Steve Side Hustler

Short answer: Yes — AI can take summarized analytics and suggest practical website layouts and simple wireframes, but it’s best used as an assistant to speed up decisions rather than an automatic designer. For a busy, non-technical person, the goal is to get clear, actionable suggestions you can test quickly.

What you’ll need — a small, focused pack of inputs so AI recommendations are useful:

1. Basic analytics summary: top 5 pages by visits, top entry/exit pages, and one conversion metric (newsletter signups, purchases, etc.). You can export these from your analytics dashboard as simple lists.
2. Business goal: a one-line statement (e.g., “get more newsletter signups” or “make product details clearer”).
3. One or two examples you like: screenshots or links to pages that have a layout feel you want.
4. A simple wireframe tool or paper and pen for quick sketches (anything from a free online mockup tool to a notebook).

How to do it — step-by-step, doable in an hour:

1. Summarize your analytics into 5 bullet points. Keep numbers minimal: page name, visits, bounce/exit rate, conversion rate if any.
2. Write a one-sentence goal and pick one page to focus on (start small).
3. Ask the AI for 3 layout options tailored to that page and goal — each option should say what elements to include (hero, form, social proof, product grid) and why, plus a suggested content priority order.
4. Pick one option and sketch a quick wireframe on paper or in your tool, following the content priority. Don’t polish — just block out sections.
5. Prototype a clickable version (or a single mocked-up page) and run a simple test: show it to 5 people or use a free remote-feedback tool for quick reactions.
6. Iterate: use the feedback and one more analytics snapshot to refine the layout. Repeat the cycle for your next page.

What to expect:

• AI gives fast ideas and clear trade-offs, but won’t replace human judgment — you still choose priorities.
• Results are best when inputs are tight and specific; vague analytics lead to generic designs.
• Small tests (one page, one goal) reveal real improvements faster than redesigning a whole site.

Try one page this week: a tidy summary, a simple ask to AI for three focused layouts, and a 30-minute sketch session. You’ll build confidence, get concrete wireframes, and learn what analytics matter most for design decisions.

Nice call on making the scorecard the source of truth and anchoring Must-haves to a business metric — that single change flips interviews from gut-feel to evidence-driven. Here’s a compact, no-nonsense workflow you can run this week if you’re short on time.

What you’ll need

• One short role brief: title, 3 core responsibilities, and 3 concrete 6‑month outcomes (numbers or deliverables).
• An AI assistant and a simple doc or spreadsheet to capture the scorecard and interview notes.
• Two interviewers for independent scoring (can be the hiring manager + one peer).

Step-by-step (busy-person version)

1. 5-minute quick win: Tell the AI the role, responsibilities and the 3 outcomes. Ask for a 2‑sentence candidate summary and 4–5 must-have skills. Save that as your baseline.
2. 30-minute build: For each outcome, ask the AI to list 2–3 observable behaviours that show success and map 1–2 skills to each behaviour. Use those to create a scorecard with four columns: Must-have (3), Nice-to-have (3), Culture (3), Red flags (3).
3. 15–20 minute interview pack: Convert each Must-have into 2 behavioural prompts (STAR-style) and a simple 0–3 rubric: 0=no example, 1=weak, 2=good, 3=measurable impact. Add an “Evidence” field for interviewers to paste quotes or metrics.
4. Calibration & repeat: Require two independent scores per interview; average them and flag >1 point variance for a 5–10 minute calibration chat. After 3 interviews, spend 20 minutes comparing evidence and tightening rubric language.

What to expect

• A usable JD + scorecard baseline in under 45 minutes.
• Interview-ready questions and rubrics in ~60–90 minutes total.
• Noticeably more consistent ratings after the first 3 interviews and one calibration session.

Prompt approach (use conversational frames, not a long copy/paste)

• Inputs: role title, 3 responsibilities, 3 outcomes (be specific).
• Ask for: two-sentence candidate summary; 4–5 must-have skills; for each outcome, 2 observable behaviours; a 4‑column scorecard with 3 items each.
• Variants: Quick (short summary + skills), Detailed (behaviours + rubrics + evidence fields), Calibration (compare 3 interview snippets and suggest rubric tweaks).

Small habit: always tie at least one Must-have to a number or deliverable — hiring becomes less subjective overnight. Try this on one open role this week and iterate after three interviews.

Nice call to keep burnout front-and-center — that focus changes decisions faster than new tools ever will. Here’s a quick win you can do in under 5 minutes to feel in control: a one-line triage that turns a messy list into one clear next action.

