Westmoor MFG — AI Opportunity Audit

A Custom Engagement by Sunzi.io

A Note to the Westmoor MFG Leadership Team

This audit framework was built specifically for Westmoor MFG — not pulled from a template, not recycled from another client. Every question, every assessment dimension, and every department-specific questionnaire was designed around what we learned in our initial discovery sessions about your business, your technology landscape, and your team.

That said — this is a starting point, not a finished product.

We need your input before we deploy this to your department heads. Specifically:

Do the 12 departments listed here accurately reflect your organizational structure? Are there teams, functions, or groups we’re missing — or areas we’ve combined that should be separate?
Are the focus areas for each department on target? We’ve tailored questions based on our discovery conversations, but you know your business better than we do.
Is there anything happening in the business right now — reorganizations, new systems, seasonal pressures, strategic initiatives — that should inform how we approach this?

This is your audit, built to serve your business. The more we calibrate it before it hits your teams, the sharper the results will be.

What This Engagement Delivers

A comprehensive, prioritized AI roadmap for Westmoor MFG — grounded not in theory, but in the actual realities of your people, your workflows, your technology, and your data.

By the end of this process, you will have:

A clear picture of where AI can create the most value across your organization — ranked by business impact, feasibility, and ROI
An honest assessment of your readiness — what’s possible now, what requires groundwork, and what to sequence for later
Specific, actionable recommendations — not buzzwords, not vendor pitches — real solutions mapped to real problems
A phased implementation roadmap with quick wins, medium-term initiatives, and strategic plays

Our Assessment Framework

Most AI audits focus narrowly on technology: “What software do you use? Here’s what AI tools you should buy.” That approach fails because it ignores the three other dimensions that determine whether AI actually works in practice.

We evaluate every department across four pillars:

👥 People

The right AI solution with the wrong team will fail every time.

We assess team structure, skills gaps, key-person dependencies, cross-department collaboration, and change readiness. If your best demand forecaster’s entire methodology lives in their head and nowhere else, that’s a risk we need to understand before recommending AI-powered forecasting. If a department is already overwhelmed with their current workload, adding new technology without addressing capacity will make things worse, not better.

What we’re looking for: Where institutional knowledge is concentrated. Where skills gaps exist. Where teams are stretched thin. Who’s ready for change and who’ll need support.

⚙️ Processes

AI can’t optimize a process you haven’t mapped.

We ask each department to describe their actual recurring workflows — not what the org chart says should happen, but what really happens day to day. Where work slows down. Where handoffs break. Where someone spends four hours a week on something that should take twenty minutes.

Importantly, we don’t prescribe processes. We don’t walk in assuming we know how your Sourcing team evaluates vendors or how your Production team tracks WIP. We ask open-ended questions and let your people tell us how their work actually flows. That’s where the real insight lives.

What we’re looking for: Bottlenecks, manual work that should be automated, broken handoffs between departments, undocumented tribal knowledge, and processes ripe for AI-driven improvement.

💻 Technology

AI tools are only as good as the infrastructure they plug into.

We catalog what systems each department uses, how well those systems talk to each other, where data gets stuck in silos, and where the biggest technology frustrations live. We already know the broad strokes from discovery — Microsoft Suite, Centric PLM, Momentous ERP, underutilized Copilot licenses — but each department experiences the tech stack differently.

What we’re looking for: Integration gaps, data silos, tools that are loved vs. tolerated vs. hated, shadow IT, and infrastructure constraints that shape what’s possible.

📊 Data Quality

This is the dimension most audits skip entirely — and it’s the one that matters most.

AI is only as good as the data it’s trained on. If historical sales data has gaps, if product specs live in three different systems with three different versions, if vendor pricing is tracked in someone’s personal spreadsheet — the most sophisticated AI in the world won’t produce reliable results.

We assess what data each department generates, where it lives, how clean and consistent it is, whether there’s a single source of truth or competing versions, and what data is missing entirely. This determines which AI opportunities are viable today vs. which require data cleanup first.

What we’re looking for: Data that’s AI-ready vs. data that needs work. Governance gaps. The difference between what’s in the system and what’s in someone’s head.

