Post 5 of 6: The AI-Augmented Product Director
The previous posts in this series have been about AI as a writing assistant, a tool you interact with to produce better output faster. This post is about something different: AI as an agent that does work on your behalf, without you having to initiate it.
This is the part of the AI story that most product management content skips over, because it requires a bit more setup and the value is not immediately obvious. But once you have experienced it, it is hard to go back.
The Difference Between Assisted and Automated
Most AI use today is assisted: you prompt, it responds, you take the output and do something with it. That is everything I described in the previous posts. Valuable, but still requiring your initiation and attention.
Automated AI is different. You define a workflow once: what to do, when to do it, where to put the result. Then it runs without you. You come back and find work done that you did not have to think about triggering.
For a director, the candidates for automation are usually the tasks that are:
- Recurring (happen weekly, daily, or per-event)
- Formulaic (same structure every time, just different inputs)
- Time-consuming relative to their cognitive complexity (high labor, low thinking)
Meeting notes are the obvious example. But there are others.
Meeting Notes: The Classic Case
Before automation, my meeting notes process was embarrassingly inconsistent. Sometimes I would take notes in real time. Sometimes I would rely on the calendar invite summary. Sometimes I would mean to write up the key decisions and forget by the next day.
Now, for most recurring meetings, the process is automated: the transcript gets pulled, processed through AI, and the structured summary (decisions, action items, key context) shows up in my notes folder before I have even finished my next meeting.
The value is not just the time saved. It is the consistency. Every meeting gets the same treatment. Nothing falls through because I was distracted or in back-to-back calls. Action items that used to live in my head (and sometimes die there) are captured and visible.
What to look for in your own workflow: Any meeting you attend regularly where you care about capturing decisions and follow-ups is a candidate for automated notes. The setup time is usually a few hours; the payoff accrues every week after.
The Weekly Status Digest
Every week I owe someone a status update on something, usually several somethings. This is the kind of task that feels small but adds up: gathering the inputs, remembering what changed since last week, formatting it for the right audience.
I have automated the gathering piece. A scheduled task runs each Friday morning, pulls the relevant Jira tickets for each active initiative, formats them by status, and drops the structured summary into a draft. I review and edit the draft, but I am not starting from scratch. I am editing, which takes five minutes instead of twenty.
The key design principle here: automate the gathering and structuring, not the judgment. I still decide what is notable, what needs escalation, and how to frame risks. The machine does the compilation. I do the editorial pass.
This distinction matters especially in healthcare tech, where what you communicate and to whom has real stakes. Automating the prose on a regulatory or compliance status update is not something I would recommend. Automating the input compilation so you can write better prose faster is exactly right.
Ticket Grooming Support
Backlog grooming is another category where I have found automation useful, specifically the first-pass triage that happens before a formal grooming session.
My setup: a scheduled task runs before each sprint cycle, scans the backlog for tickets that have not been touched in a defined period, and produces a list organized by: (a) probable candidates for deletion or deferral, (b) tickets that look underspecified based on their description, and (c) tickets where the original priority may need revisiting given recent changes.
This does not replace the grooming session. But it makes the grooming session faster and more focused. Instead of starting with 80 tickets and figuring out where to begin, I start with a structured shortlist and use the session for the conversations that actually need humans in the room.
Notification Routing and Inbox Triage
This is a newer addition to my workflow, but it has become one of my favorites. I get a high volume of messages across different channels: email, team chat, notifications from project management tools. Not all of it requires the same response or urgency.
I use an AI triage pass on my communication inputs that does a first sort: what needs a response today, what is informational only, what can wait until end of week, and what has been flagged as needing my attention but probably does not.
The result is not that I respond to fewer things. It is that I process my inbox in a sequence that matches actual priority rather than recency. The important stuff surfaces; the informational noise gets batched to a time when I am not doing deep work.
How to Think About What to Automate
Not everything should be automated. Here is the heuristic I use:
Automate if: The task is recurring, the inputs are predictable, the output is something you review before acting on, and the cost of a wrong output is low.
Don't automate if: The task requires real-time judgment, involves sensitive stakeholder relationships, has compliance implications, or where a "good enough" output would be worse than no output.
That last point is important. A mediocre automated status update that goes to an executive every week without your review is not a time-saver. It is a liability. Automation should always have a human review step for anything that leaves your team's hands.
The Setup Investment
I want to be honest: automation takes more upfront work than prompted AI use. You are designing a workflow, not just writing a prompt. The questions you have to answer take time to think through: what are the inputs? What is the trigger? What does "done" look like? Where does the output go? Who reviews it?
But the payoff compounds in a way that one-off prompts don't. A prompt you write once is a one-time win. A workflow you design once runs every week. I have set up probably a dozen automated workflows over the past year, and the cumulative time savings is significant.
My recommendation: start with the single most predictable, recurring task you dread each week. Design the workflow for just that. Get it working well before you expand.
The Bottom Line
The most powerful shift in how I use AI is not the writing assistance. It is the move from reactive use (I prompt, it responds) to proactive automation (it runs, I review). The former makes individual tasks faster. The latter systematically removes a category of work from my week.
For a director, whose time is always the constraint, that is the difference that compounds.