[Ian Investor]

I have a list of possible research questions and limited time to explore them. I’m not technical, but I’d like to focus on the few that are most likely to have real impact (academic, practical, or social).

Can anyone share simple, non‑technical ways to use AI to help rank or prioritize questions by impact? I’m especially interested in:

• What tools or services are beginner-friendly (chatbots, web apps, free tools)?
• What inputs to give the AI (question text, short background, intended audience)?
• Sample prompts or step-by-step examples I could copy and use.
• How to check that the AI’s suggestions make sense (simple validation or common pitfalls).

If you’ve used AI to prioritize research or decide what to study next, please share a brief example or a prompt that worked for you. Clear, practical tips for a non‑technical person would be most helpful. Thank you!

[aaron]

Quick win (5 minutes): Paste your top 10 research questions into a single document and run this one AI prompt (below). You’ll get an initial ranked list and short rationale you can act on immediately.

The problem: Teams generate lots of research questions but lack a repeatable way to know which will move the needle. That wastes budget, time and stakeholder credibility.

Why this matters: Prioritizing by likely impact focuses limited resources on work that changes decisions, product direction or revenue — not just curiosity-driven findings.

Experience / lesson: I’ve run prioritization workshops where a simple AI-assisted scoring reduced research backlog by 60% and shortened decision time from weeks to days.

1. What you’ll need
   • A list of research questions (10–50).
   • A spreadsheet (columns: question, impact score, confidence, effort, recommended next step).
   • Access to a general-purpose AI assistant (chat box or API).
2. Step-by-step
   1. Define 3 simple criteria: Potential Impact (strategic/value), Feasibility (data/time/cost), and Confidence (existing evidence). Assign weights (e.g., 50/30/20).
   2. Paste questions into the AI and ask it to score each criterion 1–10, calculate weighted score, and recommend a next step (run user test, analyze logs, prototype).
   3. Import results into your spreadsheet, sort by weighted score, and share the top 5 with stakeholders for quick validation.

What to expect: A ranked list with short rationales for each score. Use AI output as decision input — not an automatic decision.

Copy-paste AI prompt (use as-is)

“I will paste a list of research questions. For each question, score three criteria from 1 (low) to 10 (high): 1) Potential impact on business or user outcomes, 2) Feasibility given typical resources (data, time, cost), 3) Confidence based on existing evidence. Use weights: impact 50%, feasibility 30%, confidence 20%. Return a CSV with columns: question, impact_score, feasibility_score, confidence_score, weighted_score, two-sentence rationale, recommended next step (user interview / prototype / analytics / no further action).”

Metrics to track

• Time from question to prioritized decision (days).
• % of top-5 prioritized questions executed within quarter.
• Conversion of prioritized research to measurable outcomes (product changes, revenue impact).

Mistakes & fixes

• Over-reliance on AI: fix by adding a 10-minute human review step.
• Poor criteria: fix by reweighting after one iteration based on outcomes.
• Biased inputs: fix by diversifying question sources and anonymizing where possible.

1-week action plan

1. Day 1: Gather questions.
2. Day 2: Define criteria and weights.
3. Day 3: Run AI scoring and import to spreadsheet.
4. Day 4: 15-minute stakeholder review of top 5.
5. Day 5–7: Kick off 1–2 prioritized studies and track progress.

Your move.

— Aaron Agius

[Rick Retirement Planner]

Nice concise workflow — Aaron’s quick-win is exactly right: use a simple weighted scoring method plus a short human review to turn a long backlog into an actionable top list. That clarity builds confidence and saves time.

Here’s a practical, reassuring add-on that keeps the math simple and the team engaged.

• Do
  • Keep criteria to 3 clear items (Impact, Feasibility, Confidence) and fix weights for an iteration (e.g., 50/30/20).
  • Run an AI pass to score each question, then do a 10-minute human review of the top 8–10.
  • Track a small set of metrics (time-to-decision, % executed, outcome conversion) and adjust weights after one cycle.
• Don’t
  • Blindly accept AI output — use it as an assistant, not a decider.
  • Use too many criteria or tiny weights that make scores noisy.
  • Ignore bias sources; diversify who submits questions and anonymize where helpful.

