Jeff Bullas

Nice call-out: I like your emphasis on keeping the workflow simple — start with a few strong signals and iterate. That’s the fastest way to win.

Why this works

AI turns messy signals (job title, visits, form answers) into a single, repeatable number your CRM can act on. That saves reps time and focuses effort on leads most likely to convert.

What you’ll need

• Your CRM with a numeric field (AI_Lead_Score 0–100).
• A lead source that writes data to the CRM (form, chat, ad).
• An automation tool you use (Zapier, Make, or CRM workflows).
• An AI endpoint accessible via the automation tool (ChatGPT/OpenAI integration).

Quick do / don’t checklist

• Do start with 5 signals: company size, title seniority, industry fit, engagement (pages/email), explicit intent (requested demo/budget).
• Do store the one-sentence rationale for rep trust and overrides.
• Don’t push scores to automation without a short pilot (visible-only first).
• Don’t overwhelm the model with dozens of fields at start.

Step-by-step setup (non-technical)

1. Create a numeric CRM field called AI_Lead_Score (0–100) and a AI_Rationale text field.
2. Pick your 5 inputs and map them into CRM fields so they’re always present.
3. Build an automation: on new lead or update, send a short formatted summary to AI (company, title, industry, visits, opens, form answers, budget).
4. Use the AI prompt below to return a score and one-sentence rationale. Parse results and write SCORE → AI_Lead_Score; RATIONALE → AI_Rationale.
5. Create simple workflows: >70 assign to AE now; 40–69 nurture sequence; <40 marketing drip.
6. Run a visible-only test for 50 leads, compare conversions, then flip to enforcement when confident.

Copy-paste AI prompt (use as-is, replace placeholders)

Evaluate this lead and return a single numeric score 0-100, a one-sentence rationale, and a confidence level (low/medium/high). Use these criteria: company size, title seniority, industry fit (Ideal: SaaS, e-commerce, finance), explicit buying intent (requested demo, budget mentioned), timeline, and engagement (pages visited, email opens). Inputs: Company: {{company}}, Title: {{title}}, Industry: {{industry}}, Website visits: {{visits}} pages, Email opens: {{opens}}, Form answers: {{form_answers}}, Budget mentioned: {{budget}}, Timeline: {{timeline}}. Output format exactly: SCORE: ; RATIONALE: ; CONFIDENCE: .

Worked example

• Input: Company: “Acme Retail”; Title: “Head of eCommerce”; Industry: e-commerce; Visits: 8 pages; Opens: 3; Form: “Needs checkout optimization”; Budget: “Yes, $50k”; Timeline: “Immediate”.
• AI Output (example): SCORE: 86; RATIONALE: Senior ecommerce leader with clear budget and immediate timeline plus strong site engagement.; CONFIDENCE: high.
• Action: CRM writes 86 to AI_Lead_Score, assigns to AE and creates a task: “Contact within 1 hour.” AI_Rationale stored on lead record.

Common mistakes & quick fixes

• Too many signals — fix: reduce to top 5 and add more later.
• Blind automation — fix: run visible-only pilot for 2 weeks.
• Score drift — fix: review sample leads monthly and tweak prompt or thresholds.

1-week action plan

1. Day 1: Create AI_Lead_Score and AI_Rationale fields; list 5 signals.
2. Day 2: Build automation to send lead summary to AI; test with 5 examples.
3. Day 3: Parse AI response into fields; set score band workflows (3 bands).
4. Day 4: Train reps to read rationale and override if needed.
5. Day 5–7: Run 50-lead visible-only test and compare outcomes to previous week.

What to expect

Within two weeks you’ll see consistent prioritization. Within a month you should notice faster contact times and clearer rep focus. Keep the process simple, measure, and iterate.

Thanks — asking “where do I start?” is exactly the right question. That focus on practical first steps is the most useful point and it will save you time and money.

Here’s a pragmatic, do-first roadmap for creating affordable AI-assisted AR/VR assets — aimed at non-technical creators over 40 who want quick wins.

What you’ll need

• A free 3D editor (Blender is powerful and free).
• A game/real-time engine for preview (Unity Personal or Unreal Engine free tiers).
• An AI image tool for concept art and textures (local Stable Diffusion or commercial image generator).
• A simple AI helper or prompt template to speed drafting (I provide one below).
• Optional: access to low-cost stock 3D models to modify.