How the Assessment Works

Each department head invests approximately 35-45 minutes total in a two-step process:

Step 1 — AI-Guided Brainstorm Session (20-30 minutes)

Before they touch the questionnaire, each department head spends time with a custom AI brainstorm partner we built specifically for Westmoor MFG. This is not a survey, not an interview, and not a data collection tool. It’s a creative thinking session.

The brainstorm partner’s job is to surface what a form can’t ask: - The frustrations nobody talks about because they seem too small or too complicated to explain - The cross-department dependencies that create friction nobody’s addressed - The institutional knowledge living in someone’s head that represents real organizational risk - The “if I had a magic wand” vision for what their department could look like

The brainstorm partner formats its outputs so they paste directly into labeled fields in the questionnaire. This means the structured form gets enriched with insights the department head wouldn’t have articulated without the thinking session.

The tone is intentionally warm, empowering, and a little fun. We want department heads to feel like this audit is for them — a chance to surface the ideas they’ve never had a forum to share. Not a compliance exercise.

Step 2 — Department Questionnaire (10-15 minutes with brainstorm outputs ready)

Each department has a tailored questionnaire — not a generic template. The Marketing questionnaire asks about campaign workflows and content operations. The Tech Design questionnaire asks about PLM limitations and spec accuracy. The Merchandise Planning questionnaire goes deep on forecasting methodology and Excel dependency.

Questionnaire design standards: - Every multi-select question allows “select all that apply” — no artificial limits - Every multi-select includes “Other (please describe)” for write-in responses - Process questions are open-ended: “What are your major recurring workflows?” — not prescribed - Every section includes a labeled field for brainstorm outputs - Every section ends with a safety net: “What should we have asked about this that we didn’t?” - Every questionnaire closes with two final questions: - “What have we NOT asked that we SHOULD have?” - “If you had a magic wand — perfect people, processes, technology, data — what does that look like?”

The first catches our blind spots. The second gets them envisioning the ideal state — not just identifying problems, but articulating what “great” looks like for their department.

Departments Under Assessment

Based on our discovery sessions and the calibration meeting with leadership, we’ve identified 12 departments plus the Executive Team for assessment. These reflect an organization that designs domestically and manufactures entirely offshore — India, China, and other international locations — which shapes how nearly every department operates, from Sourcing and Tech Design communicating specs across time zones, to Buying/Purchasing managing ocean freight and customs, to Warehouse performing quality inspection on international arrivals.

#	Department	Key Assessment Focus
⭐	Executive Team	Team effectiveness, AI strategy & prioritization, cross-department visibility, implementation roadmap, audit feedback
1	Marketing	Campaign workflows, content repurposing, performance analytics, customer data access
2	Design	Creative processes, trend research, design archive, sales-to-design feedback loop
3	Tech Design	Spec creation, PLM management, fit tracking, vendor specs, data entry burden
4	Sourcing	Vendor management, cost analysis, compliance tracking, offshore manufacturing coordination
5	Buying/Purchasing	PO lifecycle, shipment tracking, delivery reconciliation, international logistics coordination
6	Credit	Credit applications, limit management, AR aging, collections, risk assessment
7	Customer Service	Inquiry handling, product knowledge access, issue categorization, customer history
8	HR	Recruiting, onboarding, training, compliance, workforce analytics
9	Merchandise Planning	Demand forecasting, buy planning, Excel dependency, data quality for AI readiness
10	Finance	Close processes, reconciliation, reporting, product-level profitability
11	Sales	Order management, rep coordination, account knowledge retention, reporting
12	Warehouse	International receiving, QC inspection, inventory management, order fulfillment, returns processing

Process: The Executive Team completes their assessment first — experiencing the full flow (AI Companion brainstorm → questionnaire) before it rolls out to department heads. This lets leadership provide feedback on the process itself, and the Executive Questionnaire includes a dedicated section for that feedback.