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

1. What you’ll need
   • A list of 10–50 research questions.
   • A simple spreadsheet with columns: question, impact, feasibility, confidence, weighted_score, recommendation.
   • Access to an AI assistant for quick scoring and a decision owner for the human review.
2. How to do it
   1. Agree weights (example: Impact 50%, Feasibility 30%, Confidence 20%).
   2. Ask the AI to score each question 1–10 on the three criteria and provide a one-line rationale and next-step suggestion.
   3. Calculate weighted_score = impact*0.5 + feasibility*0.3 + confidence*0.2 and sort the sheet.
   4. Run a 10–15 minute stakeholder review of the top 5–8 to confirm or swap priorities and note reasons.
3. What to expect
   • A ranked list with short rationales and recommended next steps — fast wins up top, riskiest but high-impact items flagged for human judgement.
   • One iteration usually reveals if weights or criteria need tweaking; expect to refine after 1–2 cycles.

Worked example (small, concrete)

1. Three sample questions:
   • Q1: “Will simplifying signup increase paid conversions?”
   • Q2: “Do users find feature X intuitive?”
   • Q3: “What content drives retention in week 1?”
2. AI scores (1–10) and calculation (weights 50/30/20):
   • Q1: Impact 9, Feasibility 7, Confidence 4 → weighted = 9*0.5 + 7*0.3 + 4*0.2 = 6.9
   • Q2: Impact 6, Feasibility 8, Confidence 6 → weighted = 6*0.5 + 8*0.3 + 6*0.2 = 6.8
   • Q3: Impact 8, Feasibility 5, Confidence 5 → weighted = 8*0.5 + 5*0.3 + 5*0.2 = 6.5
3. Action: review top two (Q1, Q2). For Q1 run an experiment/prototype; for Q2 run a 5–8 user test. Log outcomes and revisit weights if results don’t match predicted impact.

One clear concept in plain English: weighted scoring turns diverse judgments into a single helpful number, but that number is a conversation starter — not the final vote. Use it to focus human time where it changes decisions.

[Jeff Bullas]

Quick hook: You’ve got a backlog of research questions — smart, but messy. Use AI to turn that backlog into a short list of questions that will actually move the needle. Fast. Repeatable. Human-led.

Why this tweak matters

• AI speeds scoring; humans keep the judgment.
• Small changes (normalizing scores, flagging uncertainty, tracking outcomes) make your prioritization reliable across cycles.

What you’ll need

1. A list of 10–50 research questions (one per line).
2. A spreadsheet with columns: question, impact, feasibility, confidence, weighted_score, uncertainty_flag, recommended_next_step.
3. Access to an AI assistant (chatbox or API) and one decision owner for a 10–15 minute review.

Step-by-step (do this now)

1. Agree the criteria and weights. Example: Impact 50%, Feasibility 30%, Confidence 20%.
2. Run the AI pass to get raw scores (1–10), a short rationale, a next-step suggestion and an uncertainty flag when evidence is weak.
3. Normalize scores if needed (AI sometimes clusters values). For each criterion, scale scores so min=1 and max=10.
4. Calculate weighted_score = impact*0.5 + feasibility*0.3 + confidence*0.2. Add uncertainty_flag to highlight where human review is essential.
5. Sort and run a 10–15 minute stakeholder review on the top 8–10. Confirm or swap and capture reasons.
6. Kick off the top 1–2 experiments and track outcomes against expected impact.

Copy-paste AI prompt (use as-is)

“I will paste a numbered list of research questions. For each question, score three criteria from 1 (low) to 10 (high): 1) Potential impact on business or users, 2) Feasibility given typical resources (data, time, cost), 3) Confidence based on existing evidence. If evidence is thin, set an uncertainty_flag to TRUE and explain what’s missing. Use weights: Impact 50%, Feasibility 30%, Confidence 20%. Return a CSV with columns: question, impact_score, feasibility_score, confidence_score, weighted_score, uncertainty_flag, two-sentence rationale, recommended next step (user interview / prototype / analytics / no further action). Briefly note any strong assumptions you made.”

Worked mini-example

1. Q1: “Will simplifying signup increase paid conversions?” → Impact 9, Feasibility 7, Confidence 4, weighted 6.9, uncertainty TRUE (no A/B history) → next step: A/B test.
2. Q2: “Do users find feature X intuitive?” → Impact 6, Feasibility 8, Confidence 6, weighted 6.8 → next step: 5–8 user tests.

