aaron

Smart question. Converting messy meeting notes straight into Jira/Trello cards is exactly where AI pays off fast.

5‑minute quick win: Paste your notes into an AI with the prompt below and ask for “one Trello card per line.” Copy the lines and paste them into Trello’s “Add a card” field—Trello will auto-create multiple cards at once. You’ll go from notes to actionable cards in under five minutes.

The problem: Notes live in Google Docs, email, or a calendar description. Tasks die there. Manually re-typing into Jira/Trello is slow, inconsistent, and error-prone.

Why it matters: If you run 3–5 meetings a week, you’re leaking hours and momentum. Automating capture and standardizing quality (owner, due date, acceptance criteria) lifts throughput and reduces follow-up churn.

Lesson from the field: The win isn’t “AI magic.” It’s a tight template, a guardrailed prompt, and a simple handoff: notes → structured tasks → cards/issues. Add a review gate, and you get 70–90% automation with control.

1. What you’ll need
   • Access to Trello and/or Jira.
   • An AI assistant (any GPT-class tool).
   • Optional for full automation: Zapier/Make, and a “Meeting Notes” folder in Google Drive or Notion.
2. Quick Win: Trello multiline paste (no integrations)
   1. Open your meeting notes.
   2. Use the prompt below to convert notes into card lines.
   3. Copy the output, go to Trello, click “Add a card,” paste. Trello creates one card per line.
   4. Drag to lists, assign, and set due dates (30–60 seconds).

   Copy‑paste AI prompt (Trello lines):

   From the notes below, extract a maximum of 8 action items. Return one line per card, no extra commentary. Format exactly:Title — Description. Acceptance Criteria: [3 bullets]. Labels: [comma-separated]. Owner: [name]. Due: [YYYY-MM-DD].Assume 5 business days if no due date. Use short, verb-first titles. If no clear owner, write Owner: Unassigned. Notes:

3. 30–60 min Automation: Notes → AI parse → Jira/Trello
   1. Trigger: When a new doc is added to a “Meeting Notes” folder OR a meeting ends (calendar/recording app dumps notes).
   2. AI step: Parse notes into a strict JSON array of tasks.
   3. Routing: If board/project keywords appear (e.g., “Marketing,” “Platform”), route to the correct Trello list or Jira project. Use a lookup table for assignee name → email/ID.
   4. Create step: For each task, create a Trello card or Jira issue. For Jira, map: Summary, Description, Labels, Priority, Due date, Assignee, Issue type, Acceptance Criteria (into Description), optional Story Points.
   5. Safety: Send to a “Staging” list or a Jira label like “to_review” for a quick human scan before moving to live boards.

   Copy‑paste AI prompt (Structured JSON for Jira/Trello via Zapier/Make):

   You are extracting tasks from meeting notes for project management. Return ONLY valid JSON (UTF-8), an array of tasks. Max 12 tasks. Use this schema exactly:[{“summary”:””, “description”:””, “acceptance_criteria”:[“”,””,””], “labels”:[“”], “priority”:”Low|Medium|High”, “assignee_name”:””, “assignee_email”:””, “due_date”:”YYYY-MM-DD”, “platform”:”Jira|Trello”, “jira_project_key”:””, “jira_issue_type”:”Task|Bug|Story”, “trello_list”:”” }]Rules: 1) Summaries start with a verb and ≤ 10 words. 2) Description: 2–4 sentences; include context. 3) Always provide 3 acceptance criteria (Given/When/Then style). 4) If no owner, set assignee_name:”Unassigned” and leave assignee_email empty. 5) If no due date, set to 5 business days from today. 6) Use labels of at most 3 items. 7) Route by keywords inside notes: “Jira” → platform Jira, else Trello. 8) No additional text outside JSON. Notes:

4. Jira specifics that save rework
   • Keep issue type defaulting to Task unless the note clearly indicates Bug or Story.
   • Put acceptance criteria under a heading in the Description so it’s visible in the ticket.
   • Use a tight label set (e.g., meeting, followup, quickwin) to avoid label sprawl.
   • If your org uses Story Points, add them in a separate step or require an estimate comment for a human to set points later.

What to expect

• First run: 70–90% of tasks are “good enough.” A 60–120 second review catches the rest.
• After a week of tweaks (prompts + routing rules), you’ll cut manual entry by 80%+.

Metrics to track

• Time from meeting end to cards/issues created (target: under 10 minutes).
• % of items with owner + due date + acceptance criteria (target: 90%+).
• Edits per card/issue within 24 hours (target: under 1.0 average).
• Throughput: cards/issues created per meeting that reach “Done” in 14 days.
• Drop rate: tasks created but untouched after 72 hours (target: under 10%).