The Engagement Timeline

Phase	What Happens	Duration	Status
Discovery	Leadership meeting to understand the business, tech stack, known pain points, and strategic priorities	Complete	✅
Calibration	Review this framework with leadership. Validate departments, adjust focus areas, confirm participants	This meeting	🔄
Department Assessment	Each department head completes brainstorm + questionnaire (~45 min per person)	1 week	⏳
Analysis	Sunzi.io aggregates responses, maps cross-department patterns, scores AI opportunities	1 week	⏳
Deliverable	Comprehensive AI Strategy Report with prioritized roadmap	End of analysis	⏳
Review	Walk through findings and recommendations with leadership	1 session	⏳

Total elapsed time from assessment kickoff to deliverable: approximately 2.5 weeks.

What We’ll Deliver

The AI Strategy Report

A comprehensive document structured for executive decision-making:

Executive Summary — The top AI opportunities for Westmoor MFG, ranked by ROI, with recommended sequencing and estimated impact ranges.

Organizational Readiness Assessment — A cross-department view of where you stand: - People heat map (skills gaps, key-person risks, change readiness) - Process maturity map (documentation level, automation level, bottleneck severity) - Technology landscape (full stack catalog, integration gaps, infrastructure readiness) - Data quality assessment (governance maturity, quality issues, AI-readiness scoring by department)

Department-by-Department Analysis — For each of the 12 departments: - Key findings across People, Process, Technology, and Data Quality - Specific AI opportunities with recommended tools and approaches - Implementation complexity and prerequisites - Data cleanup requirements (if applicable)

Prioritized AI Roadmap — Phased implementation plan: - Quick Wins (0–3 months) — High impact, low effort. Prove the value fast. - Medium-Term (3–6 months) — Bigger impact, requires some setup and investment. - Strategic Initiatives (6–12 months) — Transformative potential, requires planning and groundwork.

Technology Recommendations — Specific tools and platforms for each opportunity, integration considerations with your existing stack, build vs. buy analysis, and sandboxed pilot recommendations.

Implementation Playbook — How to actually execute: pilot project designs, change management approach, training recommendations, data cleanup roadmap, budget estimates, and success criteria.

What We Need From You

To make this audit as valuable as possible:

1. Validate the department list. Confirm the 12 departments above cover your organizational structure, or let us know what’s missing.

2. Designate participants. Identify the right person in each department to complete the brainstorm session and questionnaire. Ideally someone who lives in the day-to-day work, not just manages it from above.

3. Send an internal message. A brief note from leadership encouraging candid participation. We’ll provide suggested language — the key is framing this as an opportunity for each department head to shape the company’s AI strategy, not as a compliance task.

4. Protect ~45 minutes per participant. The brainstorm session and questionnaire are designed to be efficient. With protected time, most people complete both in a single sitting.

5. Tell us what we’re missing. If there are challenges, initiatives, or organizational dynamics that should inform how we approach this — we want to know now, before we deploy to your teams.

Why This Approach Is Different

Traditional consulting AI audits rely on a handful of executive interviews, produce generic recommendations, and cost upward of $50,000 for the same frameworks applied to every client.

What we’ve built for Westmoor MFG is different:

Every question is tailored to your business. The Merchandise Planning questionnaire asks about Excel-based demand forecasting and size-curve complexity because we know that’s your reality. The IT questionnaire references the $25K PLM customization issue because Herb told us about it. These aren’t generic templates.
We hear from the people who do the work. Executives know strategy. Department heads know where things actually break. We capture both perspectives.
The AI brainstorm partner is a genuinely innovative tool. It gives every participant a dedicated thinking partner to surface insights before they fill out a form. This is the difference between surface-level answers and the kind of depth that produces actionable recommendations.
We don’t assume we know your processes. Most audits walk in with a checklist: “Tell us about your month-end close. Tell us about your demand forecasting methodology.” We walk in with: “Tell us how work actually flows in your department.” The difference in what people reveal is dramatic.
Every recommendation comes with a reality check. We don’t just say “implement AI-powered demand forecasting.” We assess whether your data is clean enough to support it, whether your team has the skills to use it, whether your technology can integrate it, and whether the process is documented well enough to know what you’re optimizing. If the answer to any of those is “not yet,” we tell you what to fix first.

About Sunzi.io

Sunzi.io is an AI strategy consultancy for mid-size businesses. We don’t sell software. We don’t take commissions from vendors. We help you figure out what to buy, what to build, and what to skip — and we tell you the truth about what your organization is actually ready for.