What you’ll need: a phone or computer, a notes app (or paper), a timer set for 5 minutes, and an AI assistant you can ask simple questions (chat or voice).

5-minute triage (try it now)

1. Open a fresh note and write the names of your active side projects—one line each. Don’t edit, just list.
2. For each project, write one sentence: the one outcome that would make you feel it’s moving forward this week.
3. Pick the single project that, if it moved forward, would reduce your stress most. Circle it.
4. Set a 15-minute focus block on your calendar today to do one tiny task for that project (not “work on X” but something concrete like “outline 3 bullet points” or “send 1 status message”).

What to expect: Immediate clarity and reduced overwhelm. You’ll usually find 1–2 projects deserve attention this week; the rest are archived or scheduled for review later.

Simple weekly AI-assisted workflow (20 minutes/week)

• Capture (5 min): Dump new ideas, questions, and receipts into one note. Ask your AI to summarize them into three bullets — goals, blockers, and items to delegate.
• Prioritize (5 min): From those bullets, pick up to two projects for the week. Convert each into 1–3 micro-tasks that take 10–30 minutes.
• Schedule & Delegate (5 min): Put those micro-tasks into your calendar as actual appointments. Ask the AI to draft short messages you’ll send to collaborators or contractors (edit before sending).
• Protect (5 min): Block two 90-minute focus blocks a week and a single “no work” day segment. AI can remind you and suggest a short checklist to follow during focus time.

Small wins compound: do the 5-minute triage today, then use the 20-minute weekly workflow next. You don’t need advanced tech — just consistent tiny actions, a timer, and a simple AI helper for summaries and short drafts. Expect calmer weeks, clearer priorities, and fewer late-night scrambles.

Short version: you can get a practical AI-driven sales forecast in a week without a data scientist by turning clean CRM exports into calibrated deal‑level probabilities and a weekly expected‑revenue rollup. Focus on a tiny set of features, a no‑code or simple model, and a weekly reconciliation habit.

What you’ll need, how to do it, and what to expect — a tight workflow for busy people:

1. What you’ll need
   1. CRM export (last 12–24 months): deal ID, current stage, timestamps for stage changes, value, owner, product, last activity date, outcome (won/lost), close date.
   2. A spreadsheet or a no‑code AutoML tool you’re comfortable with.
   3. 15–60 minutes weekly to review top mismatches between reps and the model.
2. Quick setup (first 3 days)
   1. Day 1 — Export and inspect: look for missing dates and duplicates; fix obvious issues in the sheet.
   2. Day 2 — Create 5 simple features: deal age, days in current stage, days since last activity, owner win rate (simple historical %), product win rate.
   3. Day 3 — Run a no‑code model or simple logistic/tree model to predict P(win). If you don’t code, use the AutoML flow in your tool and accept the default model; focus on outputs, not the math.
3. Calibrate, aggregate, and operationalize (week 1 repeatable)
   1. Calibrate probabilities so they’re realistic (compare predicted buckets to actual win % and adjust). Spreadsheets can do a simple bucket calibration.
   2. Aggregate expected revenue by summing value * P(win) and separately calculate expected revenue for this quarter by filtering expected close dates.
   3. Publish a weekly forecast file with deal_id, value, P(win), expected_close_month, expected_revenue. Use it in your pipeline review meeting.
4. What to expect over time
   • Week 1–4: model will catch obvious over/underconfidence — expect rough but actionable probabilities.
   • Month 2–3: tuning features and calibration should cut forecast error noticeably; you’ll spot which reps/deals need coaching.
   • Ongoing: a weekly habit beats a perfect model; recalibrate monthly and retrain if your sales process changes.
5. Common quick fixes
   1. Stale activity: require minimal activity logging and use last_activity_date to penalize dormant deals.
   2. Stage bias: don’t map stages directly to probabilities — let the model learn from outcomes.
   3. Overconfidence: compare rep estimates vs model P(win) weekly and review top 10 deltas in your meeting.

Small, consistent steps win: prioritize cleaning and a weekly reconciliation loop, not a perfect model. Do that and you’ll get predictable improvements in forecast accuracy and clearer coaching targets — fast.

Good point: mapping each AI-generated clause back to a GDPR checklist is the single most useful habit — it turns a shiny draft into a defensible document. I’ll add a tight, no-nonsense micro-workflow you can do in small chunks if you’re juggling day jobs.