Step-by-step — practical route

1. Define the asset and constraints: target platform (AR phone, VR headset), max polycount, and style (realistic, stylized).
2. Generate concept art: use an AI image generator for 4-6 views (front, side, top, close-up). This saves sketch time.
3. Model in Blender: block out the basic shapes (low-poly first). Keep scale consistent (use real-world meters).
4. UV unwrap and create textures: either paint in Blender or generate texture maps from AI images and tile as needed.
5. Optimize: reduce polygons, merge meshes, remove hidden faces, create LODs (levels of detail).
6. Export to glTF/FBX and import into Unity/Unreal. Test in a simple scene with correct lighting and scale.
7. Deploy to AR: use Unity’s AR Foundation or WebXR for web AR — test on device frequently.

Quick copy-paste AI prompt (use for texture or concept generation)

“Generate high-resolution texture and concept images for a mid-century wooden lounge chair: warm walnut wood grain, worn leather cushions in deep cognac, subtle fabric stitching, 4 views (front, side, top, close-up of armrest), realistic lighting, seamless wood grain texture map suitable for UV mapping.”

Worked example — mid-poly wooden chair

• Day 1: Use prompt above to get concept images and a wood texture map.
• Days 2–4: Block out chair in Blender, keeping 1–2k tris.
• Days 5–7: UV unwrap, apply AI textures, bake normal and AO maps.
• Days 8–10: Export to glTF, import into Unity, test in AR on phone.

Common mistakes & fixes

• Wrong scale — Fix: model using meters and test with a human proxy.
• Textured seams — Fix: check UV seams and use seam-aware baking.
• Too many polys — Fix: use decimate/modifier and create LODs.
• Poor lighting in AR — Fix: add environment reflections and light probes.

Action plan — 30-day sprint

1. Week 1: Concept and texture generation with AI.
2. Week 2: Modeling and UVs in Blender.
3. Week 3: Texturing, baking, optimization.
4. Week 4: Integration into engine and AR testing; iterate based on device tests.

Do this one asset start-to-finish to build confidence. Do use AI to speed ideas and textures. Don’t overcomplicate early — avoid custom shaders and extreme polygon counts until you’ve shipped a test scene.

Small, consistent action wins: pick one asset, follow the steps, and you’ll have a working AR/VR item within a week. Keep iterating from there.

Want quarterly OKRs you’ll actually follow — without overthinking? Use AI to draft clear, measurable OKRs fast, then tweak them to fit your life. This is a practical, repeatable process you can use every quarter.

Why this works: AI helps turn fuzzy ambitions into measurable Objectives and Key Results. You keep the judgment, AI does the structure and language. Quick win. Momentum follows.

What you’ll need

• A short list (1–3) of top personal goals for the quarter.
• One or two measurable signals (time, money, count, % improvement).
• Quarter dates and any constraints (travel, budget, health).
• 5–10 minutes to edit and schedule reviews.

Step-by-step: how to do it and what to expect

1. Pick 1–3 goals you care about. Don’t overcommit.
2. Use the copy-paste prompt below with your goals and constraints. Expect AI to return 2–4 Objectives with 2–4 measurable Key Results each.
3. Review and simplify: make KRs numeric, time-bound, and owned by you.
4. Set a weekly 15-minute check-in on your calendar to track progress.
5. At quarter midpoint, ask AI for a revised plan if you’re off-target.

Copy-paste AI prompt (use as-is)

Prompt: You are an expert OKR coach. For the next 3-month quarter (dates: [insert start and end dates]), create OKRs for a single person focused on these top goals: [list goals]. Produce 2–3 clear Objectives. For each Objective list 3 measurable Key Results with numeric targets and deadlines. Include one short milestone checklist and one recommended weekly action to make progress. Assume constraints: [list constraints]. Keep language simple and actionable.

Example output (quick)

• Objective: Finish a 30,000-word draft of my non-fiction book by quarter end.
  • KR1: Write 30,000 words (10 chapters) by [date].
  • KR2: Complete 3 chapters per month and 1 chapter per week.
  • KR3: Get feedback on 2 chapters from a reader by week 10.
  • Milestone checklist: Outline done → Chapters 1–3 → 4–6 → 7–10 → Feedback.
  • Weekly action: Block 90 minutes, three times a week, on calendar.

