Operations Prompts to Make Better Decisions

You are an ERP selection consultant. Build a weighted evaluation scorecard for comparing ERP systems. Company requirements: - Industry: [Industry] - Size: [Revenue, employees] - Key processes: [List: GL, AP, AR, FA, payroll, inventory, project accounting, etc.] - Current pain points: [What's wrong with current system] - Must-have requirements: [Non-negotiable features] - Budget range: [If known] Build a scorecard with these categories (suggested weights): 1) Functionality fit (30%) — does it do what we need out of the box? 2) Ease of use (15%) — can our team actually use it without constant IT help? 3) Implementation (15%) — timeline, complexity, and risk 4) Total cost of ownership (15%) — license, implementation, annual maintenance, upgrades 5) Vendor stability (10%) — financial health, market position, investment in product 6) Integration (10%) — connects to our other systems? 7) Reporting & analytics (5%) — built-in reporting capabilities For each category: - 3-5 specific evaluation criteria - Scoring scale (1-5) with definitions for each level - Data source (how to evaluate — demo, reference check, documentation) Format: Scorecard template ready to be filled in for each vendor during demos.

You are a Controller. Build a cross-training plan for the finance team to reduce key-person dependency. Team members: [Paste: name/role, primary responsibilities, backup (if any)] Identify: 1) Single points of failure (tasks only one person can do) 2) Critical processes during close (what breaks if someone is out during close?) 3) High-risk knowledge gaps (tribal knowledge not documented) Build a cross-training plan: - Priority processes to cross-train (ranked by risk) - Primary owner and backup trainee for each - Training method (shadow, SOP + practice, formal training) - Timeline (realistic — don't overload people) - Validation (how do we know the backup can actually do it?) - Documentation requirements (SOPs, checklists, video recordings) Also include: - Rotation schedule (if applicable — switch responsibilities periodically) - Emergency runbook (if someone leaves suddenly, what are the first 48 hours?) Format: Cross-training matrix + timeline + emergency runbook.

You are a finance operations analyst. Draft a process improvement proposal. Current process: [Name] Pain points: [Describe what's broken, slow, or error-prone] Data: - Current time spent: [hours per cycle] - Error rate: [if known] - Number of people involved: [count] - Systems used: [list] Draft a proposal: 1) Problem statement (what's wrong, in business terms — cost, time, risk) 2) Current state (how the process works today, step by step) 3) Root cause analysis (why is it broken — process, people, technology, or all three?) 4) Proposed solution (how it should work, step by step) 5) Expected benefits (quantified: hours saved, errors reduced, faster close) 6) Implementation plan (phases, timeline, resources needed) 7) Risks and mitigation 8) Cost (if any investment is required) 9) ROI (payback period) 10) Decision requested (approval to proceed) Tone: Business case format. Persuasive but honest about effort required. Format: 2-page proposal.

You are a revenue operations manager auditing the go-to-market technology stack. Tech stack data: [PASTE: Tool | Category | Users | Annual cost | Integration to CRM (yes/no) | Usage rate (high/medium/low) | Owner | Last evaluated] Audit for: 1. Duplication — tools doing the same job; consolidation opportunity 2. Unused tools — licenses paid for with low adoption; assess value vs. cost 3. Integration gaps — tools not connected to CRM creating manual data entry or blind spots 4. Critical dependencies — tools the business cannot function without; ensure contract continuity 5. Cost optimization — tools that could be replaced by CRM-native features without losing capability Output: Tech stack audit. Consolidation opportunities with estimated savings. Integration gap list. Tools to retire. Tools to protect. Annual savings from recommended changes.

You are a revenue operations manager assessing your organization's readiness to adopt AI-powered CRM features. Current state: [DESCRIBE: CRM system and version, data quality (completeness and accuracy), team size, current manual processes that are candidates for AI, any prior AI tool experiments, executive appetite for AI investment] Assess readiness across: 1. Data quality — AI features require clean, complete data; current completeness and accuracy score 2. Process standardization — AI works best on consistent processes; are your sales and CS processes documented and followed? 3. Tool integration — AI features need data from multiple systems; are your key systems integrated with CRM? 4. Team adoption — will your team use AI recommendations? What is the current CRM adoption rate? 5. Executive sponsorship — is leadership committed to the process change required for AI to work? Output: AI readiness score by category. Gaps to address before AI investment. Recommended first AI use cases given current readiness. Timeline to reach full AI readiness.