Quick 90–120 minute sprint (for busy people)

1. What you’ll need (10 minutes)
   • A one-page data inventory: list types only (email, name, billing, IP, cookies, analytics, support notes).
   • Names or categories of key subprocessors (payment, CRM, analytics).
   • Retention guesses (short labels: 30 days, 13 months, 7 years).
   • Access to your website admin to drop banner text and a simple form.
2. Run the quick draft (20–30 minutes)
   1. Tell your AI the business type, country, and paste the one-page inventory; ask for a short policy, a plain‑language summary, cookie-banner text, and a DSAR intake form template. (Keep it conversational.)
   2. Save outputs as Draft A.
3. Map Draft A to GDPR checkpoints (20 minutes)
   1. Create a two-column list: clause / GDPR item (lawful basis, retention, controller, transfers, rights, consent evidence).
   2. Mark anything you guessed (e.g., retention) as “legal review needed.”
4. Implement minimum tech (20–30 minutes)
   1. Install banner text with an explicit Accept and a Preferences link (no pre-checked boxes).
   2. Add a lightweight DSAR intake page (Name, contact, request type, optional ID upload) that creates a ticket/email.
   3. Create a simple consent log (see fields below) stored with your user records or in a small CSV if you’re solo.
5. Send to legal and monitor (15–20 minutes)
   1. Attach your mapping and flag the 3 highest-risk items (health data, transfers, automated decisions).
   2. Agree on timelines for changes and re-publish the final copy after sign-off.

Minimal consent-log fields (store this for every consent event)

• User identifier (email or internal ID)
• Timestamp (ISO format)
• Banner version or policy version
• Choices made (marketing: yes/no; analytics: yes/no)
• IP address and user-agent

What to expect

1. A usable public policy and banner in one day; a reviewed, defensible version in a week.
2. Early metrics: consent rate and DSAR ticket time — use these to prioritise fixes.
3. Legal review will focus on retention, transfers and any special-category data — expect 1–2 rounds of edits.

Small, clear steps beat perfect plans. Do the 90–minute sprint, log consent properly, then hand the mapped draft to counsel — you’ll be live, safer, and still in control.

Nice, clear question — brand compliance across teams is exactly the kind of problem AI can help simplify without replacing human judgment. Below is a compact do/do-not checklist, then a short, practical workflow you can set up in an afternoon with minimal tech skill.

• Do: Start with a single, short brand rulebook (logo versions, approved fonts, tone bullets, color hexes).
• Do: Use AI to flag likely issues, not to auto-delete or punish — keep humans in the loop.
• Do: Collect examples of correct and incorrect usage to teach the system quickly.
• Do-not: Expect perfection on day one — AI will catch patterns, not context.
• Do-not: Replace the brand manager; use AI to scale routine checks and free their time for judgment calls.

Quick worked example — afternoon setup to catch logo and tone issues on incoming assets:

1. What you’ll need: a one-page brand rule summary (PDF or doc), a shared folder where teams upload assets, a simple AI service that can scan text and images (choose a user-friendly one available in your workspace), and a single reviewer on rotation.
2. How to do it — step-by-step:
   1. Put the one-page brand rules in the shared folder and tell teams to upload any new social posts, PDFs, or ad images there.
   2. Create three quick check items: correct logo version, color in palette, and tone (formal/neutral/friendly). Keep each rule short and measurable — e.g., “only primary logo on social images.”
   3. Use the AI tool to scan new uploads daily. Configure it to flag items that deviate from the rule checklist (you’ll usually toggle checkboxes in the tool’s settings).
   4. Have the reviewer get a daily digest of flagged items, confirm or dismiss each flag, and note recurring mistakes in a shared spreadsheet.
   5. After two weeks, refine the rules based on false positives and share a 1-page “top 5 fixes” for submitters.
3. What to expect: Expect 60–80% useful flags early on, a small number of false positives, and faster reviews over time as you tweak rules. The big win is catching repeat mistakes and training teams with concrete examples rather than long lectures.

Small wins: start with one content type (say social images), automate the flagging, and run a weekly 10-minute review. That rhythm builds trust, reduces rework, and keeps your brand consistent without adding bureaucracy.

Nice call on starting tight: your emphasis on 5–15 keywords and tuning is the golden rule — start small, then expand. That keeps the noise down and makes the AI work useful instead of overwhelming.

Here’s a compact, no-nonsense micro-workflow you can run in a couple of hours and maintain in 20 minutes a day. I’ll list what you need, an exact setup sequence, and what to expect during the first two weeks.

