When I first wrote about the nine things a program manager does, I didn’t dwell on what the job actually feels like first thing on a Monday. The hardest part was always the scramble: a dozen threads that moved over the weekend, a risk or two that quietly materialized, and a critical deliverable whose scope someone changed in a comment buried three levels deep. All of it has to be synthesized into a coherent picture before the leadership sync, and the clock doesn’t care how many tabs you have open.
That scramble hasn’t gone away. But increasingly, I’m not doing it alone.
From async to AI-augmented
I The evolution makes sense in hindsight. I’ve argued that teams should default to async communication — written, durable, discoverable artifacts over synchronous meetings. That managers should use the same tools engineers use — issues, pull requests, project boards — to plan and track their own work. And most recently, that AI agents are extending the same patterns of transparency and code review that made open source successful.
These ideas aren’t disconnected. They’re an evolution.
If the shift from synchronous to async was about decoupling communication from presence, and managing like an engineer was about applying developer workflows to leadership, then AI-first program management is the next logical step: using AI to amplify the judgment, pattern recognition, and relationship work that makes program managers effective.
The through-line is the same principle I’ve been writing about for years: make work visible, make it durable, and reduce the friction between having an idea and acting on it. AI doesn’t change that philosophy. It accelerates it.
AI across the PM toolkit
Here’s how each core PM responsibility shifts when AI enters the picture — and where it doesn’t.
Communication, coordination, and facilitation
Program managers are professional context-switchers. You spend your day translating between engineering teams, product managers, designers, and executives — each with different mental models and vocabulary.1 It’s an O(n²) communication problem, and it only gets worse as the organization grows.
AI doesn’t eliminate that complexity, but it compresses the information-shuttling work. Hand an LLM a 200-message thread and it’ll hand back a paragraph. Feed it the engineering team’s “we’re blocked on a schema migration that requires a backward-compatible rollout strategy” and it’ll translate that into language an executive actually cares about. Drop in a wall of meeting notes and it’ll surface the three things that matter from the twenty that were discussed.
I’ve written about how we communicated at GitHub — optimizing for clarity, discoverability, and low-context readers. AI handles the mechanical work of drafting and reformatting for different audiences. The editorial choices — what to communicate, what to emphasize, when to pick up the phone instead — stay with you.
Capture and track work
One of a PM’s most underappreciated responsibilities is ensuring that work doesn’t fall through the cracks. Every conversation, decision, and commitment needs to land somewhere trackable — usually an issue or a project board.
Point AI at a meeting transcript and it’ll auto-generate the issues. Point it at a month of Slack and it’ll surface the commitments that never made it into a tracker, flag the issues nobody’s touched in weeks, and notice when a project board has quietly drifted from reality. Think of it as a continuous reconciliation process — a linter for your program’s state, catching the gap between what people said they’d do and what the artifacts actually reflect.
This matters because the biggest risk in program management isn’t that something goes wrong. It’s that something goes wrong and nobody notices until it’s too late.
Risk identification and mitigation
Speaking of risk — PMs are supposed to see around corners. In practice, that means reading a lot of threads, attending a lot of standups, and developing an intuition for when something feels off.
AI augments that intuition with pattern recognition at scale. It can analyze velocity trends across repositories, flag pull requests that have been open too long, identify dependencies that no human mapped, and surface cross-project risks that are invisible when you’re looking at one team’s board in isolation. I’ve called transparent collaboration the andon of knowledge work — pull the cord when something’s off. AI is an andon that monitors every cord at once, catching a staffing conflict between two programs before either PM realizes they’re competing for the same engineer’s time.
AI won’t replace the experienced PM’s gut feeling that “this one’s going to slip.” But it surfaces the signals earlier, giving you more time to act. The best risk management has always been about buying time.
Reporting up and across
Nobody became a program manager because they love writing weekly status reports. And yet, clear upward reporting is one of the highest-leverage things a PM does. It’s how leadership knows where to pay attention and where to stay out of the way.
At GitHub, I built an internal tool called SnippetGPT that drafted these reports from a team’s activity for the week — commits, merged PRs, issue closures, comment threads. It rolled all of that up into a first draft aimed at engineering leaders. Instead of spending Friday afternoon assembling a narrative from memory and half-updated boards, I started from something that reflected what actually happened, then layered on interpretation and recommendations.
The key word there is start. An AI-generated status report is a first draft, not a finished product. The PM’s job is to add the “so what” — the strategic interpretation that turns a list of activities into insight. SnippetGPT gave me the what. I added the why it matters and the what to do about it. And once that draft existed, retargeting it for another audience — execs, a partner team, my own engineers — was nearly free: the same week’s activity, recut for whoever needed to read it.
Relationship management
Program management is fundamentally a relationship business. When I first moved into the TPM role, I remember watching senior PMs set up “just wanted to say hello” meetings and dismissing it as socializing on company time — until I realized that’s exactly the point.2 You make regular deposits to the social capital bank long before you need a withdrawal. AI doesn’t replace that. You can’t automate trust.