Common mistakes and quick fixes

• Vague tasks. Fix: Force acceptance criteria and verb-first titles in the prompt.
• Too many cards. Fix: Cap items (8–12) and group stray notes under one follow-up card.
• Wrong board/project. Fix: Add keyword routing and a default staging list/label.
• Owner mismatches. Fix: Maintain a simple name→email/ID lookup and fall back to Unassigned.
• Unstructured AI output. Fix: Demand “ONLY valid JSON” and test with malformed inputs.

1‑week rollout

1. Day 1: Use the Trello multiline paste quick win in one meeting. Measure minutes saved.
2. Day 2: Finalize your prompts. List 5–7 standard labels. Build the assignee lookup table.
3. Day 3: Build the Zap/Make flow for Trello. Add a Staging list.
4. Day 4: Clone the flow for Jira. Map required fields. Test with 3 sample notes.
5. Day 5: Pilot with a real meeting. Time from meeting end to cards/issues created.
6. Day 6: Review errors, tighten prompts, adjust routing and caps.
7. Day 7: Roll out to one team. Schedule a 2-week check-in to review KPIs.

Bottom line: Yes—AI can take notes and create Jira/Trello work items reliably when you constrain the output and keep a quick review step. Start with the 5‑minute Trello trick, then graduate to a simple Zap that does it every time.

Your move.

— Aaron

5‑minute quick win: Ask AI to write a tiny Python script that reads a local HTML file (no internet scraping) and extracts product names and prices. It proves the end-to-end flow fast and builds confidence.

Copy this AI prompt and run it in your favorite AI assistant:

“Write a beginner-friendly Python 3 script that reads a local file named sample.html and extracts each product name and price. The HTML has repeated div class=”product” blocks with h2 for name and span class=”price” for price. Output a CSV products.csv with columns name, price. Include: exact pip install commands, how to run the script, basic error handling, and 10 lines of clear comments. Do not fetch any URLs.”

The problem You copy-paste data from websites. It’s slow, inconsistent, and doesn’t scale.

Why it matters AI can generate 80% of the code to collect and clean web data—turning hours of manual effort into a repeatable workflow. Used correctly (and legally), this becomes a dependable input to research, prospecting, pricing checks, and competitive tracking.

What I’ve seen work Non-technical teams win when they write a clear “scrape spec” and let AI generate modular, well-commented code. Respect site rules, start with public pages or your own properties, and iterate on selectors—not on guesswork.

What you’ll need

• Python 3.10+ and a terminal (Mac/Windows works).
• Permission to collect the target data. Confirm the site’s terms and robots.txt allow it. If there’s an official API, use that first.
• 1 text file listing URLs you’re allowed to fetch (urls.txt).

Step-by-step (beginner-friendly)

1. Pick a target you’re allowed to use. Start with your own site or a public page that permits automated access. Avoid logins, paywalls, and anything disallowed in terms/robots.txt.
2. Create your scrape spec. Write 6 bullets: Purpose, Allowed URLs/patterns, Fields to extract (with examples), Output format (CSV/JSON), Politeness (1 request every 2–3 seconds, custom User-Agent), Stop conditions (any 403/429).
3. Use this robust AI prompt template to generate code.“You are a senior Python engineer. Generate a script to collect allowed public data from pages listed in urls.txt. Requirements: (1) obey robots.txt and only fetch provided URLs; (2) 1 request every 2 seconds; (3) set a clear User-Agent string; (4) extract the following fields: [list your fields and CSS selectors or examples]; (5) output to data.csv (UTF-8); (6) log successes and errors to run.log; (7) stop on HTTP 403/429 and print a helpful message; (8) handle missing fields gracefully; (9) keep functions small: fetch_url, parse_html, write_row; (10) include install commands (pip) and run instructions; (11) include 10 short tests that parse two sample HTML snippets without network; (12) no login or paywall pages; (13) minimal dependencies: requests, beautifulsoup4, pandas.”
4. Install and run. The AI will output pip commands (e.g., pip install requests beautifulsoup4 pandas). Create urls.txt with 2–3 allowed test URLs and run the script as instructed.
5. Validate the output. Open data.csv. Spot-check 10 rows against the source pages. If fields are off, use your browser’s “Inspect Element” to copy exact CSS selectors, then update the spec and regenerate the parser function via AI.
6. Harden the script. Add: retries with backoff, a sleep between requests, logging, and a “canary URL” you know should always work. If the site is JavaScript-rendered, ask AI for a Playwright-based version, still within the same permissions and politeness rules.
7. Store and schedule. Save outputs with a date stamp (e.g., data_YYYYMMDD.csv). Use your OS Task Scheduler or cron for weekly runs. Start weekly; only increase frequency if permitted and needed.

Insider tips

• Check the page’s HTML for “application/ld+json” (JSON-LD). It often contains clean, structured data you can parse directly.
• Use the site’s sitemap if available to discover pages efficiently and ethically. Filter it down to your allowed scope before fetching.
• Selectors break. Anchor them to stable attributes (data-*) rather than decorative classes.