Mistakes & fixes

• Relying solely on AI: always do a short human review to catch context and bias.
• Poor inputs: anonymize and diversify question sources to reduce political bias.
• No follow-up metrics: track time-to-decision and % of top-5 executed and returned outcomes; adjust weights after one cycle.

7-day action plan

1. Day 1: Gather questions and agree weights.
2. Day 2: Run AI scoring and normalize results.
3. Day 3: Stakeholder 15-minute review and final top 5.
4. Days 4–7: Start 1–2 prioritized studies, log outcomes, and review weights at week end.

Closing reminder: Treat AI output as a powerful assistant — not an oracle. Use the scores to focus human time on the few studies that will actually change decisions.

[aaron]

5-minute win: Paste your top 10 research questions into the prompt below. You’ll get a ranked list with impact, cost-to-learn, time-to-signal, and a simple “do next” for each. Share the top three with your decision-maker before lunch.

The problem: A long backlog hides the 2–3 questions that actually drive revenue, retention, or risk reduction. Opinions take over. Momentum dies.

Why this matters: Prioritizing by likely impact and time-to-signal turns research into decisions that move KPIs this quarter — not just insights that sound smart.

Lesson from the field: Adding two things — cost-to-learn and time-to-signal — doubled throughput in one team I advised. Same people, same tools, clearer choices. Exec buy-in went up because the top items were fast, cheap, and tied to a KPI.

1. What you’ll need
   • Your backlog (10–50 questions, one per line).
   • A spreadsheet with columns: question, impact, feasibility, confidence, weighted_score, time_to_signal_days, cost_to_learn, uncertainty_flag, recommendation.
   • Access to an AI assistant and 15 minutes with a decision owner.
2. How to do it (step-by-step)
   1. Set anchors (2 minutes): Pick one KPI to optimize (e.g., paid conversion, week-1 retention, cost-to-serve) and a time horizon (30–90 days). Note any hard constraints (no new data infra, budget cap).
   2. Run the AI pass (5 minutes): Use the prompt below to score Impact, Feasibility, Confidence; add time_to_signal (days to first learn) and cost_to_learn (rough dollars or team-days). AI returns a CSV you can paste straight into your sheet.
   3. Calculate a priority number (2 minutes): weighted_score = impact*0.5 + feasibility*0.3 + confidence*0.2. Then create speed_factor = if time_to_signal_days ≤ 14, add +1 to the priority; if ≤ 7, add +2. Final_priority = weighted_score + speed_factor.
   4. Segment the list (3 minutes):
      • Now: Final_priority ≥ 8 or time_to_signal ≤ 7 days.
      • Next: Final_priority 6–7.9.
      • Later: Everything else, unless uncertainty_flag is TRUE and cost_to_learn is low (these can be small probes).
   5. 15-minute review agenda: Present the top 5 with one-line rationale and a decision ask. Agree owners and start dates. Capture any swaps and why — that becomes your learning loop.
3. What to expect
   • A clean top 3–5 tied to a KPI, with clear next steps (A/B test, 5–8 user tests, log analysis, or “no further action”).
   • Faster starts: items with time_to_signal ≤ 7 days become immediate candidates.
   • Better trade-offs: high-impact but uncertain items get a cheap probe first, not a month-long study.

Copy-paste AI prompt (premium template)

“I will paste a numbered list of research questions and one target KPI. For each question, do the following and return a CSV: 1) Score Impact on the KPI (1–10), 2) Score Feasibility with typical resources (1–10), 3) Score Confidence based on existing evidence (1–10), 4) Estimate time_to_signal_days to get a directional answer, 5) Estimate cost_to_learn (team-days or $ rough order), 6) Set uncertainty_flag TRUE if evidence is thin and name what’s missing, 7) Recommend the next step (user interview / prototype / analytics / A/B test / no action). Use weights: Impact 50%, Feasibility 30%, Confidence 20% to compute weighted_score. Also compute final_priority = weighted_score + 2 if time_to_signal_days ≤ 7, +1 if ≤ 14, else +0. Include a two-sentence rationale and any strong assumptions. Columns: question, impact, feasibility, confidence, weighted_score, time_to_signal_days, cost_to_learn, uncertainty_flag, rationale, recommended_next_step, final_priority.”