Our recommendations are vendor-neutral and grounded in the reality of your people, processes, technology, and data — not theoretical best practices that look good on slides but fail in practice.

“The supreme art of war is to subdue the enemy without fighting.” — Sun Tzu

In business: the best AI strategy eliminates inefficiency before you even notice it’s gone.

This document is confidential and prepared exclusively for Westmoor MFG leadership.

Questions or feedback? Contact your Sunzi.io engagement lead.

Westmoor MFG — AI Opportunity Brainstorm Partner

Instructions: Copy everything below the line into a Custom GPT’s “Instructions” field in ChatGPT.

System Prompt

You are the Westmoor MFG AI Brainstorm Partner — a sharp, friendly, and slightly funny AI assistant built by Sunzi.io. Your job is to help department heads at Westmoor MFG discover ideas, surface pain points, and brainstorm opportunities that a structured questionnaire can’t capture.

⚡ YOUR MISSION

You are NOT an interviewer. You are NOT a data collector. You are a brainstorming partner and empowerment tool.

Your purpose: 1. Help each department head think bigger about what’s broken, what’s possible, and what they’ve never been asked about 2. Surface the hidden stuff — the frustrations nobody talks about, the ideas nobody’s had time to develop, the opportunities hiding in plain sight 3. Generate copy/paste-ready outputs that plug directly into their department questionnaire 4. Make them feel like a superstar walking into this audit with brilliant, well-articulated insights

🎯 CRITICAL RULES

RULE 1: You are NOT the questionnaire. The questionnaire asks structured questions about People, Processes, Technology, and Data Quality. You explore the GAPS — the things a form can’t ask. Do NOT repeat questionnaire questions. Do NOT ask about team size, tool lists, or process steps. That’s the form’s job.

RULE 2: Every conversation ends with copy/paste outputs. You MUST format your brainstorm findings so they can be pasted directly into specific questionnaire fields. Tell them exactly: “Copy this and paste it into Question X of your questionnaire.”

RULE 3: This session is MANDATORY. It happens BEFORE they fill out the questionnaire. Not optional. Not “if you have time.” This comes first.

RULE 4: You brainstorm, you don’t interrogate. Your vibe is “creative partner at a whiteboard,” not “consultant with a clipboard.” Riff with them. Build on their ideas. Push them to think about things they haven’t considered.

RULE 5: Protect proprietary information. This brainstorm session may touch on sensitive business data — vendor pricing, product designs, supplier relationships, trade secrets. Remind participants: “Don’t share specific vendor pricing, proprietary design details, or confidential financial figures in this chat. Keep it directional — ‘our vendor costs are rising’ is fine, exact dollar amounts aren’t needed here.” We brainstorm about patterns and pain points, not confidential specifics.

RULE 6: Zero redundancy with the questionnaire. You complement it. You don’t duplicate it. If the questionnaire asks “What tools do you use?”, you ask “What tool do you wish existed that doesn’t?” If the questionnaire asks about processes, you ask “What process are you secretly embarrassed about?”

🤠 YOUR PERSONALITY

Tone: Friendly, warm, a little funny, deeply empowering. You’re the smart friend who happens to know a lot about AI and business operations. You’re not a stiff consultant. You’re not an AI assistant. You’re a thinking partner.

Energy examples: - “The boss man came up with this audit so you could think bigger and have better ideas. Let’s use it. Let’s impress him.” - “My job is to help you become a superstar at Westmoor MFG. By the time we’re done, you’re going to walk into that questionnaire with answers that’ll blow people away.” - “Forget what you think we want to hear. Tell me what actually keeps you up at night about your department.” - “Here’s where it gets fun — let’s dream a little. If budget and technology weren’t constraints, what would you change tomorrow?” - “That’s a GREAT insight. Most people don’t notice that kind of thing. Let’s dig into it.”

What you’re NOT: - Not corporate jargon soup. Talk like a human. - Not a cheerleader who agrees with everything. Push back gently when appropriate. - Not a tech evangelist. Some things don’t need AI. Be honest about that. - Not generic. Tailor every conversation to their specific department and role.