Expected outcomes In 1–3 hours, you’ll have a stable script capturing the fields you care about with a clear log and a repeatable run process.

KPIs to track

• Fetch success rate = successful pages / total pages.
• Parse accuracy = correctly extracted fields on a 20-row spot-check.
• Error rate = errors per 100 requests (target under 5%).
• Throughput = pages per minute within your politeness settings.
• Duplicates = duplicate keys per run (target zero; add de-dup logic by URL or unique ID).
• Time saved = minutes compared to manual copy/paste for the same rows.

Common mistakes and quick fixes

• Scraping disallowed pages → Fix: read terms and robots.txt; restrict to permitted pages; prefer the site’s API.
• Fragile selectors → Fix: re-select using unique attributes; reduce depth; keep a selector map in one place.
• No backoff → Fix: add time.sleep and capped retries; stop on 403/429 and review limits.
• Unstructured output → Fix: define a schema (field names, types) and validate before writing.
• No logs → Fix: write run.log with timestamps and error messages; keep it for audits.
• Dynamic pages with static fetch → Fix: switch to Playwright for permitted pages, then parse the rendered HTML.

1‑week action plan

• Day 1: Select a permitted target, write your 6-bullet scrape spec.
• Day 2: Use the robust prompt to generate your first Python script. Install dependencies. Dry run against 2–3 pages.
• Day 3: Tighten selectors. Add CSV schema and logging. Validate 20 rows.
• Day 4: Add retries, polite delays, and stop conditions. Introduce a canary URL.
• Day 5: Automate scheduling (weekly). Version your script and prompt.
• Day 6: Set up a lightweight KPI sheet (success rate, accuracy, errors, time saved). Aim for >90% success and <5% errors.
• Day 7: Review results, document maintenance steps, and decide whether to expand fields or frequency.

One more ready-to-use AI prompt (for dynamic pages you’re allowed to access)

“Generate a Python script using Playwright to open each URL in urls.txt, wait for the selector [your stable selector], and extract fields [list]. Requirements: headless true, 1 page at a time, wait-for-timeout 2–4 seconds, respect robots.txt and site terms, set a clear User-Agent, output to CSV, log to run.log, stop on 403/429, include install and run instructions, and 10 concise comments for a non-technical user.”

Keep it legal, keep it polite, and treat your script like a product: clear spec, versioned prompt, measurable results. That’s how you turn AI-generated code into a dependable data asset.

Your move.

Good focus: zeroing in on AI-powered smart reminders is the right lever to reduce appointment no-shows — it moves the problem from human follow-up to automated, personalized nudges that scale.

The problem: No-shows cost time and revenue, disrupt schedules and lower utilization.

Why it matters: Even a 20% reduction in no-shows increases billable appointment capacity and improves patient flow without hiring staff. That’s direct margin improvement.

Quick lesson: Personalization + timing + two-way communication delivers most of the impact. The AI piece is practical: it generates context-aware, empathy-driven messages and decides optimal send times based on engagement patterns.

1. Assess (what you’ll need)
   • Baseline data: current no-show rate, confirmation rate, avg revenue per appointment.
   • Patient contact data (phone, email), appointment metadata (type, prep steps), calendar/booking system access.
   • Messaging channel: SMS is highest impact; email + voicemail optional. Choose a provider that supports two-way replies and API integrations.
2. Build (how to do it)
   1. Segment appointments by risk (new patient, high-value, long gaps since booking).
   2. Create message templates with variables: name, date/time, location, prep, link to reschedule, and a clear CTA (Confirm/Cancel/Reschedule).
   3. Use an AI prompt to generate tone-appropriate variants and subject lines. Schedule reminders at multiple touchpoints: immediate booking, 7 days, 48 hours, 24 hours, and 2 hours where appropriate.
   4. Enable two-way replies and automated rescheduling links or agent handover rules.
3. Test & iterate (what to expect)
   • Start with a 100–200 appointment pilot. Expect an initial bump in confirmations and a measurable drop in no-shows within 2–4 weeks.
   • Use A/B tests on message tone and send timing to optimize.

Copy-paste AI prompt (use with your LLM or reminder platform):

“Write 5 reminder message variants for a medical appointment that are personalized, concise, and include: recipient’s first name, appointment date/time, location, one-sentence prep instruction, a short reschedule link placeholder [RESCHEDULE_LINK], and a clear CTA to reply YES to confirm or NO to cancel. Produce 2 short (SMS-style), 2 medium (SMS/email), and 1 empathetic variant for older patients. Keep language warm, respectful, and direct.”

Prompt variants:

• Short/SMS: “Generate 3 ultra-short SMS confirmations for dental cleaning that ask for a one-word reply (YES/NO) and include [RESCHEDULE_LINK].”
• Formal/Clinical: “Create 3 formal appointment notices with prep instructions tailored for pre-op patients, include fasting instructions, and a contact number for questions.”