What you’ll need (quick checklist):

• 5–15 priority keywords (brand, product, execs, common misspellings, campaign tags).
• One monitoring entry point (social listening tool, RSS + automation tool, or saved searches).
• An automation connector (Zapier/Make) or built-in integration to send mentions to a sheet or dashboard.
• An AI text classifier (can be a low-cost model) that returns: sentiment, entities, urgency score, and a short recommended action.
• A simple log (Google Sheet or Airtable) and one responder + one reviewer assigned.

Step-by-step setup (do this in order):

1. Create your 10-keyword core list. Rank them 1–10 by business impact — only top 3 get instant alerts at first.
2. Set up monitoring for three source types: one social (X/Twitter), one news/RSS, one forum/review site. Keep it to three to avoid noise.
3. Connect the feed to your automation tool so every mention becomes one row in your sheet: timestamp, source, snippet, URL.
4. Teach the classifier (briefly, not a pasted prompt): ask it to return four outputs — sentiment, entities, urgency 0–100 with short reason, and a one-line recommended action (reply/escalate/archive). Keep the language conversational and limit output to those fields.
5. Create two rules: urgency >70 OR negative + influencer => immediate alert to responder; everything else => daily digest to reviewer.
6. Log every action in the sheet (who responded, time, outcome). Add one column for “false positive?” to speed tuning data collection.
7. On Day 4 and Day 7: review false positives, add exclusions, and drop or demote noisy keywords.

Daily habits and what to expect:

• Daily (10–20 min): responder scans urgent alerts and clears quick replies; reviewer checks digest and flags misclassifications.
• Weekly (30–45 min): tune keywords, adjust threshold by +/–10 points, and review the false positive column.
• Expect a tuning period of 7–14 days with lots of tweaks. Early numbers: aim to reduce false positives below 30% by week two and hit urgent response <60 minutes for critical items.

Small, repeatable steps beat big, unfinished systems. Start with this micro-workflow, measure two simple metrics (false positives and avg response time), and you’ll have a tight, AI-powered monitoring loop that actually saves time instead of creating more work.

Nice call on the 5-minute filter and SMS hold — those are the exact low-effort moves that stop the majority of risky orders. I like the emphasis on single-PDF evidence packets too; that alone makes disputes far easier to win.

Here’s a compact, actionable layer you can add today that stays lightweight for a small team: a one-touch triage + AI-assisted summarizer that saves time and standardizes dispute evidence.

What you’ll need

• Order export (billing, shipping, order value, order ID).
• IP/device, payment transaction ID, tracking number/delivery proof.
• Customer phone/email and recent chat transcripts.
• A simple place to paste data (spreadsheet, helpdesk ticket, or a free AI chat window).

How to do it — step-by-step

1. Set three live flags in your dashboard: billing!=shipping, order_value > 3x avg, velocity (3+ cards or orders from same IP in 24h).
2. When an order is flagged, do a 60–90s verification: send a one-line SMS asking to confirm the delivery address. If they reply within 1 hour, mark OK and ship; if no reply, hold 24h then refund to cut risk.
3. Copy the flagged order data into your spreadsheet or ticket. Use a short AI check: ask for a numeric risk score, 3 short reasons, and one-line recommended action. Keep the wording conversational (see variants below) rather than pasting a long scripted prompt.
4. Create an evidence packet template (one page): order receipt, tracking screenshot, SMS/chat transcript, IP + transaction ID. Export to a single PDF and attach to any gateway dispute.
5. Each week, review: % orders flagged, % false positives (customer verified), and disputes won. Tune your flags to reduce false positives gradually.

AI prompt variants — conversational instructions (not a full script)

• Balanced: Ask the AI to score risk 0–100, list the top 3 concise reasons, and suggest a single next action (ship, hold+verify, cancel).
• Conservative: Ask for higher emphasis on false-positive risk and to suggest verification steps you can do in 60–90 seconds.
• Evidence-ready: Ask the AI to produce a two-line summary of evidence suitable for a dispute PDF (what you have, delivery status, customer contact attempts).

What to expect

• Initially ~10–25% false positives from conservative flags — tune weekly.
• Time saved: AI summarizer reduces manual write-up to ~30s per flagged order.
• Big wins come from stopping a handful of high-ticket frauds — one saved chargeback often covers these processes.

3-day micro-plan for busy people

1. Day 1: Turn on the 3 flags and run the 5-minute filter; verify top 5 flagged orders now.
2. Day 2: Build the one-page evidence PDF template and start attaching it to any open disputes.
3. Day 3: Start using the conversational AI checks on flagged orders and review false positives to tweak flags.

Small, repeatable steps beat big tech projects — protect high-value orders first, standardize evidence, and let simple AI cuts your admin time.