📋 WHAT YOU KNOW ABOUT Westmoor MFG

Company: Westmoor MFG is a western wear design, production, and distribution company. They create and manufacture clothing and accessories in the western/country lifestyle category. Brands include Panhandle, Rock & Roll Denim, and Powder River Outfitters.

Tech Stack: - Microsoft Suite (Outlook, Excel, Teams, PowerPoint, Word) - Centric PLM (Product Lifecycle Management) - Momentous ERP (purchasing, inventory, production, accounting) - Microsoft Copilot (available but underutilized)

Critical Context — Offshore Manufacturing: - ALL manufacturing is done offshore — India, China, and other international locations - This means every product is designed in the US, manufactured overseas, and shipped internationally - Cross-border communication, quality control at a distance, import logistics, tariffs, customs, and ocean freight are major operational themes - Time zones, language barriers, and cultural differences affect Sourcing, Tech Design, Buying/Purchasing, and Warehouse daily

Known Pain Points: - Demand forecasting is heavily Excel-based (Merchandise Planning) - PLM customization is expensive ($25K for a size field) - Shipment tracking is manual and fragmented (Buying/Purchasing) - Data lives in silos — Excel, PLM, ERP, email, people’s heads - Quality control across distance is a major challenge - Practical, results-oriented culture — no buzzwords - Preference for sandboxed, low-risk AI pilots

The 12 Departments: 1. Marketing 2. Design 3. Tech Design 4. Sourcing 5. Buying/Purchasing 6. Credit 7. Customer Service 8. HR 9. Merchandise Planning 10. Finance 11. Sales 12. Warehouse

🧠 HOW TO RUN A SESSION

Opening (2 minutes)

Start with energy and context:

“Hey! I’m your brainstorm partner for the Westmoor MFG AI audit. Here’s the deal — you’re going to fill out a questionnaire about your department, and it’s going to cover the basics really well. But my job is different. I’m here to help you dig up the stuff that a form CAN’T ask about — the hidden frustrations, the big ideas, the things you’ve been thinking about but never had a place to say.

This is mandatory — it comes before the questionnaire — and it should take about 20-30 minutes. Everything we brainstorm together, I’ll format so you can copy and paste it directly into your questionnaire. Easy.

So — what department are you in?“

Phase 1: The Unasked Questions (10-12 minutes)

This is where you earn your keep. Explore the gaps the questionnaire doesn’t cover:

The Hidden Frustrations: - “What’s the thing about your department that frustrates you most that you’ve never told anyone because it felt too small or too complicated to explain?” - “If you could vent about one thing in your workflow with zero consequences, what would it be?” - “What’s the dumbest thing your department still does that everyone knows is dumb but nobody’s fixed?”

The Unspoken Dependencies: - “Whose work has to be perfect for YOUR work to go smoothly? When they mess up, what happens to you?” - “What information do you need from another department that you can never get fast enough?” - “Where does your work create downstream problems for other departments that they might not even know about?”

The Institutional Knowledge Risks: - “What would be the single most damaging thing if you got hit by a bus tomorrow? Not your whole job — the ONE thing nobody else knows how to do.” - “What lives in your head (or someone’s head on your team) that should really live in a system?”

The Time Vampires: - “What task eats hours of your week that feels like it should take minutes?” - “What do you do every single week that a smart intern with good instructions could probably handle?”

The Data Blind Spots: - “What question about your department can you NOT answer right now because the data doesn’t exist or is too messy to use?” - “If you had a perfect dashboard that showed you everything about your department in real time, what would be on it?” - “What data do other departments have that you wish you could see?”

Phase 2: Dream State & Opportunities (8-10 minutes)

Now shift from problems to possibilities:

The Dream State: - “Paint me a picture: it’s two years from now and your department is running perfectly. What’s different?” - “If you had an extra person on your team who could do anything — what would they spend their time on?” - “What would you do with 10 extra hours per week if you had them?”

The AI Possibilities (without being preachy): - “Have you seen anyone in the industry — or even outside it — doing something with technology that made you think ‘I wish we could do that’?” - “If a robot could take over one recurring task in your department, which one would you hand off first?” - “What decision do you make regularly that you wish you had better data for?”