Metrics to track

• No-show rate (baseline vs. weekly)
• Confirmation rate (replies or clicks)
• Reschedule rate and same-week fill rate
• Revenue recovered / additional appointments filled
• Cost per reminder and ROI

Common mistakes & fixes

• Over-messaging → Fix: cap to 4 touchpoints and use behavior-based pause rules after confirmation.
• Generic templates → Fix: add three personalization tokens and use AI to vary tone.
• No two-way flow → Fix: enable replies and auto-reschedule links or quick human handoff.
• Poor timing → Fix: test send windows; move messages to times with higher reply rates.

1-week action plan

1. Day 1: Pull baseline metrics and segment appointments.
2. Day 2: Choose messaging channel/provider and confirm two-way capability.
3. Day 3: Draft templates using the AI prompt above; create 6–8 variants.
4. Day 4: Configure scheduling (booking system + messaging integration) and set touchpoints.
5. Day 5: Run a 100-appointment pilot for 7 days.
6. Day 6: Review confirmation/no-show numbers; run A/B test if sample allows.
7. Day 7: Implement fixes and scale to full schedule if KPIs are positive.

Your move.

Great question. Yes—AI can auto-categorize and tag support tickets for small teams, reliably, without a big IT lift. Here’s how to do it so the results are measurable and the rollout is low-risk.

The problem: Support inboxes mix billing, bugs, how-to questions, and urgent outages. Humans triage inconsistently, reporting gets noisy, and time-to-first-response drifts.

Why it matters: Clean, consistent tags power faster routing, accurate dashboards, and smarter staffing. For small teams, shaving 2–5 minutes of triage per ticket is material.

What I’ve seen work: Keep the category list short (6–10), use a two-pass approach (rules then AI), set confidence thresholds, and let AI tag 70–85% of tickets with high precision while humans review the rest.

• Do: Cap top-level categories at 10. Add tags for nuance.
• Do: Define each category in one sentence plus 2–3 examples.
• Do: Use a confidence threshold (e.g., 0.70) and auto-route only when above it.
• Do: Add a few keyword “guardrails” (e.g., refund, outage) before AI classification.
• Do: Review 50 tickets weekly to refine the taxonomy.
• Don’t: Start with 30+ categories. You’ll tank accuracy and trust.
• Don’t: Let AI guess when uncertain—send to “General triage.”
• Don’t: Mix bug reports and feature requests under one bucket.

What you’ll need:

• A helpdesk or inbox (e.g., Zendesk, Help Scout, Intercom, Freshdesk, Front, or Gmail).
• An automation layer (native triggers/webhooks or a connector like Zapier/Make).
• An LLM endpoint (e.g., GPT-4 class model). Use subject + first ~500 characters of body.
• 200–500 recent tickets exported for testing.
• A draft taxonomy (6–10 categories, 10–25 tags).

Step-by-step:

1. Define taxonomy: 6–10 categories such as Billing, Login/Access, Bug Report, Feature Request, Shipping/Delivery, How-To/Usage, Account Changes, Urgent/Outage.
2. Write category rules: One-sentence definition + 2 examples per category. Keep a shared doc.
3. Collect samples: 25 tickets per category. Note the correct category and tags.
4. Set rules first: If subject/body contains strong keywords (e.g., “refund,” “cancel,” “can’t log in”), apply those tags immediately and skip AI.
5. Build the classifier prompt (below). Require JSON, include your categories, and ask for a confidence score and reason.
6. Offline test: Run 100–200 historical tickets through the prompt. Target ≥85% precision on auto-routed tickets at confidence ≥0.70.
7. Wire automation: On new ticket created → apply keyword guardrails → call AI → if confidence ≥0.70, set category/tags and route; else set “General triage.”
8. Human-in-the-loop: Add an “AI-suggested” note so agents can accept/edit. Log edits to improve prompts.
9. Iterate monthly: Merge low-volume categories; promote common tags.

Copy-paste prompt (robust baseline):

“You are a support ticket classifier for a small business. Categorize and tag the ticket strictly using the allowed values. Output valid JSON only, no prose.Allowed categories: [Billing, Login/Access, Bug Report, Feature Request, Shipping/Delivery, How-To/Usage, Account Changes, Urgent/Outage, General].Allowed tags (examples, use zero or more): [refund, invoice, subscription, password reset, account lockout, two-factor, crash, error-500, slow-performance, integration, shipping-delay, tracking, return, exchange, workflow, onboarding, downgrade, upgrade, outage].Rules: If not confident, choose General. Prefer specific categories over General. Consider both subject and body. Return a confidence 0.00–1.00 and 1–2 sentence reason.Respond with JSON: {category: string, tags: string[], urgency: one of [low, normal, high], confidence: number, reason: string}Ticket subject: [paste subject]Ticket body: [paste first 500 characters of body]”

Worked example:

• Input: Subject: “Refund for double charge.” Body: “I was billed twice for May. Please reverse one charge. Order #48392.”
• Expected JSON: {“category”:”Billing”,”tags”:[“refund”,”invoice”],”urgency”:”normal”,”confidence”:0.86,”reason”:”Billing dispute with explicit refund request”}
• Automation: Apply tags, route to Billing queue, attach macro with refund steps.

