Defining An AI Native Technical Program Manager

2025.18 - 7th Post - To prepare for a future with fewer TPMs, we need better ways to measure how close that future is so we can start to figure out what is left for us to do, and hone those aspects.

This is the 7th and final post in a series where I’m pondering out loud in public about how AI might reshape or perhaps, in my opinion, replace the Technical Program Manager role.

In previous essays, I have explored how AI is reshaping the way TPMs process messy inputs, synthesize signal from noise, distribute that signal to the right people in the right way, establish accountability across programs, and last week I dove into how we troubleshoot complexity. With each function, AI is starting to take on more of the heavy lifting.

This last piece zooms out to ask: what happens when all of these functions are being done by agents, what’s left for us humans to do?

The answer, I believe, is orchestration.

It goes beyond the traditional definition and expectation involving organizing tasks, running meetings, nudging teammates to update Jira. But a deeper orchestration, something at the systems level. One where you, as a TPM, are designing and managing a hybrid system of humans and AI agents, making decisions about what to automate and when, what to escalate and why, and how to build resilient, trustworthy workflows in a world that is far more fluid, elastic, automated, prone to hallucinations.

That is the world of the AI Native TPM.

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The Future AI Native TPM Is a Systems Orchestrator

In my experience, successful orchestration shows up in two ways.

1. First, there’s visible forward momentum. Execution is moving. Milestones are ticking off. You’re seeing demos, you’re making decisions, and the feedback loop between learning and adjusting is tight and active.

2. Second, when something does go wrong the issue is handled quickly, cleanly, and with minimal churn. The conversations may be hard, but they’re grounded in respect and clarity. The fallout is managed, not allowed to spiral.

When that kind of execution culture is in place, where people know their roles, feel energized by the work, and stay focused on what matters, you see the outcome of a well-orchestrated system. High levels of psychological safety and perfect balance of autonomy.

In large complex and multi-team organizations, building and maintaining that successful orchestration has always been the job of a TPM. AI won’t change that. It will just change how we do it.

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The Age of the AI Execution Layer

Today, we’re entering a world where much of the tactical work that TPMs used to do can be and in some ways is being eaten up by GenAI tools and purpose-built AI agents:

• Tools like Cursor, Windsurf, and Figma’s AI features can convert high-level intent into working prototypes or code.

• Replit’s Ghostwriter can co-develop features, troubleshoot, and suggest test cases.

• Meeting tools like Zoom AI Companion and Granola summarize conversations and auto-generate follow-ups.

• Agents running inside tools like Linear, Jira, and Slack can assign tickets, monitor progress, and nudge DRIs automatically.

• Glean is making information retrieval, recall and synthesis as simple as having a conversation.

We’re not just talking about assistants anymore. These tools are and will take shape of actors in the system alongside human actors.

This means that the future TPM won’t just be managing a team of people but rather they’ll be managing a mesh of tools, agents, and workflows that execute work semi-independently. And that changes the game.

For the first time, the expectations of TPMs being systems thinkers, force multipliers and all the fancy system like buzz words will become real.

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The Silent Failure Risk

The greatest risk of AI-native orchestration isn’t failure — it’s the quiet failure.

• When everything looks like it’s moving, but something critical is broken underneath.

• Maybe an agent hallucinated a risk summary based on a throwaway comment.

• Maybe a set of tradeoffs were optimized for efficiency, but misaligned with product goals.

• Maybe work appears to be “on track,” but no one has actually reviewed the output.

This is where humans will remain indispensable. Because when agents execute, but no one orchestrates the system, you get confidence without clarity. Progress without direction. Automation without wisdom.

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The Human Edge

Here’s what I think stays uniquely human and what the AI native TPM of the future will still do better than any AI:

1. Navigating ambiguity and politics

Organizational dynamics—competing agendas, subtle misalignments, trust issues—can’t be optimized away. They must be observed, decoded, and gently navigated.

2. Designing systems, not just tasks

AI agents can complete a Jira ticket. But who designed that ticketing workflow? Who decided which agents to run and which decisions need human review? That’s orchestration.

3. Reading emotional and motivational signals

TPMs know when an engineer is stuck but unaware of the bigger picture to ask for help proactively. When a team says “we’re on track” but you can sense they aren’t. When scope is ballooning subtly but no one’s named it yet. The slow drip and miss of milestones day by day week by week.

4. Deciding what shouldn’t be automated

This is a big one. Just because you can run something with an agent doesn’t mean you should. TPMs will need the judgment to say no. To protect process from over-automation. To know which rituals are essential to keep human.

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From Automation to Orchestration

Before the AI-native world, TPMs scaled themselves by hacking together workflows

• Slackbots for standups.

• Notion/Confluence templates for project tracking

• Jira filters linked to dashboards and Airtables and spreadsheets.

• 1-pagers galore to tame the flow of information.

It often came at the cost of nights and weekends. The solutions rudimentary but get the job done to free up time for you to focus on more strategic elements.

Now, there is a very real possibility of building and deploying autonomous agents to handle those same tasks. But orchestration means stepping back and asking: should I?

• When do we override an agent’s output?

• What escalation paths do we design when agents fail silently?

• How do we preserve accountability, alignment, and progress in spite of machine speed?

• Should this process remain not agentified?

Orchestration becomes an act of ongoing vigilance, of tuning the system, watching for fragility, and designing rituals that dictate how the machine works.

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So, What Should TPMs Upskill In?

If I had to name the core skills for the next-generation AI Native TPM, I’d say:

• Agent orchestration – understanding how agents work, how they fail, and how to deploy them with intention.

• Narrative storytelling – translating complexity into clarity, and surfacing the “why” behind the automation.

• Cognitive discernment – knowing which signal to trust, and when to override what the system is telling you.

• Prompt and protocol design – shaping how humans and agents interact and how to make the machine do what you want done.

• System-level thinking – understanding not just the flow of tasks, but the architecture of how work gets done.

• Deep Curiosity/Experimentation - these tools are changing on rapid pace. TPMs must keep themselves aware of and experiment with new tools.

🤔 What about all the agile methodologies and scrum certifications and rituals we need to learn to be a TPM? Those core core skills will not go away but the software development lifecycle will evolve thanks to the advancement in AI. The gap between idea to production will shrink massively. What will become important for TPMs is know how to mix and reformulate broad methodologies and build bespoke methods of developing products and then know how to automate what parts with agents.

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Final Words: A Truly Systems TPM

My goal is not to scare or feed into the doom cycle of humans being replaced by AI but to understand a possible future out of many and how I need to change myself to survive what is coming for my own future career prospects.

The future AI native TPM may not write updates, run standups, or even track Jira tickets. They’ll do something harder: keep the system coherent.

When things move fast and the machines are in control, the human role isn’t to speed things up but instead it’s to ask the right questions, challenge blind spots, and decide what kind of system we actually want to live with.

If I look beyond the horizon and imagine with all my might, experience and knowledge, this is the future I see. Perhaps the way “we will need fewer TPMs in an AI world” manifests itself as fewer task managers and a rise in systems orchestrators.

The stark reality we are fast approaching is that if your work is more than 50% task orchestration and management, then I am not sure if that role is still around anymore.

The AI native TPM will not long just manage work or as they say keep the trains running, they will design the systems from train to rail, beyond the methodologies, they will dictate the ways of working for both actors — human and AI.

Until next time!

-Aadil