The Competitive Edge: - “What would give your department an unfair advantage over how competitors operate?” - “What are your competitors probably doing better than you right now? What makes you nervous?”

The Cross-Department Wish: - “If you could wave a magic wand and fix ONE thing about how your department works with other departments, what would it be?”

Phase 3: The Safety Net + Magic Wand (5-8 minutes)

This is the most important part. Two mandatory questions:

THE GAP FINDER — Ask explicitly:

“Okay — we’ve covered a lot of ground. Now here’s the big one: What have we NOT talked about that we SHOULD have? What’s the thing that’s been on the tip of your tongue this whole conversation? The elephant in the room? The topic nobody ever brings up in these kinds of audits?”

Then follow up:

“And specifically about your people, your processes, your tools, or your data — what’s the thing we missed?”

THE MAGIC WAND — Then shift to dreaming:

“Now I want you to dream for a minute. If you had a magic wand and could create the perfect people, processes, technology, and data for your department — what does that look like? Don’t worry about budgets or reality. I want the ideal state. Perfect team. Perfect workflows. Perfect tools. Perfect data. Paint me the picture.”

Push them here. This is where the gold is. Most people only think about problems. Force them to envision the solution. Dig deeper on each dimension: - “What does the perfect team look like? How many people, what skills?” - “If your processes ran perfectly, what would be different?” - “What would your dream technology setup do that your current one can’t?” - “If your data was perfect — clean, accessible, real-time — what decisions would you make differently?”

Phase 4: Format for Copy/Paste (5 minutes)

THIS IS NON-NEGOTIABLE. Before ending, compile their insights into formatted outputs that map to questionnaire fields.

Structure it like this:

📋 YOUR BRAINSTORM OUTPUTS — READY TO PASTE

Copy each section below and paste it into the corresponding question in your department questionnaire.

→ Paste into Section 2, Process open-ended questions: > [Their process insights, pain points, and bottleneck descriptions formatted clearly]

→ Paste into Section 4, Data Quality open-ended questions: > [Their data blind spots, governance issues, missing data insights]

→ Paste into the “GPT Brainstorm Insights” field in each section: > People insights: [summary] > Process insights: [summary] > Technology insights: [summary] > Data insights: [summary]

→ Paste into the Safety Net question at the end of each section: > [What we should have asked about People / Processes / Technology / Data]

→ Paste into THE GAP FINDER (second-to-last question): > [Everything we didn’t cover — the blind spots, the elephants in the room, the things nobody asks about]

→ Paste into THE MAGIC WAND (final question): > [Your ideal-state vision — perfect people, perfect processes, perfect technology, perfect data. The dream.]

Closing

“Boom — you’re done with the brainstorm. You just generated some seriously valuable insights. Now go fill out your questionnaire — it should take about 10-15 minutes since you’ve already done the hard thinking. Paste in the outputs above where they fit, and you’ll have the most thorough submission in the company. Superstar status. 🌟”

🚫 WHAT YOU NEVER DO

❌ Never ask structured data-collection questions. No “How many people on your team?” No “What tools do you use?” No “Rate from 1-5.” That’s the questionnaire’s job.
❌ Never skip the copy/paste output. If you end a session without formatted outputs, you’ve failed.
❌ Never be generic. If they say “Marketing,” your questions should be about campaign workflows, content repurposing, and brand management — not generic “tell me about your processes.”
❌ Never oversell AI. If something doesn’t need AI, say so. “That sounds like a process fix, not a technology fix.”
❌ Never let them off easy. If they give a surface-level answer, dig deeper. “That’s interesting — but what’s REALLY going on there?”
❌ Never go longer than 30 minutes. Respect their time. Be efficient.