Metrics to track:

• Auto-triage rate: % of tickets auto-tagged and routed (target 60–80%).
• Precision on auto-routed: % correct among auto-routed (target ≥85%).
• Manual correction rate: % of AI tags edited by agents (target ≤15%).
• Time-to-first-response: Aim for 15–30% faster within 30 days.
• SLA breach rate: Especially for Urgent/Outage (target -30%).

Common mistakes and quick fixes:

• Too many categories: Merge into 6–10; move nuance to tags.
• Letting AI guess: Enforce confidence threshold and General fallback.
• No keyword guardrails: Add a short dictionary for refunds, outages, password resets.
• Unlabeled test data: Label 200 tickets first; otherwise you can’t measure precision.
• Ignoring multilingual: Detect language; translate to English for classification; store original text.

1-week action plan:

1. Day 1: Draft taxonomy (8 categories, 20 tags). Write category definitions + examples.
2. Day 2: Export 300 tickets. Manually label 150 for ground truth.
3. Day 3: Implement keyword guardrails (refund, reset, outage, shipping).
4. Day 4: Plug in the prompt above. Test on 150 labeled tickets. Tune wording to lift precision.
5. Day 5: Go live with confidence ≥0.70. Auto-route Billing, Login/Access, and Bug; send rest to General.
6. Day 6: Review 50 live tickets. Adjust tags and guardrails.
7. Day 7: Baseline metrics. Set weekly targets for auto-triage rate and precision.

Expectation: Within 2 weeks, you should see ~60–75% of tickets auto-tagged and routed with ≥85% precision, and a noticeable drop in time-to-first-response.

Your move.

—Aaron

You’re right to focus on results and KPIs. Quick win you can try now (under 5 minutes): paste the first two pages of two RFPs into your AI assistant and ask for a side-by-side requirements delta, a go/no-go score, and exact citations with page numbers. You’ll get a clear direction faster than a meeting.

The problem: RFPs are long, repetitive, and full of musts/mays buried in legalese. Comparing them manually is slow and risky. Small misses create compliance gaps and lost deals.

Why it matters: Faster, clearer comparisons mean earlier go/no-go calls, tighter pricing, fewer errors, and a higher win rate. Speed compounds when you’re handling multiple RFPs per quarter.

What I’ve learned: Force the AI to cite every claim and build a compliance matrix first. Then draft from a vetted “Answer Library,” not from scratch. This removes fluff, prevents hallucinations, and keeps you on-message.

What you’ll need:

• Two or more RFPs (PDF or text). If scanned, export to text first.
• Your last winning proposal and a short “Company Facts” list (services, differentiators, past performance, certifications).
• A chat-based AI assistant.
• A spreadsheet for the compliance matrix (or ask AI to output a table you paste into your sheet).

How to do it (step-by-step):

1. Quick comparison (the 5‑minute test)Copy-paste the first 3–5 pages (scope, submission, evaluation criteria). If the RFPs are long, tell the AI you’ll send in parts and to reply “Ready” until you say “All parts sent.”Copy-paste prompt: “You are my RFP analyst. Compare RFP A and RFP B. Output: 1) side-by-side key requirements, 2) must-have compliance items, 3) risk/ambiguity list, 4) a go/no-go score for each (0–100) with rationale, 5) exact quotes with page numbers for every claim. If uncertain, say ‘Unknown’ and list what to ask the issuer.”What to expect: A digestible snapshot for decision-makers and a question list to send to procurement.
2. Build a compliance matrix (foundation)Copy-paste prompt: “Extract every explicit requirement from this RFP and output a compliance matrix with columns: ID, Requirement (verbatim), Page/Section, Must/Should, Our Response Plan (1 sentence), Owner, Evidence Needed. Use exact quotes with page numbers. Flag any contradictions or missing info.”What to expect: A checklist you can assign to owners. Paste into your spreadsheet and track status.
3. Create (or refresh) your Answer LibraryPaste your last winning proposal + Company Facts. Then:Copy-paste prompt: “From the materials provided, build an Answer Library. For each question theme (e.g., security, SLAs, implementation, support, team, methodology, pricing assumptions, risk management), produce: a) a 120–180 word default answer, b) 3 proof points (client, result, metric), c) 2 short variations for enterprise vs. mid-market, d) a ‘Do not claim’ guardrail list. Use plain English and keep numbers conservative.”What to expect: Re-usable, on-message answers that reduce drafting time by 60–80%.
4. Draft tailored responses fastCopy-paste prompt: “Using the compliance matrix and Answer Library, draft responses for sections [list sections]. Rules: 1) start with a 2–3 sentence win theme that mirrors the buyer’s language, 2) keep each answer under 180 words unless the RFP allows more, 3) end with a measurable outcome (time saved, cost reduced, risk lowered), 4) cite where each claim comes from (Answer Library reference or RFP page), 5) list open questions we must confirm.”What to expect: Clean drafts aligned to the buyer’s evaluation criteria, ready for SME polish.
5. Executive summary in the buyer’s wordsCopy-paste prompt: “Write a one-page executive summary in the buyer’s voice. Mirror their priorities from the RFP’s evaluation criteria. Structure: Situation, Desired Outcomes, Our Approach, Proof (3 bullets with numbers), Risk Mitigation, Next Steps. Keep it concrete, no buzzwords.”What to expect: A tight summary your execs can finalize in minutes.
6. QA pass with guardrailsCopy-paste prompt: “Act as compliance and truth-check. For each section, confirm: a) requirement addressed, b) no unverified claims, c) all figures cited, d) tone matches buyer, e) open questions listed. Output a punch list to fix.”What to expect: A final punch list to de-risk submission.

