time magazine’s decision to name the personal computer as its “Machine of the Year” was more than an editorial surprise. It marked the moment society acknowledged that a new technological presence had moved from the periphery into the centre of human possibility. In this week’s FCRQ, I explore how early recognition, disciplined observation, and the courage to interpret weak signals shaped one of the most significant technological transitions of the modern era. Leadership is rarely about predicting the future with certainty. It is about noticing the shift before others do, understanding its implications, and holding vision steady while the world is still unconvinced. 🔍 What can change leaders learn from emerging patterns, accelerating adoption curves, and the moment when a machine became the most influential force of the year? This reflection is part of the Leadership of Change® series — designed for boards, CEOs, and senior teams navigating AI‑driven transformation with moral clarity, strategic discipline, and the readiness to lead ahead of widespread acceptance. #LeadershipofChange #ChangeLeadership #FCRQ #AILeadership #StrategicDiscipline

For years, the math behind tech investments was simple: spend money, increase efficiency, get a predictable return. But that formula doesn’t work in the age of AI.     Today’s technology isn’t just creating new efficiencies, establishing new value streams, and reducing costs—it’s changing how we work altogether. So, the way we measure growth has to change, too.    Still, many leaders are using outdated playbooks. Deloitte’s new 2025 Global Human Capital Trends report (https://deloi.tt/4lbuME2) found that organizations that are writing the new playbooks are measuring technology in a new matrix.     Rather than focusing on cost savings, they’re looking past short-term efficiencies toward highways for agility, creativity, and talent retention that drive long-term growth.     We’re seeing that AI is changing the game, but it’s not replacing people – it’s expanding what we can do: we’re making better decisions, unlocking bolder creativity, and reimagining capabilities.     Leaders who are gripping onto old ROI models will hesitate and lag. Those embracing a new way of evaluating technology, one that prioritizes human potential with better business outcomes, will shape the future.  

It is a mistake to view the current evolution of work as a simple matter of adopting new software or learning to write better prompts for a machine. We have spent decades tying our professional value to output, measured by how quickly we can process information or move through a list of technical tasks. This focus on speed and volume turned many into operators of digital assembly lines, where the primary goal was always to produce more in less time. The arrival of advanced technology changes that calculation, as it handles the repetitive and analytical parts of our roles with an efficiency no person can match. Ryan Roslansky, the CEO of LinkedIn and EVP of Microsoft Office & Copilot, and Aneesh Raman, LinkedIn’s Chief Economic Opportunity Officer, address this fundamental change in their book, Open to Work. They suggest that every job is essentially a collection of different tasks, many of which are now better suited for automation. The ideas they have shared point toward a necessary transition in how we view our careers. They argue that as the burden of production moves to machines, professionals must reclaim the human capabilities that have been sidelined by the pursuit of efficiency. This shift requires us to move away from the mindset of being a technical processor and instead focus on curiosity and communication. Our future at work depends on our ability to ask better questions and connect with others in ways that technology cannot replicate. By allowing tools to handle high-volume work, we can devote our energy to judgment and creativity. The challenge is deciding how to spend our time once the most demanding parts of our work are handled for us.

Step inside a modern tractor, and you quickly realize this is no longer just a machine for pulling equipment across a field. It is a control center where data, precision, and decision-making come together in real time. From the moment you sit down, you are surrounded by screens that guide every move. You can monitor field conditions, adjust application rates, and follow precise guidance systems without stepping outside. What used to rely heavily on experience alone is now supported by clear, continuous information. This does not replace the farmer. It strengthens the way decisions are made. Instead of guessing, you can see what is happening and respond immediately. That ability to adjust on the go saves time, reduces input waste, and improves overall performance across the field. What makes this powerful is how naturally it fits into the work. The technology is not separate from farming. It is integrated into the daily routine, supporting the operator without slowing things down. You are still farming, but with better visibility and control over every step. This is also changing what it means to be skilled in agriculture. Understanding how to operate equipment is still essential, but now it also involves interpreting data, managing systems, and making faster, more informed decisions. The role is evolving, and those who adapt will be better positioned to succeed. Moments like this make it clear that the future of agriculture is already in motion. The tools are here, and they are becoming part of everyday operations. The real question is not whether this change is happening. It is whether we are ready to fully use what is already in front of us.

If you spend enough time in technology, you start to recognize patterns. One such pattern is what I call "a hammer looking for a nail." I've seen this with innovations like blockchain, and now AI seems to be following the same path. The possibilities are endless, with vendors creating intriguing, focused capabilities, and businesses feeling the pressure to capitalize on this wave of innovation. However, the recurring challenge is that many companies struggle to figure out how to harness this remarkable shift. The risk is that you hear people say “we have to get AI” as a somewhat aimless direction. Consider your business. Perhaps you want to innovate a new capability or automate a specific process. Maybe you wish to enable your customers to access information more quickly and accurately. Or you might aim to improve internal efficiencies by swiftly finding relevant content. While technology evolves, the problems you need to solve remain the same. Issues that once seemed unsolvable may now have solutions. To leverage innovation effectively, you need to thoroughly understand your current state—not just your technology, but how your business operates and what you aspire to achieve in terms of efficiency, problem-solving, or new capabilities for your staff or customers. On the technology and IT side, how ready are you to support this shift? There has never been a time with as many possibilities in the history of tech. The key is to identify the nails, screws, and bolts so you can use the right tools to build your aspirations.

Over Christmas, I found myself sitting with my daughter, looking through old family photos. We came across pictures of my father's old office - walls lined with books, each spine representing a piece of knowledge he deemed worth preserving. It struck me how different her world is becoming from mine, just as mine differed from his. My father's generation dealt with scarcity of information - books were precious, expertise was hard-won, and knowledge was something you had to deliberately seek out and carefully preserve. My generation grew up during the transition to digital abundance - we had Google, but we still needed to know what to search for, how to filter it, and how to retain what mattered. But my daughter? She's growing up in a world where knowledge isn't something you seek or store - it's simply there, like air, responding to her needs before she even articulates them. This shift has me thinking deeply about what it means for her future, and about the different challenges each generation faces as technology reshapes our relationship with knowledge and learning. In this essay, the third in my series about living with AGI in society, I explore how these changes are affecting not just how we learn and remember, but what it means to be human in a world where technology has become as invisible and essential as the atmosphere itself. https://lnkd.in/gB5Zh3fP

In my experience the role of a manager has always been to manage change. But what's really interesting about the tech shifts now is the pace of change is starting to outrun the structure of your organization. It feels like we’re entering an era where titles matter less, and outcomes matter more. I think AI is beginning to dissolve the boundaries between roles. Now the designer codes. The engineer shapes the product. The analyst creates strategy. Suddenly, everyone is a builder. So for banks, I think it's a mistake to just assume this just another tech trend. You have to look deeper at what happening. Flat, empowered teams can move faster, adapt quicker, and unlock more value than traditional hierarchies. And in a world where customer expectations can shift overnight, we know that speed isn’t optional. It makes me think management is more critical than ever. But the role is different. No longer about controlling processes, it’s about clarity of vision, resilience under uncertainty, and the ability to orchestrate people and machines. For banks then, some interesting choices on the horizon. Hold onto rigid structures and risk irrelevance, or embrace a future where leaders move with agility, and every team member becomes a builder.