Common mistakes & fixes

• Vague objectives — make them outcome-focused (replace “exercise more” with “complete 36 workouts”).
• Too many KRs — limit to 2–4 per Objective.
• No measurement — attach numbers or timeframes to every KR.
• Ignoring cadence — set weekly check-ins and a mid-quarter review.

Action plan (next 30 minutes)

1. Choose 1 top goal and fill the prompt fields (goal, dates, constraints).
2. Run the prompt in your AI tool and paste the result into a document.
3. Edit KRs to be numeric, add them to your calendar, and set weekly reminders.

Small, measurable steps beat big intentions. Use the prompt, refine for reality, and review weekly — that’s how quarters get won.

You’ve nailed idea volume and convergence. Now let’s install a “Decision Theatre” so the room moves from options to action with receipts — fast, calm, and bias-resistant.

The missing layer: workshops stall after the shortlist. People talk; momentum fades. The fix is a lightweight operating system that auto-creates the artifacts leaders need to say “yes” — a decision brief, a test tracker, and a clean handoff — while the energy is high.

What you’ll need (adds 10 minutes of prep, saves 30 minutes of debate)

• Visible timer and a single shared scorecard (Impact, Feasibility, Speed, Confidence)
• One laptop with AI on a shared screen; one scribe (the “AI sidecar”)
• Pre-baked prompts: Decision Brief, Test Tracker, Synthesis (copy/paste below)
• Decision rule and tie-breaker: Highest weighted score wins; if tied, choose the idea with the lowest Days-to-Signal (setup hours ÷ daily reach)

Decision Theatre — layer this onto your 60–75 min flow (adds 12–15 minutes)

1. Score and shortlist — Use your weighted formula. Keep the winner + runner-up visible.
2. Days-to-Signal check (3 min) — Ask: “How many hours to set up? What’s the expected daily reach?” Compute Days-to-Signal for the two finalists. If tied on score, pick the lower number.
3. Decision Brief auto-build (5–6 min) — Paste the Decision Brief prompt with the winning Concept Card. Display the draft live; edit only for numbers and clarity.
4. Owner lock + calendar (2–3 min) — Name the owner. Book a 60–90 min Day-1 block while everyone watches. Add the test start and mid-point review.
5. Test Tracker generate (2–3 min) — Paste the Test Tracker prompt. Copy the table into your sheet/doc. Everyone knows what to log tomorrow.

Copy-paste AI prompts (use as-is)

• Decision Brief (for the winner)“Turn this concept into a one-page decision brief and 7-day experiment plan.
  Concept Card: [PASTE WINNING CONCEPT CARD]
  Include exactly:
  1) Goal (one sentence) and Why Now (one sentence)
  2) Hypothesis (if we do X for [audience], we’ll see Y by Day 7)
  3) Success Metric + Threshold (single number with target)
  4) Plan: 3 steps with time and cost per step (sum totals)
  5) Day-1 Action (what happens in the first 60–90 minutes)
  6) Risks + Mitigations (3 bullets)
  7) Owner and Support (roles, not names)
  8) Kill Rule (when to stop) and Pivot Option (one)
  9) Calendar text (title, description, agenda for the Day-1 block)
  10) Team message draft (short email/slack announcing the test)
  Keep it crisp and skimmable.”
• Test Tracker (simple, daily)“Create a 7-day test tracking sheet based on this brief: [PASTE DECISION BRIEF]. Provide:
  – A table with columns: Date, Action Taken, Exposure/Reach, Primary Metric Count, Conversion %, Spend, Notes, Next Action.
  – A daily two-sentence ‘What we learned’ template.
  – A mid-test checkpoint rule: ‘If we are below 50% of threshold by Day 4, apply this one adjustment: [suggest one within constraints].’
  Return the table and the template ready to paste into a doc.”
• Synthesis (end-of-test wrap)“Using this tracker data: [PASTE RESULTS], write 5 bullets: 1) What happened (with numbers), 2) What it means, 3) Decision (scale, iterate, or stop) with rationale, 4) Next actions for 14 days (3 bullets), 5) One-sentence narrative we can tell stakeholders. Keep it concrete and brief.”

Insider upgrade: calibrate the room in 90 seconds

• Before scoring, show a reference card (a past win) and say: “This is a 4 on Impact.” It anchors everyone. Then score.
• Ask one question before voting: “What would make this test fail fast?” Note it. If it’s solvable within constraints, proceed; if not, demote.

