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The role of a TPM has always evolved with technology, but this moment feels different. AI is not just another tool, it’s literally reshaping how we define work, success, and even leadership itself. As managers and leaders, we now face questions that don’t have easy answers. And maybe the real challenge is not in finding certainty, but in learning how to lead while the ground beneath us keeps shifting.
Like many in the TPM space, we are all watching the world shift in real time. Each week, new milestones are reached with LLMs and AI tools. These breakthroughs raise questions that cannot be brushed aside.
This is especially true for those in leadership roles such as Heads of TPM, Directors, Senior Managers, and People Managers. The expectations placed on you are no longer just about managing programs. They now include guiding your teams through the uncertainty of AI adoption.
I don’t claim to have the answers to everything. But I do have questions that stay with me as I observe how our field is evolving. These questions may not resolve today, but they are worth holding as guideposts for the path ahead.
Senior leaders are calling for teams to “use AI every day.” But how do TPMs translate that call into something concrete? A roadmap is needed, one that balances curiosity with clarity.
When everything is new and shiny, it’s easy to fall into the trap of experimenting for the sake of experimenting. The challenge is knowing which tools actually deliver value, and which are distractions. Throwing random things on the wall may show activity, but it doesn’t create long-term progress.
Beyond engineering workflows and code generation, where else can AI tools create impact? For TPMs, the most powerful opportunities may come when these tools have access to internal data (wiki notes, issue trackers, google documents, and workflows etc). That’s where real transformation will begin.
Every TPM team faces the same hurdle: the steep learning curve. These tools are often not plug-and-play especially when you go beyond ChatGPT or Claude chat like tools. They require re-education, not just on how to use them, but also on the new concepts and mental models they introduce.
The reality is that training doesn’t come free. Leaders will need to find budget for tools, courses, and access. Without investment, adoption will remain shallow.
The bigger question is how to convince organizations that this isn’t optional. AI fluency is fast becoming a core competency for modern TPMs. Those who can’t use these tools effectively risk being left behind.
The calendar of a TPM is already filled with back-to-back meetings. Fires to put out. Deadlines to chase. Where does the space for AI learning fit in?
If leaders expect TPMs to learn and experiment only in their personal time, adoption will suffer. Forcing people to figure this out outside of work sends the wrong message. It signals that AI exploration is a hobby, not a priority.
Creating structured time during business hours for experimentation is critical. Just like we allocate time for sprint planning or retrospectives, space must be made for exploring AI. Otherwise, it will always remain an afterthought.
The language of AI adoption is still immature. Measuring success by counting how many documents or lines of code were generated with AI misses the point. That is activity, not impact.
Similarly, asking why teams aren’t building AI agents when the only accessible tool is ChatGPT is not useful. Leaders must bridge the gap between aspiration and capability.
What TPMs can do is help translate the broad “CEO AI Memo” into actionable, measurable, and trackable terms. By developing a shared vocabulary, adoption can be discussed with clarity rather than confusion.
AI tools will equalize output in many ways. “Good enough” work will become easier to produce. That raises a hard question: how do we know what truly meets expectations?
If so much of the work is AI-assisted, traditional metrics may not apply. Is it still about volume of output? Or does it shift to judgment, decision-making, and creativity?
And what happens when leadership wants to measure AI adoption by asking “how many AI agents did you build this year”? TPM leaders will need to guide that energy toward more productive and fair measures.
Another uncomfortable reality is the impact on team size. When efficiency rises through AI adoption, leaders may ask: “Why do we still need this many people?”
For TPM teams, the challenge is twofold.
First, making the case for the unique value TPMs provide beyond what AI can do.
Second, rethinking growth strategies in a world where elementary activities can be automated.
Headcount and capacity planning will no longer be a simple function of workload. It will demand a deeper understanding of what only humans can bring to the table (think: judgment, alignment, and the human side of program leadership).
The truth is, no one has a perfect playbook for this transition. AI is rewriting the rules as we go, and TPM leaders are being asked to navigate uncharted ground. What we can do is stay curious, create space for experimentation, and redefine success in ways that honor both human judgment and new tools.
The questions we ask today will shape the answers our teams discover tomorrow. The real measure of leadership may not be in having all the answers but in guiding others through the uncertainty with clarity and intent.
Until next time.
-Aadil
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