Insider trick: calibrate the AI before scoring

Run two “anchor” questions you already know the outcome for (one high, one low). If the AI’s scores don’t match reality, tell it how to adjust (e.g., “increase weight on Feasibility for infra-heavy items”), then run your full list. This keeps scores grounded in your context.

Optional micro-prompt (calibration)

“Here are two anchor questions with known outcomes and why. Adjust your scoring guidelines so similar items receive comparable scores. Confirm the adjusted rules in 5 bullet points, then ask me to paste the full list.”

Metrics that prove this works

• Decision lead time: days from backlog to top-5 sign-off.
• Time-to-signal: days to first directional answer on top 2.
• Execution rate: % of top-5 started within 7 days.
• Decision impact: % of prioritized items that triggered a product/marketing change.
• Forecast accuracy: ratio of expected impact vs realized (aim to get within 20% after two cycles).

Mistakes and easy fixes

• Scores bunch at 7–8: Ask AI to normalize so the lowest item gets ~3 and the highest ~9; or force at least one 9 and one 3.
• Pet projects creep in: Require a one-line KPI link for any swap. No KPI, no swap.
• Feasibility optimism: Add a quick tech check by the implementer before finalizing the top 3.
• No learning loop: After each study, record the actual time_to_signal and impact to recalibrate the next scoring pass.

1-week action plan

1. Day 1: Pick your KPI and paste your questions into the prompt. Get the CSV and create your sheet.
2. Day 2: Calibrate with two anchors. Re-run the list if needed. Add speed bonuses and segment Now/Next/Later.
3. Day 3: 15-minute decision review. Lock the top 3, owners, and start dates.
4. Days 4–5: Launch 1–2 quick studies (≤7 days to signal). Log assumptions and expected outcomes.
5. Days 6–7: Capture first signals, update the sheet with actuals, and adjust any items in Next.

Make the backlog serve the KPI, not the other way around. Your move.

— Aaron Agius

[Jeff Bullas]

Shortcut to better choices: Don’t just rank questions — tie each one to a decision you’ll actually make. Then let AI estimate the value of learning that answer now versus later.

You’ve already got Impact, Time-to-signal, Cost-to-learn. Great. Add one simple layer: decision-first framing and a quick “expected value of learning” score. This keeps shiny ideas out and pulls forward the few questions that trigger a real move on your KPI.

What you’ll need

• Your backlog (10–50 questions) and one target KPI for this cycle.
• A sheet with these columns: question, decision_if_yes, decision_if_no, impact_1–10, feasibility_1–10, confidence_1–10, time_to_signal_days, cost_to_learn_days, probability_actionable_0–1, reversibility, exposure, edl_score, final_priority, recommendation.
• 10–15 minutes with a decision owner for a quick review.

Step-by-step (decision-first + expected value of learning)

1. Frame the decision (1–2 minutes per item)
   • Rewrite each question as: “If true, we will ____. If false, we will ____.” That’s the decision_if_yes / decision_if_no.
   • Add a simple acceptance threshold: “We act if the uplift is ≥ X%” or “We act if 70% of users succeed in task Y.”
2. Run an AI scoring pass (use the prompt below)
   • Keep your three core scores (Impact, Feasibility, Confidence), plus Time-to-signal and Cost-to-learn.
   • Ask the AI to estimate probability_actionable — the chance you’ll get a clear “yes/no” within the time horizon.
3. Compute a simple expected value of learning (EDL)
   • Use this plain formula: edl_score = (impact × probability_actionable × time_factor) − cost_factor.
   • time_factor: 1.2 if ≤ 7 days, 1.1 if ≤ 14, else 1.0.
   • cost_factor: cost_to_learn_days ÷ 5 (cap at 3). Keep it simple; you’re comparing, not forecasting Wall Street.
4. Gate by risk
   • If reversibility = low and exposure = high, require confidence ≥ 7 or run a small probe first.
   • Everything else: rank by edl_score + your weighted score (Impact/Feasibility/Confidence) and review the top 5–8.
5. Lock the shortlist (15-minute review)
   • Show top 5 with one-line decision and the next step. Agree owners and start dates. Note any swaps and why.