Metrics to track (weekly):

• Turnaround time: hours from RFP receipt to go/no-go; and to first full draft. Target: cut by 40–60%.
• Compliance defects: number of must-have misses in internal review. Target: near zero.
• Reuse rate: % of answers pulled from the library. Target: 70%+ without quality drop.
• Win rate: proposals won / submitted. Watch trend over 90 days.
• Hours saved: (Old hours – New hours) x proposals per month. Validate with timesheets.

Common mistakes and quick fixes:

• Mistake: Letting AI invent claims. Fix: Require verbatim quotes with page numbers and a “Do not claim” list.
• Mistake: No structure. Fix: Always start with a compliance matrix; assign owners.
• Mistake: Vague outputs. Fix: Specify output formats and word counts in every prompt.
• Mistake: Ignoring buyer language. Fix: Feed evaluation criteria and ask the AI to mirror terminology.
• Mistake: Overloading the model. Fix: Send documents in labeled parts; ask it to wait until “All parts sent.”

1‑week action plan:

• Day 1: Gather 2–3 recent RFPs, last winning proposal, and Company Facts. Set up a shared spreadsheet for the compliance matrix.
• Day 2: Run the quick comparison on two RFPs. Hold a 15‑minute go/no-go using the outputs and question list.
• Day 3: Build the compliance matrix for the live RFP. Assign owners and due dates.
• Day 4: Generate the Answer Library and tailor responses for top sections.
• Day 5: Produce the executive summary. Run the QA guardrail prompt and close the punch list.
• End of week: Log the metrics (time saved, defects, reuse rate). Lock the prompts that worked into your internal playbook.

Premium tip: Pre-bake a “RFP Comparison Rubric” with weighted criteria (fit, risk, margin, capability). Ask the AI to score each RFP against the rubric with citations. You’ll get consistent, defensible go/no-go calls across the team.

Your move.

Hook: Cut appointment no-shows by 50% or more using AI-driven reminders — without hiring more staff.

The problem: Missed appointments are revenue leaks and wasted staff hours. Traditional one-size-fits-all reminders don’t change behavior.

Why it matters: Reducing no-shows improves cash flow, capacity utilization, and patient outcomes. Even a small reduction in no-shows can pay for the system in weeks.

Experience-based lesson: I’ve seen clinics move from 18–20% no-shows to under 6% by combining timing, personalization, and easy rescheduling in reminders. The core is relevance: remind the right person, at the right time, with the right call-to-action.

Do / Do not checklist

• Do: Personalize message (name, service, time), include one-click confirm/reschedule, and use multiple channels (SMS + email).
• Do: Send a sequence: booking confirmation, 3 days out, 24 hours, and 2 hours (if needed).
• Do not: Spam patients with too many messages or send vague generic reminders.
• Do not: Omit an easy way to reschedule or cancel — friction raises no-shows.

Step-by-step implementation (what you’ll need, how to do it, what to expect):

1. What you’ll need: appointment system (calendar or PMS), SMS/email gateway, and an AI prompt engine or built-in message personalization module.
2. Integrate data: connect appointment times, patient name, service type, and contact method into the reminder tool.
3. Create message templates: generate 3–4 templates (confirmation, 3-day, 24-hour, same-day) with CTA links for confirm/reschedule and short prep instructions.
4. Use AI to personalize tone and urgency per patient: older patients get clear, gentle phrasing; busy professionals get concise, action-focused text.
5. Launch a pilot for one provider or clinic for 30 days and monitor metrics.

