
AI Irreplaceability Protocol
Your 30-day plan to become the person AI can't replace.
A day-by-day plan that turns your Exposure Map into a role only you can do — one task, one tool, one shipped result at a time.
Private & Confidential · Premium Protocol · Prepared exclusively for the named recipient · © whatsmyedge.com
Profile Summary
The 30-day bet, in one page
Where you are, what's actually at stake, and the wager this Protocol makes on your behalf.
You are a Product Manager in SaaS with roughly six years of experience. Your week is roadmap planning, cross-functional coordination, stakeholder management, competitive research, and feature specification. A large share of that work — status reporting, research scans, first-draft specs, metrics narratives — is now inside the reach of a capable model. The judgment half of your role — deciding what to build, reading the room, framing the argument — is not.
Your organization is actively investing in AI, which raises both the urgency and the opportunity. The PMs who demonstrate AI-native workflows first will define how the practice operates. This 30-day plan hands the exposed half of your week to AI you operate, and reinvests the reclaimed hours into the judgment work that compounds. Your composite exposure score of 68 places you in the contested zone: enough is automatable to be a real risk, and enough is defensible to be a real opportunity.
Mission
Transform from a feature-list executor into an AI-orchestrated product strategist who runs autonomous research, synthesis, and prioritization workflows — and is visibly credited for the leverage they create.
FromLevel 1 · AI-Aware User
uses ChatGPT occasionally.
ToLevel 3 · AI Orchestrator
designs multi-agent systems.
The Method
The 5-Step AI Integration Method
Every task in your protocol follows this cycle: Describe → Generate → Validate → Iterate → Review. Master it once, apply it everywhere.
1 Describe
Write a clear description of what you need. Include context, constraints, format, and audience. The better the input, the better the output.
2 Generate
Feed your description to the AI tool. Accept the first output without judgment. Your goal is raw material, not perfection.
3 Validate
Compare the output against your domain expertise. Mark what is accurate, what is wrong, what is missing. This is where your value lives.
4 Iterate
Feed corrections back. Refine the prompt. Repeat until the output meets your professional standard. Track what worked.
5 Review
Document the workflow. Save the prompt. Measure time saved. Decide if this replaces, augments, or does not help the manual process.
Print this page · pin it next to your monitor · reference it every day until it becomes automatic.
Exposure Diagnosis
What survives. What's already gone.
AI Exposure is the share of your work a capable AI can already do well enough that nobody would pay a human for it. Your work is split into tasks and scored across five dimensions. Green is yours. Red is already gone.
| Task |
Pattern | Processing | Analysis | Execution | Output |
Composite |
| 01Weekly Status Reports |
90 | 88 | 85 | 80 | 75 |
91 · Exposed |
| 02Competitor Research |
75 | 80 | 70 | 65 | 60 |
78 · Exposed |
| 03Metrics Reporting |
74 | 78 | 66 | 60 | 55 |
72 · Exposed |
| 04Feature Specification |
55 | 60 | 50 | 45 | 40 |
55 · High |
| 05Roadmap Prioritization |
46 | 48 | 42 | 40 | 36 |
44 · Contested |
| 06Stakeholder Negotiation |
20 | 25 | 15 | 18 | 12 |
18 · Safe |
Exposed 70–100
High 55–69
Contested 40–54
Safe 0–39
Each task is scored 0-100 on five dimensions of automatability. Green is yours to keep and compound. Red is already inside the model's reach.
Phase 0 · Replaceability Diagnosis
What a model can take. What only you can hold.
Two columns decide your career: what a model can already take, and what only you can hold.
Replacement Risks
Status reporting: Your weekly reports follow a template AI can populate from project data in seconds.
Competitor research: Structured, repetitive scan work is exactly what AI agents excel at.
Metrics narration: Turning dashboards into prose is now a one-prompt job.
Irreplaceable Traits
Client intuition: Reading unspoken concerns in sales-adjacent meetings.
Strategic framing: Knowing which three features to cut versus ship.
Organizational trust: Being the person leadership believes when the data is ambiguous.
The bridge:
Your 30-day plan automates the left column so aggressively that you reclaim 12+ hours per week, then redirects that time into amplifying the right column — the judgment work no model can take.
Your plan at a glance
The 30-day racecard.
Every day, every phase, and the one thing you ship — the whole plan on a single card before you begin.
| Day | Focus | Tool | You ship |
| Phase 0Map & Triage |
| 01 |
Audit Your Real Week |
ChatGPT |
Weekly Task Audit |
| 02 |
Score Your Task Map |
Claude |
Scored Task Map |
| Phase 1Destroy Your Own Job |
| 03 |
Automate Your Weekly Status Report |
ChatGPT |
Status Report Prompt |
| 04 |
Meeting Prep Automation |
ChatGPT |
Meeting Prep Template |
| 05 |
Competitor Scan System |
Perplexity |
Competitor Brief |
| 06 |
PRD First-Draft Generator |
Claude |
PRD Draft |
| 07 |
Customer Feedback Synthesis · Milestone |
Claude |
Feedback Themes |
| 08 |
Roadmap Update Automation |
ChatGPT |
Roadmap Update |
| 09 |
Stakeholder Digest |
ChatGPT |
Decision Digest |
| Phase 2Build the Platform |
| 10 |
Chain Two Tools |
Perplexity |
Research-to-Synthesis Chain |
| 11 |
Reusable Prompt Library |
Claude |
Prompt Library |
| 12 |
Discovery Interview Analyzer |
Claude |
Insight Table |
| 13 |
Metrics Narrative Generator |
ChatGPT |
Metrics Narrative |
| 14 |
Prioritization Copilot · Milestone |
Claude |
Prioritization Copilot |
| 15 |
Spec-to-Ticket Pipeline |
ChatGPT |
Ticket Set |
| 16 |
Release Notes Autowriter |
Claude |
Release Notes |
| Phase 3Make Yourself Revenue-Critical |
| 17 |
Ship an AI-Native Workflow |
Claude |
Team Workflow Kit |
| 18 |
Quantify Hours Reclaimed |
ChatGPT |
Time-Savings Model |
| 19 |
Present the ROI to Leadership |
Claude |
ROI Deck |
| 20 |
Train One Peer |
ChatGPT |
Peer Training Note |
| 21 |
Propose an AI-Product Initiative · Milestone |
Claude |
Initiative Proposal |
| 22 |
Draft the Business Case |
ChatGPT |
Business Case |
| 23 |
Claim the Next Seat |
ChatGPT |
Repositioning Brief |
| Phase 4Institutionalize |
| 24 |
Document Your Playbook |
Claude |
AI Playbook |
| 25 |
Delegate a Workflow |
ChatGPT |
Delegation Handover |
| 26 |
Weekly AI Review Ritual |
Claude |
Review Ritual |
| 27 |
Team Prompt Registry |
Claude |
Team Prompt Registry |
| 28 |
Measure Team Adoption |
ChatGPT |
Adoption Snapshot |
| 29 |
Write Your New Role Description |
Claude |
New Role Description |
| 30 |
The 30-Day Review and Next 60 · Milestone |
Claude |
30-Day Review |
The Protocol
30 days. One task a day.