Example (realistic, numbers-first)

• Problem: “Boutique gym needs 20 extra weekday bookings in 7 days.”
• Top concept: “Lunch Break Buddy Pass via SMS: bring a friend free at 12–2pm.”
• Days-to-Signal: Setup 2 hours (SMS + poster). Expected reach 800/day (member list). Days-to-Signal ≈ 0.25 — fast.
• Threshold: 20 bookings in 7 days (primary metric: bookings from SMS code LUNCH20).
• Plan (3 steps): import opt-in list and send SMS ($60, 1h); front-desk code tracking (0$, 30m); social post for members to share (0$, 30m).
• Kill Rule: If bookings <10 by Day 4, switch to “Trainer-led 20-min sampler” video link in SMS (no extra cost).

Common mistakes and quick fixes

• Unrealistic time/cost lines — Fix: Require sums in the Decision Brief; if totals exceed constraints, the idea is not ready.
• Metric drift — Fix: One success metric per test. Secondary numbers live in Notes only.
• Soft ownership — Fix: “Owner” is a role with a booked Day-1 calendar block. No block, no test.
• Novelty bias — Fix: Apply the Days-to-Signal tie-breaker; lowest wins.
• Debate spirals — Fix: Use the pre-mortem prompt for 7 minutes, then lock the brief. Discussion ends when the calendar invite is sent.

What to expect

• Within-session outputs: one Decision Brief, one Test Tracker, owner + calendar booked.
• Clarity: everyone sees the threshold, the kill rule, and the exact Day-1 action.
• Tempo: first signal inside 24–72 hours for most low-cost channel tests.

1-week action plan (tight, realistic)

1. Before Day 1: Paste the three prompts into a doc; test them once with a dummy idea (10 minutes).
2. Day 1: Run your convergence flow + Decision Theatre. Leave with a booked Day-1 block.
3. Day 2–3: Execute step 1–2 of the plan. Log daily in the tracker.
4. Day 4: Apply the checkpoint rule if under 50% of target.
5. Day 5–6: Finish step 3. Prepare the Synthesis prompt.
6. Day 7: Run Synthesis, make the scale/iterate/stop call, and set the next 14-day plan if scaling.

Closing thought: Creativity is the spark; convergence is the engine. Use AI to manufacture the proof — brief, tracker, and calendar — and you’ll turn fast ideas into faster decisions, week after week.

Strong call on “evidence over volume” and anchored rubrics. That’s the difference between a nice chat and a predictive interview. Let me add a fast, two‑pass system that gives you questions, scoring, follow‑ups, and sample answers you can calibrate in one sitting.

What you’ll need (10–15 minutes prep):

• A 2–4 sentence brief: team, outcomes, top responsibilities.
• 2–3 must‑have skills/behaviors tied to business outcomes.
• Seniority (junior/mid/senior) and interview length.
• One recent strong hire’s notes (for a quick back‑test).

The two‑pass method (why it works): Pass 1 generates a tight interview pack. Pass 2 produces excellent/acceptable/weak sample answers so interviewers align on what “good” actually sounds like. You walk away with evidence‑driven questions and a shared scoring language.

Copy‑paste master prompt (Two‑Pass Interview System)

Pass 1 — Interview Pack

“You are my interview design assistant. Build a complete interview pack for a [junior/mid/senior] role. Brief: [2–4 sentences: team, outcomes, top responsibilities]. Must‑have skills/behaviors: [2–3 items]. Interview length: [30–60] minutes. Deliver: (1) 10 questions split into 4 behavioral, 4 technical/skills, 2 situational; (2) for each question: a one‑line rubric with clear anchors for excellent/acceptable/weak and 2–3 follow‑up probes that test evidence (baseline, target, constraints, metrics, trade‑offs); (3) a short red‑flags list per question; (4) a timing plan and weights by competency totaling 100 points; (5) a plain‑text scoring sheet I can print. Keep language simple, job‑related, and specific to the brief. Avoid generic questions.”

Pass 2 — Sample Answers for Calibration

“Using the interview pack you just created, generate three short sample answers (excellent, acceptable, weak) for each question. For each sample, note the signals that make it that level (numbers used, decisions made, constraints handled, outcome). Keep each sample answer under 120 words. End with a one‑line coaching tip for the interviewer (what to probe next if the answer is thin).”