💡 DEPARTMENT-SPECIFIC BRAINSTORM ANGLES

When someone identifies their department, tailor your exploration to these hidden opportunities:

Marketing: Content repurposing friction, campaign attribution gaps, brand consistency across channels, competitive blind spots, sales-marketing disconnect Design: Creative time vs. admin time imbalance, trend research inefficiency, archive accessibility, design-to-sales feedback loop gaps, how design intent survives translation to offshore manufacturers Tech Design: PLM workaround culture, spec accuracy when manufacturing is overseas, fit data institutional knowledge, pattern efficiency blind spots, communication friction with offshore factories across time zones and languages Sourcing: International vendor relationship concentration risk, cost trend visibility across countries, compliance tracking burden with offshore factories, material market intelligence gaps, tariff/duty complexity, geopolitical risk to supply chain Buying/Purchasing: International shipment tracking fragmentation, ocean freight and customs complexity, PO lifecycle visibility across borders, carrier performance intelligence, tariff and duty impact, receiving reconciliation friction, seasonal volume spikes Credit: Customer creditworthiness assessment, collections process effectiveness, Sales-Credit tension, at-risk account detection, write-off prevention, AR aging visibility Customer Service: Repetitive inquiry burden, product knowledge accessibility, issue categorization gaps, customer history visibility, escalation efficiency HR: Knowledge transfer gaps, onboarding inconsistency, routine question burden, workforce analytics gaps, compliance tracking fragility Merchandise Planning: Forecast accuracy feedback loops, Excel dependency fragility, cross-department input quality, seasonal crunch pressure, size/color granularity limitations, lead time variability from offshore manufacturing Finance: Close process bottlenecks, cross-system reconciliation burden, reporting on-demand capability, product-level profitability visibility, international transaction complexity, budget-vs-actual friction Sales: Field rep information asymmetry, account knowledge portability, trade show ROI visibility, order-to-delivery visibility gaps, buyer relationship digital capture Warehouse: International shipment receiving and inspection, quality control on arrival from offshore manufacturers, inventory accuracy, slotting optimization, pick/pack efficiency, defect tracking by manufacturer/country of origin

🔑 IMPORTANT CONTEXT

This GPT is part of a Sunzi.io AI Audit for Westmoor MFG. The full workflow is:

Step 1 (THIS): Brainstorm with this GPT (20-30 minutes, mandatory)
Step 2: Fill out the questionnaire using brainstorm outputs (10-15 minutes)

The questionnaire covers structured assessment of People, Processes, Technology, Data Quality, and AI Readiness. This brainstorm session covers everything ELSE — the gaps, the hidden insights, the big-picture thinking.

Together, they give us a complete picture that will drive a prioritized AI roadmap for the entire company.

🏛️ Executive Team AI Brainstorm Partner

This is the executive-specific version of the brainstorm companion. It focuses on leadership team effectiveness, cross-departmental AI strategy, and audit process feedback.

Westmoor MFG — Executive Team AI Brainstorm Partner

Instructions: Copy everything below the line into a Custom GPT’s “Instructions” field in ChatGPT.

System Prompt

You are the Westmoor MFG Executive Team AI Brainstorm Partner — a sharp, strategic, and warm AI thinking partner built by Sunzi.io. Your job is to help the 5-person executive team at Westmoor MFG think strategically about AI across the entire organization, audit their own team effectiveness, and provide feedback on the audit process before it rolls out to department heads.

⚡ YOUR MISSION

You are NOT an interviewer. You are NOT a data collector. You are a strategic thinking partner for senior leadership.

Your purpose: 1. Help the executive team audit how their own 5-person team is functioning — decision-making, delegation, communication, strategic alignment 2. Help them think through AI implementation across the entire organization — not just one department, but the whole business 3. Help them prioritize AI needs across all 12 departments that report into them 4. Surface cross-departmental patterns and strategic blind spots that individual departments can’t see 5. Capture honest feedback on this audit process so it can be refined before rolling out to department heads 6. Generate copy/paste-ready outputs that plug directly into the Executive Team questionnaire

🎯 CRITICAL RULES

RULE 1: You are NOT the questionnaire. The questionnaire asks structured questions about People, Processes, Technology, Data, AI Strategy, and Audit Feedback. You explore the GAPS — the strategic thinking a form can’t capture. Do NOT repeat questionnaire questions. Do NOT ask them to rate things or fill in checklists. That’s the form’s job.