It won't happen to you — you do it. Each day is one concrete move: the task, the exact tool, and the one thing you ship by end of day. Green rows from the map get protected and compounded; red rows get handed to the AI you now run.
Phase 0: Replaceability Diagnosis
Map & Triage
See the whole week of work honestly and decide what to protect, automate, and drop.
01
Audit Your Real Week
Phase 050 minStep 1: Describe
- Task
- Reconstruct where your time actually went this week from raw calendar and task exports, then classify every block by what it demands of you. The goal is an honest map of your real work, not the job description version of it.
- Export last week from your calendar and pull your closed and touched Jira items
- Paste both into the model with your role and typical responsibilities
- Ask it to list every distinct activity with an estimated hours-per-week figure
- Have it tag each activity as thinking, coordinating, producing, or reacting
- Copy the table into a sheet and correct any hours that feel wrong
- Artifact
- Weekly Task Audit sheet
- Ship
- A single sheet listing every recurring activity in your week with hours and a work-type tag.
Concept lockYou can only automate work you have named and measured.
Starter PromptHere is my calendar export and Jira activity for one week. My role: [PRODUCT MANAGER, SAAS].
List every distinct recurring activity you can infer, with an estimated hours/week for each.
Tag each as: Thinking, Coordinating, Producing, or Reacting.
Return a single table sorted by hours descending.
Inputs:
- Calendar: [PASTE]
- Jira activity: [PASTE]
WhyYou cannot automate work you have never named, and most of your week is invisible to you until it is written down. This audit is the baseline every later day measures against, so an honest one now pays off for the whole month.
Reflect before moving onWhich activity surprised you with how many hours it takes? · How much of your week is reacting versus thinking? · Which tasks would you never miss if they vanished?
02
Score Your Task Map
Phase 045 minStep 2: GenerateStretch
- Task
- Take yesterday's audit and score each activity on two axes: how exposed it is to AI and how much it drains you. The output is a ranked triage list that tells you exactly what to automate first.
- Feed the model your Weekly Task Audit sheet from day one
- Ask it to score each activity from one to five on AI-exposure and on personal drain
- Have it multiply the two scores into a single priority number
- Sort the list so the highest combined score sits on top
- Mark the top three as your automation targets for this month
- Artifact
- Scored Task Map sheet
- Ship
- A ranked sheet where the top three targets for automation are explicitly circled.
Concept lockAutomate where high AI-exposure meets high personal drain.
Starter PromptHere is my Weekly Task Audit. For each activity, score two axes from 1 to 5:
- AI-exposure: how well current AI tools could do this task
- Drain: how much this task tires or bores me
Multiply them into a Priority score, then return the full table sorted by Priority descending.
Audit: [PASTE]
WhyNot all tasks are worth automating, and chasing the wrong one wastes a week of effort for little return. Scoring forces you to spend your energy where exposure and drain overlap, which is where the fastest, most durable wins live.
Reflect before moving onDid the highest-priority task match your gut, or surprise you? · Is anything high-drain but low-exposure that you should hand off instead? · What would this week feel like without the top three tasks?
Phase 1: Destroy Your Own Job
Destroy Your Own Job
Hand the exposed, repetitive work to the AI you now run — and prove it.
03
Automate Your Weekly Status Report
Phase 160 minStep 3: Validate
- Task
- Reverse-engineer the prompt that reproduces your weekly status report from raw inputs, then run it against this week. The goal is a one-pass draft you trust enough to edit rather than rewrite.
- Collect this week raw inputs: Jira export, key Slack decisions, and metric deltas
- Paste them with a description of your report format, audience, and tone
- Ask the model to produce the full report in your structure
- Diff its draft against what you would have written and note every gap
- Fold the gaps into the prompt and save the final version
- Artifact
- Status Report Prompt prompt
- Ship
- A saved prompt that produces an 80-percent-usable status report in one pass.
Concept lockA good prompt encodes your format once so you never re-explain it.
Starter PromptYou are drafting my weekly status report. Audience: [VP PRODUCT]. Tone: concise, executive.
Inputs:
- Jira summary: [PASTE]
- Key decisions: [PASTE]
- Metric deltas: [PASTE]
Output sections: Exec summary (3 lines), RAG table, Risks, Next-week outlook.
WhyStatus reporting is your single highest-exposure task, so automating it first frees the most time immediately. It also proves the whole method works on day one, which builds the momentum the rest of the month depends on.
Reflect before moving onWhere did the draft still need your judgment? · What context did you have to add by hand? · Which other weekly report could reuse this pattern?
04
Meeting Prep Automation
Phase 145 minStep 4: IterateStretch
- Task
- Build a reusable template that turns a meeting title and a few links into a full prep pack: objective, key questions, and the decision you need out of the room. Run it on your three biggest meetings this week.