But it can help you maintain those relationships at scale: a nudge that you haven’t checked in with a particular stakeholder in two weeks, a bit of context surfaced before a meeting — “last time you spoke with this team, they raised concerns about the timeline for the API migration.” A first pass at a thoughtful reply to a tricky message when you’re running low on cognitive bandwidth at 4 PM on a Thursday.
The human work — building rapport, reading a room, knowing when someone’s frustrated versus when they’re genuinely blocked — stays firmly in the PM’s domain. AI just helps you show up more prepared and more responsive than you could be on your own.
Consensus and conflict resolution
Driving consensus across teams with competing priorities is among the hardest things a PM does. It requires understanding not just each team’s position, but their interests — the underlying needs that drive those positions.
AI can help by synthesizing different viewpoints, drafting proposals that incorporate multiple perspectives, and suggesting compromises based on stated constraints. Need an RFC that reflects six teams’ input? AI can produce a first draft that no single person could assemble manually — but you still need to facilitate the conversation that turns a draft into a decision.
Where AI falls short is in reading the political dynamics. Understanding that Team A’s objection is really about being burned in a previous launch, or that a particular VP’s silence means something different than a junior engineer’s silence — that’s pattern recognition of a deeply human kind. No model is going to learn the org chart’s shadow topology from a prompt.
What doesn’t change
It would be easy to read all of this and conclude that AI is about to make program managers obsolete. The opposite is true.
The responsibilities haven’t changed. Communication, risk management, relationship building, consensus-driving — these are still the job. What’s shifted is the ratio of information processing to judgment in a PM’s day. AI handles more of the former so you can focus more on the latter.
The things AI can’t do are precisely the things that make great PMs great: reading a room, building trust over months, knowing when to push and when to back off, navigating organizational politics, and making the hard call when the data is ambiguous. These are human skills, and they become more valuable as AI handles the routine work, not less. When everyone has access to the same AI tools, the differentiator is the human wielding them.
What changes for PMs
That said, AI-first program management does require new muscles — or at least, new applications of existing ones. Four capabilities stand out:
Prompt craft matters. The quality of what AI produces depends entirely on the quality of what you ask for. It’s garbage in, garbage out — a principle as old as computing, now pointed at prompts instead of punch cards. PMs who can write clear, specific prompts — essentially, good requirements — will get dramatically better results than those who can’t. It turns out that years of writing crisp issue descriptions and well-defined acceptance criteria is excellent training for working with LLMs. Good requirements have always been a PM superpower. Now they’re a superpower twice over.
Knowing when not to use AI is as important as knowing when to use it. A sensitive personnel conversation, a politically charged escalation, a message to a stakeholder who’s having a rough week — these require a human touch that AI can’t fake. Developing judgment about when AI helps versus when it gets in the way is a skill in itself, and it’s one that’s hard to teach in the abstract.
Verification becomes a core competency. AI will confidently summarize a thread and miss the most important nuance. It’ll draft a status report that’s 90% accurate and 10% dangerously misleading. PMs need to be skilled editors and fact-checkers, not just consumers of AI output. The role shifts from author to editor-in-chief — you set direction, review drafts, and ensure quality before anything ships.
AI fluency is table stakes. Just as PMs were expected to be comfortable with project management tools, version control, and whatever collaboration platform their teams use, they’ll be expected to work fluently with AI assistants. Not as a novelty, but as a core part of the daily workflow — the way we already think about Slack or email or GitHub Issues.
The PM as orchestra conductor
The mental model I keep coming back to is the program manager as orchestra conductor. A conductor doesn’t play every instrument — they don’t play any instrument during the performance. But they’re essential: they set the tempo, bring in the right sections at the right time, interpret the score, and turn individual performances into a coherent whole.
AI agents are new instruments in the orchestra. They play fast, they play consistently, and they can handle parts that used to require a human musician. But someone still needs to read the score, understand the audience, and make the hundred small decisions that turn a technically correct performance into something that actually moves people. That’s the PM’s job — and it always will be.
The shift from synchronous to async made program management more intentional. Managing like an engineer made it more transparent. AI makes the same hours count for more. Each evolution builds on the last, and each one makes the human judgment at the center of the work more important, not less.
Much of the job is a long tail of grab-bag work that never fits a clean category — writing the missing spec, scheduling the meeting nobody wants to own, reformatting a spreadsheet at 9 PM because a VP asked for a different view of the data. AI is exceptional at exactly this kind of mundane-but-necessary task, and it’s the easiest place to start. If you’re a PM wondering where to begin, pick the task that consumes the most time but requires the least judgment — status report assembly, meeting note cleanup, stakeholder update drafts — and hand it to an AI. You’ll free up hours for the work that actually drew you to the role: solving hard problems with smart people across organizational boundaries.
Footnotes
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Tally up the number of distinct audiences a PM communicates with in a single week and the answer is genuinely depressing. Every additional team or stakeholder doesn’t just add one more communication channel — it adds one for every stakeholder you already had. This is why PMs’ calendars look the way they do. ↩
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As much as I might like them to be, human-to-human requests are unlike server-to-server requests. A properly authenticated request from a never-before-seen client is less likely to be fulfilled, or fulfilled in a timely manner, even if it’s facially valid. Invest in the relationship before you need the favor. ↩