You are a VP of Revenue Operations building a 12-month AI automation roadmap for the revenue team. Current state: [DESCRIBE: Current manual processes consuming the most time, data quality status, CRM system, team size by function (SDR/AE/CSM/ops), key pain points that automation could address, budget range for tools] Build the roadmap: Quarter 1 — Quick Wins: - AI-assisted deal summaries and pre-call briefs - Automated follow-up email drafting - Pipeline risk alert automation Quarter 2 — Process Automation: - Lead scoring model update with AI signals - Automated activity logging from email/calendar - AI-generated QBR prep for CS team Quarter 3 — Intelligence Layer: - Conversation intelligence and coaching - Forecast AI using pipeline signals - Account health scoring with predictive churn alerts Quarter 4 — Advanced: - AI-assisted territory and quota planning - Next-best-action recommendations for reps - Automated competitive intelligence monitoring For each initiative: business case / expected time saved or revenue impact / tool or CRM-native feature / dependencies / owner. Output: 12-month AI automation roadmap. Expected ROI per quarter. Quick wins to demonstrate value immediately. Total estimated time saved and revenue impact at full execution.

You are a senior accountant performing period-end reconciliation. Reconciliation data: [PASTE: Subledger name | Subledger balance | GL control account balance | Any known timing items] For each pair: - Calculate variance ($ and %) - Classify cause: timing difference / unposted transaction / manual override / unknown (investigation required) - For variances over $[AMOUNT]: draft the correcting journal entry with accounts and memo - Flag unexplained variances with a specific next step Output: Reconciliation workpaper. Sign-off line: Reconciled / Partially reconciled / Unreconciled — escalation required. Tone: Audit-ready.

You are a controller reviewing journal entries for the period. Journal entry log: [PASTE: JE number | Preparer | Approver | Post date | Amount | Debit account | Credit account | Description] Materiality threshold: $[AMOUNT] Flag entries meeting any of these criteria: 1) Round dollar amounts over materiality 2) Posted on a weekend, holiday, or after period-end cutoff 3) Same person is preparer and approver 4) Description uses vague language — "adjustment", "misc", "true-up" with no further detail 5) Unusual account pairing (e.g., debit to revenue, direct credit to equity) 6) Same amount + same accounts within 7 days — potential duplicate 7) Posted by someone who doesn't normally access these accounts For each flagged entry: explain why it's flagged, assign risk (Low/Medium/High), recommend follow-up action. Output: Risk-ranked list, highest first. Summary: X entries reviewed, Y flagged, Z require action before close can be certified.

You are a finance process analyst reviewing the close process for improvement opportunities. Current close process: [DESCRIBE: Each close step — who does what, how long it takes, what tool they use, known pain points] Example format: - Step 1: GL cutoff (Day 0, 2 hours, manual, error-prone) - Step 2: Reconcile balance sheet accounts (Days 1–3, 8 hours, spreadsheet-based) - Step 3: Variance explanations (Day 4, 3 hours, drafted in Word) For each step, recommend: - Automation opportunity (AI, RPA, ERP native feature, or third-party tool) - Estimated time saved per period - Implementation complexity (low/medium/high) - Required controls to maintain if automated Prioritize: Highest time savings + lowest implementation complexity. Output: Improvement roadmap table. Add a summary: current total close time vs. target close time with recommended changes.

You are a controller reviewing accounts payable. AP aging data: [PASTE: Vendor | Invoice # | Invoice date | Amount | Due date | Aging bucket (Current/1-30/31-60/61-90/90+)] Current cash balance: $[AMOUNT] Produce: 1) Aging summary — total payables by bucket with % of total 2) Top 10 vendors by outstanding balance — flag any with invoices 60+ days overdue 3) Duplicate invoice check — same vendor + same amount ±5% + dates within 7 days 4) Cash exposure — payables due in next 14 days vs. current cash balance 5) Vendor risk flags — vendors with past-due payables AND open purchase orders Output: Executive memo. End with 3 highest-priority actions this week. Tone: Direct, no filler.

You are a senior FP&A analyst building a rolling cash forecast. Inputs: - AR aging: [PASTE OR DESCRIBE — expected collection timing] - AP schedule: [PASTE OR DESCRIBE — upcoming payments] - Recurring monthly expenses: [LIST key items and amounts] - Payroll cycle: [FREQUENCY and approximate amount] - Minimum cash threshold: $[AMOUNT] Build a 13-week forecast with columns: Week | Beginning Cash | Cash In (AR collections + other) | Cash Out (AP + payroll + recurring + discretionary) | Net Cash Flow | Ending Cash. Flag any week where ending cash drops below the minimum threshold. Below the table: list your top 3 assumptions and rate each as high/medium/low confidence. If there's a projected shortfall, suggest 2 realistic options to bridge the gap. Output: 13-week table + assumptions + shortfall actions (if applicable).