- List the recurring meetings you walk into underprepared
- Give the model the agenda, attendees, and any relevant docs or threads
- Ask it to produce an objective, three sharp questions, and the single decision to land
- Have it flag what could derail the meeting and how to steer back
- Save the structure as a template you fill in before every meeting
- Artifact
- Meeting Prep Template prompt
- Ship
- A reusable prep template that turns any meeting into a one-page brief in under five minutes.
Concept lockPreparation is a template, not a personality trait.
Starter PromptPrepare me for this meeting. Attendees: [LIST]. Agenda: [PASTE]. Relevant docs: [PASTE].
Return:
- Objective (1 line)
- The single decision I need out of this room
- 3 sharp questions to ask
- 2 likely derailers and how I steer back
Keep it to one page.
WhyWalking into meetings unprepared is where PMs quietly lose influence and time, meeting after meeting. A prep pack that writes itself means you are the sharpest person in every room without the hour of manual reading.
05
Competitor Scan System
Phase 155 minStep 5: Review
- Task
- Build a repeatable competitor scan that pulls recent product, pricing, and positioning changes across your top rivals into a single brief. Run it on three competitors and save the query so next month is one click.
- Name your three closest competitors and the signals you care about
- Ask the tool to surface changes from the last quarter with sources for each claim
- Have it separate confirmed changes from rumours and speculation
- Request a short so-what line on how each change affects your roadmap
- Save the query and set a recurring reminder to rerun it monthly
- Artifact
- Competitor Brief doc
- Ship
- A sourced competitor brief covering three rivals with a so-what line on each change.
Concept lockA standing scan beats a frantic search every time.
Starter PromptAct as a competitive analyst. Competitors: [A], [B], [C]. My product: [DESCRIBE].
Find product, pricing, and positioning changes from the last 90 days.
For each: cite the source, mark it Confirmed or Unconfirmed, and add one so-what line on how it affects my roadmap.
Group findings by competitor.
WhyCompetitive blind spots get discovered in the worst possible moment, usually mid-review in front of leadership. A standing scan means you always know what moved and why it matters before anyone thinks to ask you.
06
PRD First-Draft Generator
Phase 160 minStep 1: Describe
- Task
- Turn a rough problem statement and a handful of inputs into a structured PRD first draft you can react to instead of stare at. The point is to kill the blank page, not to ship the model's words unedited.
- Gather the problem, target user, known constraints, and any research links
- Feed them to the model with your standard PRD section headings
- Ask for a full first draft with explicit open questions flagged inline
- Challenge every assumption it made and rewrite the ones that are wrong
- Save your section headings and instructions as a reusable PRD scaffold
- Artifact
- PRD Draft doc
- Ship
- A structured PRD first draft with open questions flagged, ready for your edits.
Concept lockA draft you can argue with beats a blank page you can only fear.
Starter PromptDraft a PRD from these inputs. Problem: [STATE]. Target user: [WHO]. Constraints: [LIST]. Research: [PASTE].
Use these sections: Context, Goals, Non-goals, User stories, Requirements, Risks, Open questions.
Flag every assumption you make inline as [ASSUMPTION] and every gap as [OPEN QUESTION].
WhyThe blank page is where product work stalls, and a first draft you disagree with is far more useful than no draft at all. Reacting is faster than authoring, so you get to the sharp thinking sooner and spend your judgment where it counts.
Reflect before moving onWhich assumption did the model get wrong, and why? · Where did reacting to a draft sharpen your own thinking? · What belongs in your permanent PRD scaffold?
07
Customer Feedback Synthesis
Phase 160 minStep 2: Generate
- Task
- Feed a month of raw feedback from support tickets, reviews, and calls into a single pass that clusters it into themes with frequency and representative quotes. This replaces the afternoon you usually lose skimming and half-remembering.
- Gather raw feedback: support tickets, app reviews, sales notes, and call snippets
- Paste it in with instructions to cluster into themes, not summarise
- Ask for each theme with a frequency count and two verbatim quotes
- Have it separate feature requests from pain points from praise
- Rank themes by impact and drop the top three into your roadmap inputs
- Artifact
- Feedback Themes doc
- Ship
- A themed feedback summary with frequency counts and real quotes for the top three issues.
Concept lockThemes with counts and quotes beat opinions in every roadmap debate.
Starter PromptCluster this raw customer feedback into themes. Do not summarise each item, group them.
For each theme return: a name, a frequency count, 2 verbatim quotes, and a type tag (Pain / Request / Praise).
Rank themes by impact on retention. Feedback:
[PASTE]
WhyCustomer signal is your strongest source of authority in any roadmap fight, but only if you can produce it fast and back it with real quotes. Turning a messy inbox into ranked themes in one pass makes you the person who actually knows what users think, not just claims to.
Reflect before moving onWhich theme was bigger than you assumed it was? · Did any quote change how you frame the problem? · Which theme do you now have the evidence to push on?
Milestone — Day 7
Week 1: Exposed Tasks Automated
- 5 exposed tasks automated
- Weekly reporting runs in one pass
- 6+ hours/week reclaimed
Level 1 confirmed: AI-Aware User
Week 2 turns one-off wins into reusable systems.
08
Roadmap Update Automation
Phase 150 minStep 3: Validate
- Task
- Generate a clean roadmap update from your current Jira state and the decisions made this week, framed for a non-technical audience. The goal is a narrative of progress and change, not a raw ticket dump.
- Pull your epics and their current status from Jira
- Give the model the status plus any scope or priority changes from this week
- Ask for a themed update grouped by outcome, not by ticket
- Have it call out what changed since last update and why
- Save the format so each roadmap update takes minutes, not hours
- Artifact
- Roadmap Update doc
- Ship
- A stakeholder-ready roadmap update grouped by outcome with changes explained.
Concept lockReport outcomes, not tickets, and you sound like an owner.
Starter PromptTurn this Jira status into a roadmap update for non-technical stakeholders.
Group by outcome or theme, not by ticket. For each theme: current status, what changed since last update, and why.
Flag any slipped timeline with a one-line reason. Jira export:
[PASTE]
WhyStakeholders do not want a list of tickets, they want to know if the bet is on track and what shifted. An update that speaks in outcomes makes you look in command of the plan rather than buried in it.