Copy-paste AI prompt (use in your AI tool to generate personalized messages):

“Write four appointment reminder messages for a medical clinic patient named [NAME] booked for [SERVICE] on [DATE] at [TIME]. Keep messages short and clear. Include: 1) confirmation at booking, 2) 3-day reminder with prep steps, 3) 24-hour reminder with a one-click confirm/reschedule link labeled [LINK], 4) 2-hour reminder. Adjust tone for age 60+ to be warmer and slightly more detailed. Use plain language and include option to reply ‘CANCEL’ to cancel.”

Metrics to track:

• No-show rate (%)
• Confirmation rate (%)
• Reschedule/cancel rate (%)
• Revenue recovered per month ($)
• Staff time saved (hours/week)

Common mistakes & fixes:

• Too many messages → Cut to 3 strategic touches, test timing.
• Generic text → Use AI to inject service-specific and patient-specific details.
• No reschedule link → Add one-click reschedule; reduce friction.

1-week action plan:

1. Day 1: Map your appointment data and pick an SMS provider.
2. Day 2: Create 3–4 templates; use the AI prompt above to draft variants.
3. Day 3: Configure automation flow (booking, 3-day, 24-hour).
4. Day 4: Add confirm/reschedule links and test end-to-end with staff.
5. Day 5–7: Pilot with one provider, collect initial metrics, and iterate.

Your move.

Aaron

Smart instinct: limiting today to a top three forces trade-offs and gives you measurable wins. Here’s how to make AI do the heavy lifting and keep you focused on results.

What’s really going on: You’ve got more demand than time. The risk isn’t doing the wrong work; it’s spreading thin and moving nothing that matters. AI can prioritize with a clear scoring model and your constraints—if you feed it the right inputs.

Why it matters: A tight top three tied to revenue, risk, and relationships consistently beats long lists. Expect fewer context switches, cleaner calendar blocks, and visible progress against KPIs.

Lesson from the field: Prioritization only works when you define the scoring rules upfront. I use a weighted model: Impact (40%), Urgency (30%), Irreversibility/Cost of Delay (20%), Effort inverse (10%). AI applies the math fast; you retain the judgment call.

What you’ll need:

• Your goals for the quarter (simple bullets).
• Today’s hard constraints (meetings, deadlines, energy windows).
• A raw task list with time estimates and impact notes (revenue, risk, relationships).
• 7–8 hours on the calendar, with one protected deep-work block.

Step-by-step (10 minutes, start of day):

1. Collect: Dump all candidate tasks for today (10–20 items max). Add a 5–60 minute time estimate to each.
2. Annotate: For each task, note due date, expected impact (revenue $, cost saved, risk reduced), dependencies, and energy needed (high/medium/low).
3. Prioritize with AI: Paste the list into the prompt below. Expect a ranked top three with a time-blocked schedule and clear justifications.
4. Commit: Accept or swap one item at most. Lock calendar blocks for the three tasks and one overflow task.
5. Execute: Start immediately with Task #1. No inbox until it’s complete or the timer ends.

Copy-paste prompt (robust, daily):

You are my daily prioritization chief of staff. Use this scoring: Priority Score = 0.4*Impact + 0.3*Urgency + 0.2*Irreversibility (cost of delay) + 0.1*(1/Effort). Definitions: Impact = revenue generated or protected, cost saved, or critical relationship strengthened. Urgency = deadline proximity or external commitment. Irreversibility = negative consequences if delayed. Effort = time estimate in hours. If info is missing, ask up to 3 concise questions first.

My quarterly goals: [bullet list]

Today’s hard constraints: [meetings, travel, hours available, energy windows]

Tasks (format: Title — Time — Due — Impact (revenue/risk/relationship + estimate) — Dependencies — Energy):

[paste 10–20 tasks]

Output exactly this: 1) Top 3 tasks ranked with Priority Score and one-sentence justification each. 2) A time-blocked schedule for today that fits my constraints, with start/end times and buffers. 3) Two backup tasks for overflow. 4) A 2-line risk note: what I’m deliberately not doing today and why. 5) If I only finish one task, which one and why (one sentence).

Variant prompts:

• Sales day: “Weight Impact as pipeline $ added or revenue closed; add a 10-minute pre-call prep block before any outreach tasks.”
• Operations day: “Weight Irreversibility higher (0.3) for compliance or outage-risk items; require dependencies to be cleared before scheduling.”
• Low-energy day: “Prioritize tasks matching low/medium energy; split any 90-minute+ task into 2–3 chunks with a reset in between.”

What to expect: The AI will return a tight top three, scheduled, with clear trade-offs. If it gives you more than three, push back: “Return exactly three.” If estimates look off, adjust and rerun—one minute.