Copy-paste AI prompt (decision-first + EDL)

“I will paste a numbered list of research questions and one target KPI. For each question, do the following and return a CSV I can paste into a spreadsheet: 1) Rewrite the question into decisions: decision_if_yes and decision_if_no (one line each). 2) Score Impact on the KPI (1–10), Feasibility (1–10), Confidence given current evidence (1–10). 3) Estimate time_to_signal_days (days to a directional answer) and cost_to_learn_days (team-days). 4) Estimate probability_actionable (0.2–0.9) = chance we’ll get a clear ‘act / don’t act’ answer within the time horizon. 5) Flag reversibility (low/medium/high) and exposure (low/medium/high) with a one-line reason. 6) Compute edl_score = (impact * probability_actionable * time_factor) − cost_factor, where time_factor = 1.2 if time_to_signal_days ≤ 7, 1.1 if ≤ 14, else 1.0, and cost_factor = min(3, cost_to_learn_days / 5). 7) Recommend the next step (user interview / prototype / analytics / A/B test / small probe / no action) and include a two-sentence rationale. Columns: question, decision_if_yes, decision_if_no, impact, feasibility, confidence, time_to_signal_days, cost_to_learn_days, probability_actionable, reversibility, exposure, edl_score, recommended_next_step, rationale. Also note any strong assumptions in 1 short phrase.”

What to expect

• A ranked, decision-ready top 3–5 with clear “if-then” moves and cheap first steps.
• Fast cycles: items with high EDL and ≤ 7 days to signal jump to the front.
• Fewer dead-ends: low EDL plus high cost gets parked or turned into a tiny probe.

Worked mini-example

1. Q1: “Will simplifying signup increase paid conversions?” → decision_if_yes: ship simpler flow; decision_if_no: keep current, fix copy only.
   • impact 9, feasibility 7, confidence 4, time 7, cost 4 days, probability_actionable 0.6 → edl ≈ (9×0.6×1.2) − (4/5=0.8) = 6.48 − 0.8 = 5.68 → next step: A/B test.
2. Q2: “Do users find feature X intuitive?” → decision_if_yes: expand rollout; decision_if_no: redesign onboarding.
   • impact 6, feasibility 8, confidence 6, time 5, cost 2 days, probability_actionable 0.7 → edl ≈ (6×0.7×1.2) − (2/5=0.4) = 5.04 − 0.4 = 4.64 → next step: 5–8 user tests.
3. Q3: “What content drives week-1 retention?” → decision_if_yes: prioritize top content; decision_if_no: pause content work.
   • impact 8, feasibility 5, confidence 5, time 21, cost 8 days, probability_actionable 0.4 → edl ≈ (8×0.4×1.0) − (8/5=1.6) = 3.2 − 1.6 = 1.6 → next step: small probe (log analysis) before a full study.

Insider tricks

• Decision cards: For each top item, keep a one-liner: “If true we will __; if false we will __; threshold is __.” It kills vague debates.
• Anchor and adjust: Run two known items (one obvious win, one obvious low) first. If scores feel off, tell the AI how to shift (e.g., “reduce feasibility for data-engineering-heavy items”). Re-run the list.
• Speed bonus: When two items tie, pick the one with shorter time-to-signal. Momentum compounds.

Mistakes and quick fixes

• Vague decisions: Force the “If yes/If no” line. No clear action, no research.
• Optimistic probabilities: Calibrate with two anchors and cap early-cycle probability_actionable between 0.3–0.7.
• Ignoring reversibility: If the decision is hard to undo and exposure is high, run a cheap probe first.
• Metric drift: Tie each item to one KPI. No KPI, no slot in the top 5.

1-week action plan

1. Day 1: Pick KPI and paste your questions into the prompt. Get the CSV into your sheet.
2. Day 2: Add the “If yes/If no” lines and acceptance thresholds for the top 8–10.
3. Day 3: 15-minute review. Lock top 3, owners, and start dates.
4. Days 4–5: Launch 1–2 quick studies (≤ 7 days to signal). Track assumptions.
5. Days 6–7: Capture first signals, compare to expectations, and adjust probability/feasibility rules for the next cycle.

Closing nudge: Let decisions pull the research, not the other way around. When every question ends with a clear “If yes/If no,” AI can prioritize what matters — and you move faster with confidence.

[Author]

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