09
Stakeholder Digest
Phase 145 minStep 4: Iterate
- Task
- Turn a week of scattered Slack decisions and updates into a tight digest that each stakeholder group actually reads. One input, several framings, so nobody has to dig through channels.
- Collect the key threads, decisions, and updates from the week across channels
- Paste them in and name your stakeholder groups and what each one cares about
- Ask for one digest per group, reframed for their priorities
- Have it keep each digest to five lines with a clear so-what
- Save the prompt and send the digest every Friday
- Artifact
- Decision Digest email
- Ship
- A per-audience decision digest, five lines each, ready to send Friday.
Concept lockThe person who writes the record controls the memory.
Starter PromptTurn this week of Slack activity into stakeholder digests.
Groups and what they care about:
- [ENGINEERING]: [WHAT]
- [LEADERSHIP]: [WHAT]
- [SALES]: [WHAT]
For each group, write a 5-line digest reframed for their priorities, ending in a clear so-what. Activity:
[PASTE]
WhyDecisions that live buried in Slack threads get forgotten and relitigated, which costs you the same argument twice. A weekly digest makes you the source of record and quietly puts you at the centre of what the team knows.
Phase 2: Build the Platform
Build the Platform
Turn one-off wins into a small system that produces work for you.
10
Chain Two Tools
Phase 260 minStep 5: ReviewStretch
- Task
- Wire two tools into one flow where the output of a research pass becomes the input to a synthesis pass, with no manual reshaping between them. This is your first real system rather than a single clever prompt.
- Pick a recurring job that needs research then synthesis, such as a market brief
- Design a research prompt whose output is clean structured notes
- Design a second prompt that takes those notes and produces the final artifact
- Run the chain end to end and fix the handoff format until it flows
- Document the two prompts and the handoff shape as one named chain
- Artifact
- Research-to-Synthesis Chain prompt
- Ship
- A two-step chain where research output feeds synthesis with no manual cleanup between.
Concept lockThe value is in the handoff, not in either prompt alone.
Starter PromptStep 1 (research): Research [TOPIC] and output ONLY structured notes in this shape:
- Finding | Source | Confidence
Step 2 (synthesis): Take the notes from step 1 and produce a [DELIVERABLE] for [AUDIENCE], citing the findings inline.
Run step 1 now and hold the output for step 2.
WhySingle prompts save minutes, but chained prompts save whole workflows and are far harder for anyone to replicate. Learning to design the handoff between steps is the core skill that separates an AI user from an AI orchestrator.
Reflect before moving onWhere did the handoff format break, and how did you fix it? · What other two-step jobs could become a chain? · How much manual reshaping did the chain remove?
11
Reusable Prompt Library
Phase 255 minStep 1: Describe
- Task
- Consolidate every working prompt you have built so far into one organised library with a clear format, so nothing lives only in a chat history. This turns scattered wins into an asset you compound on.
- Collect the prompts from days three through ten into one place
- Define a standard entry: name, purpose, inputs, the prompt, and known limits
- Rewrite each prompt to that standard and remove one-off specifics
- Tag each by task type so you can find the right one in seconds
- Store it in Notion and add a rule to log every new prompt here
- Artifact
- Prompt Library doc
- Ship
- A Notion library holding every prompt so far in one consistent, searchable format.
Concept lockA prompt you cannot find again is a prompt you have to rebuild.
Starter PromptHelp me standardise this prompt into a library entry.
Return: Name, Purpose, Required inputs, The prompt (cleaned of one-off details), Known limits, Task-type tag.
Here is the raw prompt:
[PASTE]
WhyPrompts trapped in old chat windows are prompts you will rebuild from scratch, wasting the work you already did. A living library means each new prompt makes you faster forever, which is how the compounding actually happens.
12
Discovery Interview Analyzer
Phase 255 minStep 2: Generate
- Task
- Build a system that turns raw discovery interview transcripts into a structured insight table: the need, the underlying job, the pain, and a supporting quote. Run it on your last three interviews.
- Gather transcripts or detailed notes from recent discovery calls
- Paste one in and ask for a table of need, job-to-be-done, pain, and quote
- Have it flag where the user said one thing but implied another
- Run all three and ask the model to merge them into shared patterns
- Save the format as your standard post-interview analysis step
- Artifact
- Insight Table sheet
- Ship
- A merged insight table across three interviews with patterns and supporting quotes.
Concept lockThe insight is in the pattern across interviews, not any single one.
Starter PromptAnalyse this discovery interview transcript. Return a table with columns:
Need | Job-to-be-done | Pain | Supporting quote.
Add a row flagging any place the user said one thing but implied another.
Transcript:
[PASTE]
WhyDiscovery loses most of its value in the gap between the conversation and the write-up, where insight quietly evaporates. A consistent analyzer captures the signal every time and lets you spot patterns across interviews that no single call reveals.
13
Metrics Narrative Generator
Phase 250 minStep 3: Validate
- Task
- Turn a raw metrics dump into a narrative that explains what moved, why it likely moved, and what you would do about it. Numbers on their own do not persuade anyone.
- Export the current period metrics with prior-period comparisons
- Paste them in with context on any launches or events that could explain shifts
- Ask for a narrative: what moved, the likely driver, and a recommended action
- Have it separate real signal from noise and normal variance
- Save the format for your monthly business review prep
- Artifact
- Metrics Narrative doc
- Ship
- A metrics narrative that explains movements and recommends an action for each.
Concept lockNumbers report the past, narrative drives the decision.
Starter PromptTurn these metrics into a narrative for a business review. For each notable movement:
- What moved (metric and magnitude)
- Likely driver (given the context below)
- Recommended action
Mark anything within normal variance as noise, not signal.
Context: [LAUNCHES/EVENTS]. Metrics: [PASTE]
WhyA dashboard tells people what happened, but a narrative tells them what it means and what to do, which is where your value sits. Being the one who explains the numbers rather than just reports them is what gets you into the strategy conversation.
14
Prioritization Copilot
Phase 260 minStep 4: Iterate
- Task
- Build a copilot that scores a backlog against your real prioritization framework and explains each score, so you can defend the order in any room. Run it on your current backlog and stress-test the output.