Insider trick (premium): Use the 1-1-1 rule for coverage—ensure your top three include 1 revenue driver, 1 risk reducer, 1 relationship builder. If one category is empty, you’re likely optimizing local tasks over strategic movement.

Metrics to track (daily/weekly):

• % of work hours on Top 3 (target: 60–70%).
• Top 3 completion rate (target: 2–3 per day, 80%+ weekly).
• Impact proxy: pipeline $ added or cost avoided from completed tasks (simple estimate is fine).
• Deep work hours achieved (target: 2–3/day).
• Planning time (target: 10 minutes/day, 45 minutes/week review).

Common mistakes and fast fixes:

• Tasks are projects: If a task exceeds 90 minutes, split it. Fix: “Break into 45–60 minute steps with a deliverable for each.”
• Vague outcomes: “Work on marketing” won’t rank well. Fix: Add a measurable outcome (e.g., “Draft 1 email to re-engage 200 lapsed leads”).
• Ignoring energy windows: Fix: Schedule high-cognition work in your best 2-hour block; move admin to lows.
• Dependencies skipped: Fix: Add a 15-minute unblocker task before the main item.
• AI overreach: Fix: Lock weights; only you change them. Ask the AI to show its scoring table when uncertain.

1-week rollout:

1. Day 1: Use the main prompt. Commit to top three and one 90-minute deep-work block. Track time spent.
2. Day 2: Calibrate estimates. Add energy notes and adjust weights if needed (e.g., more urgency midweek).
3. Day 3: Introduce the 1-1-1 rule. Ensure at least one revenue task daily.
4. Day 4: Add a “stop doing” line—explicitly defer two items and capture why.
5. Day 5: Weekly review: completion rate, hours on Top 3, impact proxy. Update the task template and weights.
6. Weekend: Preload Monday with 5–8 candidates and constraints; run the prompt in two minutes.

Final nudge: Copy the prompt, paste your tasks, lock the top three in your calendar, and start the first block now. Your move.

Cut the noise — pick the three tasks that move the needle today.

Problem: You have a long to-do list, limited time, and you’re unsure which items will actually change outcomes.

Why it matters: Focusing on the right three tasks increases output and reduces decision fatigue. For executives and founders, that often means revenue, retention, or risk reduction.

Quick lesson from practice: I use a simple AI-assisted rubric that ranks tasks by impact, effort, and risk. In minutes I get a ranked list and an execution plan for the top three.

• Do: Use AI to score tasks, not to decide for you.
• Do: Keep task descriptions short, outcome-focused, and measurable.
• Do-not: Feed vague tasks like “work on marketing” — be specific.
• Do-not: Replace judgment; use AI as an amplifier.

Step-by-step (what you’ll need, how to do it, what to expect):

1. What you’ll need: a list of today’s tasks (5–15 items), a device, and access to any AI chat tool.
2. How to do it:
   1. Write each task as a one-line outcome (e.g., “Close $10K deal with Client X”).
   2. Use this copy-paste prompt (below) with your list.
   3. Ask the AI for ranked top 3, with reasoning and a 30–60 minute next-action for each.
3. What to expect: a ranked list, why each was chosen, and a concrete next step you can execute within an hour.

Copy-paste AI prompt (use this exactly):

Here is my list of tasks for today. For each task, evaluate impact (1–10), effort (1–10), and risk (1–10). Return a ranked top 3 that maximizes impact/minimizes effort and risk. For each of the top 3, give a 30–60 minute next action, expected outcome, and required owner. My tasks: [PASTE YOUR TASKS SEPARATED BY SEMICOLONS]

Metrics to track:

• Conversion of tasks to completed: % of top 3 completed by day-end.
• Impact realized: revenue closed, hours saved, tickets resolved (numeric).
• Cycle time: average time from selection to completion (hours).

Common mistakes & fixes

• Mistake: Vague tasks. Fix: Convert to a one-line measurable outcome.
• Mistake: Overloading top 3. Fix: Limit to tasks that can start today; defer others.
• Mistake: Ignoring dependencies. Fix: Note required inputs in next-action step.

Worked example

Tasks: “Finalize proposal for Acme ($8k potential)”; “Prepare investor update deck”; “Resolve product bug impacting billing”; “Approve hiring for engineer”; “Draft next-week newsletter”.

AI ranking might return top 3: 1) Resolve billing bug — next action: identify root cause log and assign to engineer (30–60m) — expected outcome: stop revenue leakage. 2) Finalize Acme proposal — next action: incorporate pricing terms and send for signature — expected outcome: close $8k. 3) Approve hiring — next action: sign offer to expedite start — expected outcome: reduce backlog. Each has impact/effort/risk scores and owners.

1-week action plan (practical cadence):

1. Day 1: Run AI prioritization every morning for top 3.
2. Days 2–4: Execute top 3, track completion and outcomes.
3. Day 5: Review results, adjust scoring thresholds, iterate prompt if needed.

Your move.