Step‑by‑step (do this once, then reuse):

1. Draft the brief — 3 sentences: team, outcomes, top responsibilities. Add seniority and must‑haves.
2. Run Pass 1 — you’ll get questions, rubrics with anchors, probes, red flags, and a 100‑point scorecard in 1–2 minutes.
3. Run Pass 2 — print or share the sample answers so interviewers see what “excellent” vs. “weak” sounds like.
4. Back‑test — hold the pack against one recent hire’s notes. If your “excellent” sample matches their behaviors, you’re calibrated.
5. Freeze the pack — keep 6–8 questions for a 45‑minute interview. Store the rest as optional follow‑ups.

Insider trick (saves time): Ask the AI to order questions by difficulty: easy (context), medium (decisions under constraints), hard (counterfactuals/trade‑offs). Start easy to build rapport, finish hard to separate rehearsed answers from real competence.

Bonus prompts (copy‑paste as needed):

• Bias & legal check: “Review these questions and rubrics for non‑job‑related or potentially discriminatory content. Suggest neutral, job‑related rewording while preserving intent. Highlight anything to avoid and explain why in one line.”
• Follow‑up probe generator: “For each question, add 2 evidence probes using this formula: Numbers (baseline/target), Constraints (what got in the way), Decisions (trade‑offs), Outcome (what changed).”
• Phone‑screen cut: “From the pack, produce a 15‑minute phone‑screen version with 5 high‑signal questions and a 20‑point scorecard. Include knockout red flags.”
• Panel split: “Split the 8 strongest questions between two interviewers. Add owner labels, timing, and a combined scoring sheet totaling 100 points.”

Worked mini‑example (Senior Operations Manager)

Brief: Team: Operations for a multi‑site services business. Outcomes: cut unit cost by 10% and improve on‑time delivery to 95% in 6 months. Responsibilities: streamline processes, manage vendor performance, and lead continuous improvement with the site leads. Must‑haves: process improvement, stakeholder communication, data‑driven decision‑making. Seniority: Senior. Interview length: 45 minutes.

• Behavioral: “Tell me about a time you reduced operational cost without hurting service levels.” Rubric: Excellent = baseline and target, root‑cause method, pilot, results with % and timeframe; Weak = vague fixes, no numbers.
• Technical: “Walk me through how you map a process to find bottlenecks. What data do you collect and why?” Rubric: Excellent = SIPOC/value‑stream map, cycle/queue time, variance, Pareto; Weak = general observations.
• Situational: “Vendor on‑time drops from 96% to 88% this month. What’s your 30‑day plan?” Rubric: Excellent = triage, SLA review, root cause with data, corrective actions, checkpoint metrics; Weak = wait‑and‑see.

What to expect:

• 8–12 role‑specific questions in minutes, with clear scoring anchors and red flags.
• Three sample answers per question to align interviewers and speed calibration.
• A 100‑point, printable scorecard so decisions are consistent and defensible.

Common mistakes & fixes:

• LLM drift — AI invents extra skills. Fix: repeat must‑haves in the prompt and say “ignore other competencies.”
• Vague rubrics — words like “strong communicator.” Fix: force numbers, constraints, decisions, outcome in every anchor.
• Overstuffed interviews — too many questions. Fix: cap at 8, weight to 100 points, and stick to timing.
• No back‑test — looks good on paper, weak in practice. Fix: test against one recent hire’s notes and adjust anchors.
• Single‑interviewer bias. Fix: share the sample answers pack; compare variance; standardize follow‑ups.

1‑week action plan:

1. Day 1: Write two briefs (3 sentences each). Pick one role to pilot.
2. Day 2: Run Pass 1 and Pass 2. Print the scorecard and sample answers.
3. Day 3: 20‑minute calibration with a hiring manager. Tweak anchors and red flags.
4. Day 4: Run one live interview with the script. Time each question.
5. Day 5: Back‑test on a recent hire’s interview notes. Adjust where needed.
6. Days 6–7: Roll to a second role. Start tracking interview‑to‑offer rate and 90‑day ramp proxy.

Closing thought: Don’t chase more questions. Chase clearer evidence. With a tight brief, anchored rubrics, and sample answers, AI becomes your fastest way to run fair, repeatable, high‑signal interviews.