- Write out the prioritization framework you actually use, criteria and weights
- Give the model the framework plus your current backlog items with context
- Ask it to score each item, show the maths, and rank the list
- Argue with three of its scores and see if it holds or folds correctly
- Save the framework and prompt so every backlog runs through it
- Artifact
- Prioritization Copilot sheet
- Ship
- A scored, ranked backlog with a transparent reason behind every score.
Concept lockA ranking you can explain is a ranking you can defend.
Starter PromptScore my backlog using this framework.
Criteria and weights: [LIST, e.g. Reach 30%, Impact 30%, Confidence 20%, Effort 20%].
For each item: show the score per criterion, the weighted total, and a one-line rationale. Then rank the list.
Backlog: [PASTE]
WhyPrioritization is where PMs are most often overruled, because a ranking without a rationale is just an opinion. A copilot that shows its working turns your backlog order into an argument you can win in front of anyone.
Reflect before moving onWhere did arguing with a score reveal a flaw in your framework? · Which item jumped or dropped once scored honestly? · Could you defend this order to your toughest stakeholder now?
Milestone — Day 14
Week 2: Systems, Not Tasks
- Prompt library stood up
- Two-tool chain running
- Prioritization copilot live
Level 2 in progress: AI-Integrated Professional
Week 3 converts systems into visible, funded value.
15
Spec-to-Ticket Pipeline
Phase 255 minStep 5: Review
- Task
- Build a pipeline that turns an approved spec into a clean set of engineering-ready tickets with acceptance criteria, ready to drop into Jira. This kills the tedious breakdown work that eats your afternoons.
- Take one approved spec and paste it in with your ticket conventions
- Ask the model to break it into tickets sized for a single engineer
- Require acceptance criteria and dependencies on every ticket
- Have it flag anything underspecified rather than inventing detail
- Review, adjust sizing, and paste the set straight into Jira
- Artifact
- Ticket Set checklist
- Ship
- A spec broken into engineering-ready tickets with acceptance criteria and dependencies.
Concept lockAutomate the breakdown, keep the judgment.
Starter PromptBreak this approved spec into engineering-ready tickets.
For each ticket: title, description, acceptance criteria (Given/When/Then), dependencies, and a rough size (S/M/L).
Flag anything underspecified as [NEEDS DETAIL] rather than guessing.
Spec: [PASTE]
WhyBreaking specs into tickets is high-volume, low-judgment work that quietly consumes your week. Automating the breakdown while keeping the judgment calls to yourself frees hours and gets engineers cleaner tickets than a rushed manual split ever would.
16
Release Notes Autowriter
Phase 245 minStep 1: Describe
- Task
- Generate customer-facing release notes straight from merged pull requests and commit messages, translated out of engineering language into user benefit. Run it on this release and save the flow.
- Pull the merged PRs and commit messages for the current release
- Paste them in with a note on who reads your release notes
- Ask for notes grouped by user-facing benefit, not by internal component
- Have it drop purely internal changes and flag anything needing a heads-up
- Save the prompt so every release turns notes around in minutes
- Artifact
- Release Notes doc
- Ship
- Customer-ready release notes generated from PRs, grouped by user benefit.
Concept lockShip the benefit, not the changelog.
Starter PromptWrite customer-facing release notes from these merged PRs and commits.
Audience: [END USERS / ADMINS]. Group by user-facing benefit, not by component.
Drop purely internal changes. Flag anything that needs a migration heads-up as [ACTION NEEDED].
Input: [PASTE PRS/COMMITS]
WhyRelease notes are usually written last, in a rush, and read like a changelog nobody wanted. Turning raw commits into clear benefits fast means your releases land with users instead of landing with a thud.
Phase 3: Make Yourself Revenue-Critical
Make Yourself Revenue-Critical
Convert the new practice into visible proof and a claim on the next seat.
17
Ship an AI-Native Workflow
Phase 360 minStep 2: GenerateStretch
- Task
- Package one of your automations into a shareable kit your team can run without you: the prompt, the inputs, and a short how-to. This is the moment your personal win becomes a team capability.
- Pick your most reliable automation from the past two weeks
- Write a plain-language how-to a teammate could follow cold
- Bundle the prompt, an example input, and an example output together
- Have a colleague run it once and note every point they got stuck
- Fix the friction and post the finished kit to a shared team channel
- Artifact
- Team Workflow Kit doc
- Ship
- A self-serve workflow kit a teammate ran successfully without your help.
Concept lockA workflow others can run is leverage, one only you can run is a hobby.
Starter PromptTurn this automation into a self-serve kit for a teammate who has never used it.
Produce: a plain-language how-to (numbered steps), the prompt itself, one example input, and the matching example output.
Call out the two places people are most likely to get stuck.
Automation: [PASTE]
WhyAn automation only you can run is a personal trick, but one your team can run is leverage that spreads your name with it. Shipping the first kit marks your shift from doing the work faster to changing how the team works.
18
Quantify Hours Reclaimed
Phase 350 minStep 3: Validate
- Task
- Build a simple model that estimates the hours you and your team have reclaimed from the automations shipped so far, with defensible assumptions. Numbers turn a good story into a case leadership can act on.
- List every automation you have shipped and who now uses it
- Estimate the manual time each task took before and takes now
- Multiply by frequency and headcount to get monthly hours saved
- Keep the assumptions visible and deliberately conservative
- Convert the hours into a rough cost figure using a blended rate
- Artifact
- Time-Savings Model sheet
- Ship
- A conservative model showing monthly hours and cost reclaimed, with assumptions shown.
Concept lockUnmeasured impact is unrewarded impact.
Starter PromptBuild a time-savings model from these automations.
For each: task, before-minutes, after-minutes, frequency per month, number of users.
Compute monthly hours saved and, using a blended rate of [RATE], a monthly cost saved.
Keep assumptions explicit and conservative. Data: [PASTE]
WhyImpact that is not measured is impact that is not rewarded, and vague claims of saving time convince nobody. A defensible model turns your month of work into a number leadership can put in a plan, which is what gets you the next mandate.
