Learn The New Work Before You Lead It
Rethink how you plan, fund, and lead as AI changes the work.
AI has shortened the distance between a decision and its result. Work that once moved through several teams over months can now be planned, produced, and adjusted in days, and the people doing it can see what works almost as soon as they try it. That changes what it takes to lead well. When results arrive this quickly, the quality of your decisions depends on how well you understand the work behind them, and that understanding is hard to keep current from a distance.
This is a real opening for rising leaders. The people who will run AI-era organizations are being shaped now, and the ones moving fastest share a way of learning. They learn the new work by doing it themselves, then bring their teams along with them. That approach is open to you at any level, and it starts with being willing to question what your experience tells you.
Question the Instincts That Got You Here
Every leader carries a picture of what good work looks like, built in the organizations where they learned. That picture is valuable. It lets experienced leaders spot a weak plan quickly and know which problems deserve attention first. It was also formed under conditions that are changing. If you learned that a major campaign takes a quarter to launch, that a new capability requires a large program and a full budget cycle, or that coordinating across teams takes a standing meeting, you will apply those assumptions to new decisions, often without noticing.
The opportunity is to treat your own instincts as ideas worth testing. When a timeline, a cost estimate, or a staffing plan feels obviously right, that is a useful moment to ask what it would look like if AI handled part of the work. Sometimes the instinct holds. Often the answer reveals a faster or less expensive path that experience alone would have ruled out before anyone tried it.
I learned this firsthand when I sat down to build my marketing budget the way I always had, with the same tools and the same categories. It quickly became clear that AI had changed the budget itself along with the operating model. Much of what I had always accounted for no longer applied, and what remained needed new assumptions. I ended up rethinking the budget and the tools behind it from the ground up, so that finance, budget, and resourcing matched how the team now worked.
“The fastest way to update your instincts is to use AI on your own work before you ask anyone else to.”
Learn the New Work With Your Own Hands
The fastest way to update your instincts is to use AI on your own work before you ask anyone else to. Hands-on use teaches you what the tools do well, where they fall short, how much direction they need, and how long the work really takes. That knowledge improves every decision you make about AI afterward, from which projects to fund to how to staff a team to which results are realistic to promise.
Starting small works. Personal efficiency is a practical entry point because the stakes are low and the feedback is immediate. Drafting, research, analysis, and meeting preparation all give you a daily chance to learn how the tools respond to direction. As you become more fluent, the questions you ask about AI get sharper, and the people around you notice.
My own path started this way. I began using AI for personal efficiency while leading marketing inside a large enterprise, well before it was part of any formal plan, and what I learned at my own desk shaped how I later set direction for the team and the organization. I still work this way. I use AI every day to run and improve my own brand, from positioning to promotion, and I am building an AI-native marketing operating model that organizations can put to work with the systems and people they already have. I share what I learn with my mentees and inner circle as I go, so we keep learning from each other as the tools change.
Reset How the Work Gets Done
Organizations increasingly judge leaders by the outcomes they deliver, and AI raises that expectation, because execution that once took months now takes days. A strategy that sounds right earns far less credit than a result people can see. That puts new weight on your ability to apply AI in real time, either directly or by leading the people producing the work, and to understand it well enough to tell strong output from output that only looks strong.
Meeting that expectation means resetting your priorities, where you spend your attention, and how you and your team divide the work with AI. Time that once went to coordinating handoffs and reviewing drafts can move to the decisions that shape results, such as which accounts deserve investment and what the team should stop doing. Used well, AI also becomes a thinking partner, a way to pressure-test a plan or explore options before committing to one, so decisions improve in quality as well as speed.
With the budget rebuilt and the team working fluently with AI, we designed the new operating model from a blank slate and replaced the old one outright, without a lengthy transformation program. That removed hundreds of thousands of dollars in program cost, let us personalize engagement for individual accounts and executives, and eliminated the handoffs between account, sales, and marketing teams that used to slow every campaign.
Learn in Front of Your Team
Your team watches how you approach AI as closely as what you say about it. When you share what you are trying, what worked, and what fell short, you make it easier for others to experiment, and you give them confidence that the direction is being set by someone who understands the work. Learning in the open also spreads skill quickly, because people see real examples tied to what they do every day.
Structure turns that openness into progress. Organize the learning around the team's actual priorities, give each person a clear area to explore, and bring what they find back to the group so each discovery builds on the last. Your role is to keep setting direction as the team's skills grow, so the experiments add up to something the organization needs.
Once my own use of AI had shown its value, I set up workstreams to align the team and bring everyone along so we could learn together. As our proficiency grew, I kept leading the direction we took it and how it connected to the organization's priorities. That shared foundation is what made it possible to rebuild how we worked.
Make Learning Part of How the Team Works
The work AI changes will keep changing. New tools arrive, costs shift, and a task that took a week this quarter may take an hour the next. That makes learning part of the job itself. Build it into how your team operates, so it continues whether or not you are in the room.
In practice, that means reserving regular time to try new approaches on live work, sharing results across the team, and updating roles, budgets, and measures as the work evolves. It also means handing people larger responsibilities as AI takes on more of the routine work, so their growth keeps pace with the tools.
When you learn the new work first and then make that learning part of how your team operates, the organization's capability grows along with its people. Any rising leader can begin, and the place to begin is your own work this week.
About John Fildes
I grow the top line by connecting marketing to business strategy. By leveraging powerful positioning, content marketing, and client insights, I help organizations drive qualitative and quantitative results at scale.
I've built an amazing network of incredibly talented people over the years. What I've appreciated most is those who have invested in me, mentored me, and helped me become the talented professional I am today. I pay it forward by doing the same for other high performing professionals and entrepreneurs.
Learn More: Growth Leader | Business Builder | Leadership Multiplier
All views are my own and not those of my current or prior employers.