Want faster local search wins? Use AI to tune your listings and posts so customers find you first.

Local SEO is simple when you break it into repeatable steps: clean listings, helpful posts, real reviews, and consistent info. AI speeds up writing, testing and scaling without needing technical skills.

What you’ll need

• Access to your Google Business Profile (and any other directories you use).
• A short list of what makes your business special (services, neighbourhoods, opening hours, photos).
• An AI writing tool (Chat-style AI or similar) to generate descriptions, posts and replies.

Step-by-step (do this in order)

1. Audit — List every directory where your business appears. Check name, address, phone (NAP) and hours for consistency.
2. Standardize — Pick one exact format for NAP and update every listing to match.
3. Optimize profile description — Use AI to write a short, local-focused description with one or two main keywords and a call-to-action.
4. Post regularly — Create 1–2 weekly posts about events, offers or local tips. Keep them short and include location words.
5. Generate FAQs & Q&A — Use AI to craft answers to common customer questions and add them to your profile.
6. Reply to reviews — Use AI to draft warm, personalized responses you can tweak and post.
7. Monitor & tweak — Check search impressions and calls after 4–6 weeks and refine keywords.

Example (before → after)

Before: “We sell plumbing supplies.”

After: “Reliable emergency plumbers in Northlake — 24/7 repairs, clear pricing and same-day visits. Call for a quick quote.”

Common mistakes & fixes

• Inconsistent NAP — Fix: Make one master listing and update everywhere.
• Keyword stuffing — Fix: Use natural language; focus on 1–2 local phrases.
• Generic posts — Fix: Make posts local and useful (events, tips, offers).
• Ignoring reviews — Fix: Respond within 48 hours with gratitude and a next step.

Copy-paste AI prompt (use as-is)

“Write a 150–200 character Google Business Profile description for a family-owned cafe in downtown Oakwood. Include: local phrase ‘downtown Oakwood’, two specialties (artisan coffee, gluten-free pastries), a friendly tone, and a call to action to visit today.”

7-day quick action plan

1. Day 1: Export all listings and note inconsistencies.
2. Day 2: Fix NAP on your top 5 listings (including Google).
3. Day 3: Use the AI prompt above to create a new profile description and publish it.
4. Day 4: Create three post ideas (events, offer, local tip) using AI.
5. Day 5: Draft replies to recent reviews and post them.
6. Day 6: Add or update 5 photos with short captions and alt text.
7. Day 7: Check analytics for clicks and calls; refine your keywords.

Small, consistent work wins local search. Start with the audit, use the AI prompt, publish, and repeat weekly. You’ll see more visibility and more real customers.

— Jeff

Quick win: if you’ve already tried the 7-day moving average method, great — now let’s try a no-code AI check that takes about 10 minutes and automatically spots season-aware anomalies.

Why try no-code AI next? It can auto-adjust thresholds, recognise weekly or monthly seasonality, give confidence scores, and send alerts — so you spend time investigating real problems, not chasing noise.

What you’ll need

• A CSV or Excel file with Date and Sales columns (ideally 90+ data points).
• A no-code tool with anomaly-detection or an AI assistant that accepts CSV uploads.
• A rough sense of periodicity (daily, weekly, monthly) and how sensitive you want detection to be.

1. Choose the tool: pick any no-code platform with a guided anomaly wizard or an AI assistant that can read CSVs.
2. Upload your data: point the tool to your file and confirm which column is Date and which is Sales. Ensure dates are parsed correctly.
3. Set seasonality: tell the tool whether your series is daily/weekly/monthly. If unsure, try weekly first for retail sales.
4. Pick sensitivity: start with medium (default). This balances false positives and misses.
5. Run detection: review the flagged dates, their deviation %, and the confidence score.

   Many tools will also show a small chart of expected vs actual — use that to verify visual mismatches.

6. Label a few cases: mark the flagged items as “true anomaly” or “expected”. This helps the tool learn.
7. Automate alerts: if useful, connect email/Slack so you get a short anomaly summary automatically.

Example (imaginary)

Daily sales for 120 days. Tool flags 2025-07-14: Sales 1,200 vs expected 420 (185% above), confidence 0.92 — reason: sudden spike. Action: check promotion/return logs for that date.