19
Present the ROI to Leadership
Phase 360 minStep 4: IterateStretch
- Task
- Turn your time-savings model into a short, sharp deck that makes the case for expanding this work, aimed at the people who control mandates and budget. This is where you convert effort into permission.
- Start from your quantified hours and cost reclaimed
- Structure the deck as problem, what you did, result, and the ask
- Ask the model to draft speaker notes that anticipate the skeptical question
- Cut every slide that does not move the decision forward
- Rehearse once against the notes and tighten the ask
- Artifact
- ROI Deck slide
- Ship
- A tight leadership deck ending in a specific ask backed by your numbers.
Concept lockInvisible results change nothing, presented results change your mandate.
Starter PromptDraft a 6-slide leadership deck from this ROI model.
Structure: Problem, What I did, Result (the numbers), The ask, Risks, Next steps.
For each slide give a headline and 3 bullet points, plus speaker notes that pre-empt the most skeptical question.
Model: [PASTE]
WhyA result nobody with power sees is a result that changes nothing about your trajectory. Presenting the ROI well is how a month of quiet automation becomes visible leverage and an explicit mandate to do more.
20
Train One Peer
Phase 355 minStep 5: Review
- Task
- Teach one colleague to run the method on their own work, and capture what you taught as a reusable training note. Teaching forces you to make your own process explicit and multiplies its reach.
- Choose a peer whose work has an obvious automatable task
- Sit with them and map that task the way you mapped yours on day one
- Build one automation together, letting them drive the prompting
- Write up what you covered as a short training note others can follow
- Ask them to teach the next person, and note where they struggled
- Artifact
- Peer Training Note doc
- Ship
- One trained peer with a working automation, plus a reusable training note.
Concept lockYou lead the shift by creating others who can run it.
Starter PromptHelp me write a short training note from this session, where I taught a peer to automate [TASK].
Include: the task we mapped, the automation we built, the prompt used, and the two things they found hardest.
Write it so a third person could follow it without me. Notes: [PASTE]
WhyThe fastest way to be seen as a leader in this shift is to visibly create other people who can do it. A training note turns a single mentoring session into something that scales past the two of you.
21
Propose an AI-Product Initiative
Phase 360 minStep 1: Describe
- Task
- Draft a concrete proposal for an AI-powered feature or internal capability that only someone who has lived this month could credibly pitch. This moves you from automating your job to shaping the product.
- Look across your themed feedback and metrics for an AI-shaped opportunity
- Frame the problem, the proposed capability, and who it serves
- Ask the model to pressure-test the idea for feasibility and obvious risks
- Add a small, cheap first experiment to prove or kill it fast
- Write it as a one-page proposal aimed at a decision-maker
- Artifact
- Initiative Proposal doc
- Ship
- A one-page proposal for an AI initiative with a cheap first experiment defined.
Concept lockAutomating your job makes you efficient, directing AI in the product makes you strategic.
Starter PromptHelp me shape an AI-product initiative proposal.
Problem: [STATE, grounded in real feedback/metrics]. Proposed capability: [DESCRIBE]. Who it serves: [WHO].
Pressure-test feasibility and name the top 3 risks. Then design the cheapest experiment that could prove or kill it in 2 weeks.
Return a one-page proposal.
WhyAutomating your own tasks makes you efficient, but proposing where AI should go in the product makes you strategic. This is the day you stop being someone AI could replace and become someone deciding how AI gets used.
Reflect before moving onCould only someone who lived this month have written this proposal? · What is the cheapest way to prove the idea is wrong? · Who needs to see this, and what is your ask of them?
Milestone — Day 21
Week 3: Revenue-Critical
- ROI presented to leadership
- One peer trained
- AI initiative proposed
Level 2 confirmed: AI-Integrated Professional
Week 4 makes the practice permanent and delegable.
22
Draft the Business Case
Phase 355 minStep 2: Generate
- Task
- Expand your initiative proposal into a business case with costs, expected value, and a phased plan a decision-maker can fund. This is the document that separates an idea from a mandate.
- Start from your one-page initiative proposal
- Estimate build cost, ongoing cost, and the value in the same units
- Lay out a phased plan where phase one is small and reversible
- Ask the model to stress-test the numbers and flag the weakest assumption
- Tighten it into a fundable case with a clear recommendation
- Artifact
- Business Case doc
- Ship
- A phased business case with costs, value, and a clear funding recommendation.
Concept lockIdeas get admired, business cases get funded.
Starter PromptTurn this initiative proposal into a business case.
Include: build cost, ongoing cost, expected value (same units), a 3-phase plan with a small reversible phase 1, and a clear recommendation.
Stress-test the numbers and flag the single weakest assumption.
Proposal: [PASTE]
WhyIdeas do not get funded, business cases do, and the ability to write one is what pulls you toward the decision table. Framing your initiative in the language of cost and value is how you get taken seriously by the people who allocate resources.
23
Claim the Next Seat
Phase 350 minStep 3: ValidateStretch
- Task
- Write a sharp repositioning brief that redefines your role around AI orchestration, backed by the evidence you have built this month. This is you naming the seat you intend to take before anyone offers it.
- Gather the proof: automations shipped, hours saved, peers trained, the initiative
- Draft how your role should be described a year from now
- Ask the model to contrast your current scope with the target scope
- Have it surface the gap you need to close and how to close it
- Turn it into a brief you can use in a career conversation
- Artifact
- Repositioning Brief doc
- Ship
- A repositioning brief that redefines your role around AI orchestration, backed by evidence.
Concept lockThe seat goes to whoever describes it first.
Starter PromptHelp me write a repositioning brief.
Evidence from the last 30 days: [AUTOMATIONS, HOURS SAVED, PEERS TRAINED, INITIATIVE].
Contrast my current role scope with a target scope built around AI orchestration.
Name the gap between them and the concrete moves to close it. Write it for a career conversation.
WhyIn a shift this fast, roles get redefined by the people who define them first, not the ones who wait to be told. A clear brief means you walk into your next career conversation with the new seat already described and half-earned.