You are an AP manager reviewing the weekly payment run before release. Payment batch data: [PASTE: Vendor | Invoice # | Amount | Due date | Payment method | Bank account on file] Review for: 1) Payments to vendors not in the approved vendor master — flag for confirmation 2) Payments where bank account changed in the last 30 days — high fraud risk, require secondary approval 3) Invoices without a matching approved PO — flag with amount 4) Duplicate payments — same vendor + same amount within 60 days 5) Early payments where no early payment discount applies — potential cash optimization opportunity 6) Payments over $[THRESHOLD] — confirm secondary approval is on file Output: Payment run approval report. Clear section for: Approved / Requires review before release / Do not pay (reason).

You are a procurement analyst reviewing vendor spend for [PERIOD]. Spend data: [PASTE: Vendor | Category | Invoice total | Number of invoices | Department | Payment terms] Analyze: 1) Total spend by vendor — top 10 by amount 2) Spend by category — flag any category where spend increased >20% vs. prior period 3) Department spend breakdown — which departments are driving the largest payables? 4) Payment terms compliance — vendors where average days to pay exceeds agreed terms 5) Consolidation opportunities — categories with 3+ vendors where spend could be rationalized Output: Spend analysis report. Top 3 recommendations for cost reduction or process improvement.

You are a treasury analyst evaluating early payment discount opportunities. Invoice data: [PASTE: Vendor | Invoice amount | Payment terms (e.g., 2/10 net 30) | Invoice date | Current date] For each invoice with discount terms: - Calculate the annualized return of taking the discount - Compare to your current cost of capital or short-term borrowing rate: [RATE]% - Recommend: take discount / pass / borderline (explain) Also flag: - Invoices where discount deadline is within 5 days - Total cash required to capture all available discounts - Net savings if all recommended discounts are captured Output: Decision table — Vendor | Invoice | Discount Amount | Annualized Return | Recommendation. Summary: total savings available, total cash required, net recommendation.

You are an AP specialist triaging invoices that failed automated 3-way match. Exception data: [PASTE: Invoice # | Vendor | Invoice amount | PO amount | Receipt amount | Variance $ | Variance % | Exception reason] For each exception: - Classify root cause: price variance / quantity variance / missing receipt / PO closed / duplicate / coding error - Recommend resolution: approve with override (if variance < $[THRESHOLD]) / request vendor credit / contact receiver / reject invoice / escalate to management - Estimate resolution time (same day / 2–3 days / 1 week+) Flag: Any exception over $[AMOUNT] or unresolved >5 business days — these need manager escalation. Output: Exception triage table sorted by dollar amount. Summary: total exceptions value, % resolvable today, items requiring escalation.

You are a procurement manager preparing for vendor payment terms discussions. Vendor data: [PASTE: Vendor | Annual spend | Current payment terms | Industry standard terms | Years as vendor | Any disputes or quality issues] For each vendor: - Assess negotiation leverage: spending volume / length of relationship / alternative suppliers available - Recommend target terms: current terms → proposed terms - Calculate cash flow impact of proposed change (annual $ improvement) - Note any trade-offs: risk of pricing increase if terms extended Prioritize: Vendors where cash flow improvement is largest relative to relationship risk. Output: Negotiation prep table — Vendor | Current Terms | Target Terms | Annual Cash Impact | Leverage Level | Talking Points. End with total cash flow improvement if all proposals succeed.

You are a finance process analyst reviewing AP workflows for automation potential. Current AP process: [DESCRIBE: Invoice receipt method, data entry process, approval workflow, payment process, average invoices per month, average FTEs handling AP] For each AP process step, assess: - Current tool/method used - Error rate and common failure points - Automation opportunity: AI extraction / ERP workflow / RPA / no automation possible - Estimated time savings per 100 invoices - Implementation effort (low/medium/high) Output: AP automation roadmap — process step, current state, recommended tool, time savings, priority. End with: estimated total FTE hours saved per month if all recommendations implemented.

You are an AP auditor reviewing payment history for potential duplicate payments. Payment data: [PASTE: Vendor name | Invoice # | Amount | Date paid | PO # | Payment method] Check for: 1) Same invoice number paid twice (exact match) 2) Same amount to same vendor within 30 days (possible duplicate) 3) Similar invoice numbers — transposition errors (INV-1023 vs INV-1032) 4) Same amount, different vendor names that look similar (DBA variations) 5) Round-number payments without matching invoices For each potential duplicate: - Confidence level (high/medium/low) - Evidence supporting the flag - Recommended action (investigate, recover, or dismiss) Output: Ranked by $ amount. Highest recovery opportunity first.