Common mistakes & fixes

• Missing dates: cause false anomalies. Fix: fill or mark missing days before upload.
• Trend drift: growing sales look anomalous. Fix: use trend-aware detection or compare year-over-year.
• Too small dataset: noisy results. Fix: use at least 60–90 points or aggregate to weekly.
• Over-sensitive settings: lots of flags. Fix: lower sensitivity or increase smoothing window.

Copy-paste AI prompt (use in the tool’s prompt box or an AI assistant):

“I have a CSV with columns ‘Date’ and ‘Sales’. Detect anomalies in the Sales time series, accounting for weekly seasonality. For each anomaly, return: date, sales value, expected value, deviation percent, confidence score (0–1), and a one-line suggested action (investigate, ignore, correct). Also suggest an appropriate sensitivity setting and whether I should aggregate to weekly or keep daily. Ask me questions if your results need clarification.”

Action plan (next 7 days)

• Day 1: Run the spreadsheet moving average check from earlier.
• Day 2–3: Upload last 90 days to one no-code tool and run the prompt above.
• Day 4: Label results, tweak sensitivity, and set a twice-weekly 10-minute review.

Keep it simple: start with one tool, one routine, and tune slowly. You’ll move from chasing noise to finding real problems fast.

Cheers — Jeff

Yes — AI can turn long-form into short, monetizable clips. But the money comes from good hooks, a single CTA and a measurement loop.

Short version: use AI to find moments, write tight 45–60s scripts, add captions and a single-step landing page. Then measure watch-time and CTR, iterate on the winning format.

What you’ll need

• Source: article, podcast transcript or full video.
• Auto-transcription tool or the original text.
• AI writing tool (ChatGPT-style).
• Simple editor for clipping and captions (mobile or desktop).
• Thumbnail creator and a landing page or trackable link (UTM or short link).

Step-by-step (do this now)

1. Pick one high-value asset (the episode/post that best matches your product or audience).
2. Create a clean transcript (auto-transcribe, 5–15 minutes to tidy).
3. Run the transcript through the AI prompt below to get 5x 45–60s scripts with hooks and CTAs.
4. Choose 2 scripts: one emotion-driven hook, one practical tip hook.
5. Clip or re-record to match the script; add readable captions and a clear visual CTA in the last 3 seconds.
6. Publish native-format (vertical), test 2 thumbnails, run organic and a small boost to the top performer.
7. Track: 3–10s retention, % watched, CTR to the landing page. Double down on winners.

Copy-paste AI prompt (use as-is)

Paste the transcript below. Create five 45–60 second video scripts optimized for Instagram Reels, TikTok, and YouTube Shorts. For each script include: 1) a 3-second hook, 2) three concise points, 3) one direct call-to-action (sign-up, link, DM), 4) suggested on-screen captions, and 5) a thumbnail idea. Keep language second person, action-focused, and short. Number each script.

Prompt variant — focus on conversions:

Use the transcript below. Identify the 3 moments most likely to drive clicks (based on urgency, benefit, or surprise). For each moment, write a 45-second script with: 3s hook, 3 points, one-step CTA, suggested caption text, and a short headline for the thumbnail. Also recommend the landing page type (lead magnet, webinar sign-up, product page).

Worked example

Source: 1,000-word podcast transcript about lead gen. AI extracts a surprising stat, a short how-to and a micro-case. It produces five scripts — you pick two, clip the original audio/video to match, add captions and a simple signup link. One clip, boosted with $20, drives sign-ups and improves CPL within a week.

Mistakes & fixes

• Weak hook → Fix: open with a question, shock stat, or bold promise in first 3s.
• No tracking → Fix: use UTM or short links and track CTR separately for each clip.
• Too many CTAs → Fix: pick one action and repeat it visually and verbally.

7-day action plan

1. Day 1: Pick asset and generate transcript.
2. Day 2: Run AI prompt; pick 2 scripts.
3. Day 3: Produce clip A; add captions and thumbnail.
4. Day 4: Produce clip B; prepare landing link.
5. Day 5: Publish both; A/B thumbnail in first 48 hours.
6. Day 6: Boost top performer with small spend; track CPL.
7. Day 7: Review metrics; scale the winner.

Quick reminder: AI speeds the work — your judgment on hooks, CTA clarity and measurement turns views into revenue. Start with one asset, test two hooks, and scale the winner.