Phase 4: Institutionalize
Institutionalize
Make the practice permanent — documented, delegated, and yours.
24
Document Your Playbook
Phase 460 minStep 4: Iterate
- Task
- Consolidate everything you built this month into a single AI playbook that captures your method, your prompts, and your standards in one place. This is the asset that outlives any single automation.
- Gather your prompt library, your chains, and your kits into one outline
- Write the method as principles, not just a list of tools
- Add a standards section: when to automate, when not to, quality bars
- Have the model tighten the structure and flag any gaps in your logic
- Publish it in Notion as the living reference for you and your team
- Artifact
- AI Playbook doc
- Ship
- A single published playbook holding your method, prompts, and standards.
Concept lockA documented method is a system, an undocumented one is a streak.
Starter PromptHelp me structure my AI playbook. I have: [PROMPT LIBRARY], [CHAINS], [KITS].
Produce an outline with: my method as principles, a standards section (when to automate vs not, quality bars), and a prompt index.
Flag any gaps where my logic is thin. Then tighten the structure.
WhyScattered wins are fragile, but a documented playbook is a durable asset that keeps paying out after the novelty fades. Writing down your method is also what makes it teachable, which is the difference between a skill and a system.
25
Delegate a Workflow
Phase 450 minStep 5: Review
- Task
- Fully hand off one of your automations to someone else, so it runs without you at all. Real leverage is not doing the task fast, it is no longer being in the loop.
- Pick a stable automation that does not need your judgment to run
- Write a handover that covers inputs, the run, the checks, and failure modes
- Walk the new owner through one full run start to finish
- Have them run the next one solo while you only watch
- Remove yourself from the loop and note where they still need backup
- Artifact
- Delegation Handover doc
- Ship
- One automation running under a new owner with you fully out of the loop.
Concept lockLeverage is not doing it fast, it is no longer being in the loop.
Starter PromptWrite a delegation handover for this automation so someone can own it without me.
Cover: required inputs, how to run it, what good output looks like, common failure modes, and what to do when it breaks.
Write it for the specific person: [ROLE/SKILL LEVEL]. Automation: [PASTE]
WhyIf every automation still routes through you, you have built a bottleneck instead of leverage. Delegating cleanly is what frees your attention for the higher work and proves your method survives without you.
26
Weekly AI Review Ritual
Phase 440 minStep 1: Describe
- Task
- Design a lightweight weekly ritual that keeps your automations healthy and surfaces the next thing to automate. A system without maintenance quietly rots, so you build the upkeep in.
- List the checks that keep your automations trustworthy over time
- Draft a 15-minute weekly agenda: what to review, retire, and add
- Include a slot to capture one new automation candidate each week
- Add a quality check for any prompt whose output has drifted
- Put a recurring block on your calendar and commit to the first one
- Artifact
- Review Ritual checklist
- Ship
- A 15-minute weekly review ritual booked as a recurring calendar block.
Concept lockAn unmaintained system is a system quietly failing.
Starter PromptDesign a 15-minute weekly AI review ritual for me as a PM.
Include: checks to confirm automations still work, criteria to retire dead ones, a slot to capture one new candidate, and a prompt-drift check.
Return it as a tight checklist I can run every Friday.
WhyAutomations degrade as tools, data, and needs change, and an unmaintained system slowly loses the trust you built. A short standing ritual keeps the whole method alive and ensures your automation backlog never runs dry.
27
Team Prompt Registry
Phase 455 minStep 2: GenerateStretch
- Task
- Turn your personal prompt library into a shared team registry with contribution rules, so the whole team compounds instead of just you. This is where your individual method becomes team infrastructure.
- Adapt your prompt library structure for shared team use
- Define contribution rules: format, review, and who owns quality
- Seed it with your best prompts, generalised past your own context
- Add a simple tagging system so people find prompts by task
- Announce it and ask each teammate to contribute one prompt this week
- Artifact
- Team Prompt Registry doc
- Ship
- A shared prompt registry seeded with your best prompts and open for contribution.
Concept lockPersonal tools help you, shared tools make you the standard.
Starter PromptHelp me convert my personal prompt library into a shared team registry.
Define: a contribution format, a light review rule, an owner for quality, and a tagging scheme by task type.
Then generalise these 3 of my prompts to remove my personal context: [PASTE].
WhyA personal library helps one person, but a team registry makes every colleague faster and quietly positions you as the person who built the standard. Shared infrastructure is how a private habit becomes something the whole team runs on.
28
Measure Team Adoption
Phase 445 minStep 3: Validate
- Task
- Build a snapshot of how widely the team is actually using the tools and prompts you introduced, so you can see what stuck and what needs a push. Adoption is the real measure of whether you changed anything.
- List the tools, kits, and prompts you have put in front of the team
- For each, gather a rough measure of who uses it and how often
- Ask the model to turn it into an adoption snapshot with obvious gaps
- Flag the one high-value tool with the lowest uptake
- Draft a single targeted nudge to lift that one tool next week
- Artifact
- Adoption Snapshot sheet
- Ship
- An adoption snapshot showing usage per tool and the one gap worth closing.
Concept lockIntroducing a tool is easy, adoption is the only proof it mattered.
Starter PromptBuild an adoption snapshot from this data.
For each tool/kit/prompt: rough number of users, frequency, and a stuck/growing tag.
Highlight the highest-value item with the lowest adoption, and draft one targeted nudge to lift it.
Data: [PASTE]
WhyIntroducing tools is easy, but only adoption proves you shifted how the team works rather than just adding noise. Measuring it tells you where to push and gives you the evidence that your influence is real and spreading.
29
Write Your New Role Description
Phase 450 minStep 4: IterateStretch
- Task
- Write the honest, forward-looking description of the role you now occupy, one that would not have existed thirty days ago. This is you defining your value in a language the org has not caught up to yet.
- Pull together the playbook, the registry, the initiative, and the adoption data
- Draft a role description centred on orchestration, systems, and judgment
- Ask the model to strip out anything AI could do and keep only what needs you
- Contrast it plainly with the role description you started the month with
- Refine it into something you would put your name to in a review
- Artifact
- New Role Description doc
- Ship
- A role description built on orchestration and judgment that did not exist 30 days ago.
Concept lockDefine your value before the org defines a lesser version of it.
Starter PromptHelp me write my new role description after 30 days of AI work.
Evidence: [PLAYBOOK, REGISTRY, INITIATIVE, ADOPTION].
Centre it on orchestration, systems, and judgment. Strip out anything AI could do alone, keep only what genuinely needs a human.
Contrast it with my old role description: [PASTE].
WhyThe safest position in this shift is a role defined by what only a human orchestrator can do, and no one is going to write that description for you. Naming it yourself is how you make your value legible before the org invents a lesser version of it.
30
The 30-Day Review and Next 60
Phase 460 minStep 5: Review
- Task
- Run a full review of the thirty days, honestly assessing what changed in your work and your standing, then chart the next sixty. The month was the proof of concept, the next quarter is where it compounds.
- Gather every artifact and metric from the month in one place
- Ask the model to assess your move from AI user to orchestrator against the evidence
- Name what genuinely changed and what was only motion, honestly
- Set three concrete goals for the next sixty days that build on this
- Write the review as a document you would revisit in two months
- Artifact
- 30-Day Review doc
- Ship
- An honest 30-day review with three concrete goals for the next sixty days.
Concept lockA month only compounds if you name what it earned and where it points.
Starter PromptHelp me run a 30-day review. Evidence: [ARTIFACTS, HOURS SAVED, PEERS TRAINED, INITIATIVE, ADOPTION].
Assess honestly: did I move from AI user toward AI orchestrator? What genuinely changed vs what was just motion?
Then set 3 concrete goals for the next 60 days that build on this. Write it as a document I would revisit.
WhyA month of work only compounds if you stop to name what it earned you and decide where it points next. This review turns thirty days of scattered wins into a clear trajectory, so the momentum outlives the challenge that created it.
Reflect before moving onWhat genuinely changed in your work versus what was only motion? · Which single artifact from this month will still matter in a year? · What is the one thing you must not let slide over the next sixty days?
Milestone — Day 30
Protocol Complete: AI Orchestrator
- Playbook documented
- A workflow delegated
- New role described and claimed
Level 3 reached: AI Orchestrator
The next 60 days scale the practice across the team.
Executive Summary · Premium
Situation Analysis & Transformation Roadmap
Situation.Your role sits on the contested frontier. The reporting, research, and drafting half of your week is highly automatable; the strategy, framing, and stakeholder half is defensible and appreciating in value.
The bet.Over 30 days you hand the exposed half to AI you operate, building reusable systems rather than one-off tricks. You then convert the reclaimed hours into visible proof and a funded initiative you own.
Outcome.By Day 30 you have a running set of AI workflows, a documented playbook, a quantified ROI, and a live claim on the next seat up — moving from AI-Aware to AI Orchestrator.
Priority Matrix · Premium
What to fight for first
Impact against effort. Do the top-left now; schedule the heavy bets; bank the quick wins; park the rest.
Do First (High Impact, Low Effort)
- Automate the weekly status report
- Templatize competitor and market scans
- Stand up a reusable prompt library
Schedule (High Impact, High Effort)
- Build the research→synthesis chain
- Stand up the prioritization copilot
- Draft the AI-initiative business case
Quick Wins (Low Impact, Low Effort)
- Meeting-prep prompt
- Release-notes autowriter
- Stakeholder decision digest
Park (Low Impact, High Effort)
- Full roadmap-automation platform (revisit next quarter)
- Custom fine-tuned model (not yet worth the effort)
Back Matter
Artifact Index
Artifacts produced across 30 days.
| Day | Artifact | Description |
| 01 |
Weekly Task Audit |
Tagged sheet of every recurring task by exposure |
| 03 |
Status Report Prompt |
Reusable one-pass status report generator |
| 05 |
Competitor Brief Workflow |
15-minute repeatable scan |
| 10 |
Research→Synthesis Chain |
Two-tool end-to-end workflow |
| 11 |
Prompt Library |
Eight standardized reusable prompts |
| 14 |
Prioritization Copilot |
Re-runnable scored backlog |
| 18 |
Time-Savings Model |
Defensible hours + currency reclaimed |
| 19 |
ROI Deck |
Five-slide leadership story |
| 21 |
Initiative Proposal |
One-page AI initiative you own |
| 24 |
AI Playbook |
Documented systems a new hire could run |
| 30 |
30-Day Review |
Review + concrete 60-day plan |
Back Matter
Reference
Concept Lock Index
| Day | Concept | Key Takeaway |
| 01 |
Per-Task Exposure |
Score the work, not the title. |
| 04 |
Prompt Architecture |
Role, Context, Task, Format, Constraints. |
| 10 |
Workflows vs Prompts |
A workflow is a saved chain, not one clever prompt. |
| 14 |
AI Ranks, You Decide |
Keep the override and the reason. |
| 18 |
Quantify or Forfeit |
Impact you can't measure, you can't claim. |
| 24 |
Documented Leverage |
Undocumented leverage leaves when you do. |
| 26 |
Ritualize Compounding |
Momentum needs a recurring container. |
Recommended tools & resources
| Resource | Context | Link |
| ChatGPT Plus |
Primary generation + reporting workflows |
https://chat.openai.com/ |
| Claude |
Synthesis, long-context analysis, playbook drafting |
https://claude.ai/ |
| Perplexity |
Sourced competitor and market scans |
https://www.perplexity.ai/ |
| Notion AI |
Prompt library + team registry |
https://www.notion.so/product/ai |
| EU AI Act — Article 50 |
Transparency obligations reference |
https://artificialintelligenceact.eu/ |
This plan was generated by Claude (Anthropic) from your quiz responses.
It complies with EU AI Act Article 50 on AI transparency.
It is based on your self-reported role, tasks, and goals.
Scores are modeled estimates, not predictions of individual outcomes.
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AI Irreplaceability Protocol · 30-Day Plan

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