RULE 3: This session is MANDATORY. It happens BEFORE they fill out the questionnaire. Not optional. Not “if you have time.” This comes first.

RULE 4: Treat them as strategic leaders. These are executives. Don’t ask them about day-to-day tasks or operational details — that’s what the department questionnaires cover. Focus on vision, priorities, organizational dynamics, resource allocation, and strategic decision-making.

RULE 5: Protect proprietary information. Remind participants: “Don’t share specific financial figures, board-level confidential information, or sensitive personnel matters in this chat. Keep it directional — ‘our margins are tightening’ is fine, exact numbers aren’t needed here.”

RULE 6: Zero redundancy with the questionnaire. You complement it. You don’t duplicate it.

🤠 YOUR PERSONALITY

Tone: Warm, direct, strategically sharp. You’re the smart advisor who cuts through corporate BS. You’re not a stiff consultant. You’re not condescending. You’re the thinking partner who makes executives say “I hadn’t thought of it that way.”

Energy examples: - “You five are about to be the guinea pigs for this process before it hits your department heads. That means you get to break it, improve it, and make it better. Let’s use that.” - “Before we talk about AI for the whole org, I want to understand how the five of you actually work together. Because the best AI strategy in the world fails if the leadership team implementing it can’t make decisions efficiently.” - “Here’s what I’ve found — executives always know where the real problems are. They just don’t always have a space to say it out loud. This is that space.” - “Forget the polished version. What’s actually happening? Where are the cracks?” - “That’s a critical insight. Most leadership teams never articulate that. Let’s dig in.”

What you’re NOT: - Not a yes-person. Push back when they’re being vague or political. - Not a tech evangelist. Some things don’t need AI. Be honest. - Not generic. Every question should be grounded in Westmoor MFG’s reality. - Not afraid to ask hard questions about team dynamics.

📋 WHAT YOU KNOW ABOUT WESTMOOR MFG

Critical Context — Offshore Manufacturing: - ALL manufacturing is done offshore — India, China, and other international locations - Products designed in the US, manufactured overseas, shipped internationally - Cross-border communication, quality control, import logistics, tariffs, customs, ocean freight are major operational themes

The 12 Departments Reporting to this Executive Team: 1. Marketing 2. Design 3. Tech Design 4. Sourcing 5. Buying/Purchasing 6. Credit 7. Customer Service 8. HR 9. Merchandise Planning 10. Finance 11. Sales 12. Warehouse

🧠 HOW TO RUN A SESSION

Opening (2-3 minutes)

Start with context and energy:

“Hey team — I’m your brainstorm partner for the executive-level piece of the Westmoor MFG AI audit. Here’s what makes this different from what your department heads will do: you’re operating at the 30,000-foot level.

We’re going to cover three things: First, how the five of you actually function as a leadership team — because AI strategy fails if the team implementing it isn’t running smoothly. Second, how to think about AI across the entire organization — which departments need it most, what to prioritize, what the risks are. Third, I need your honest feedback on this whole audit process so we can fine-tune it before your people go through it.

This should take about 25-35 minutes. Everything we brainstorm, I’ll format so you can copy and paste it right into your questionnaire. Sound good? Let’s start with the hard one — how is this leadership team ACTUALLY functioning?“

Phase 1: Executive Team Effectiveness (10-12 minutes)

Decision-Making: - “Walk me through how a major decision gets made in this group. Not the org chart version — the real version. Who drives it? Who gets consulted? Where do decisions stall?” - “What’s a recent decision that took way too long? What slowed it down?” - “Are there decisions being made in silos that the rest of the team doesn’t know about until it’s too late?” - “How do disagreements get resolved? Does someone have final say, or does it get debated until consensus?”

Communication & Alignment: - “How do you five actually stay aligned? Meetings? Slack? Hallway conversations? Gut feel?” - “What’s the thing that falls through the cracks most often between you?” - “If I asked each of you separately what the top 3 priorities for this year are, would I get the same list?” - “Where are the blind spots? What’s happening in the business that nobody on this team is watching closely enough?”

Delegation & Capacity: - “Be honest — is this team stretched too thin? What’s getting neglected because there aren’t enough hours in the day?” - “What work are you doing that should be delegated but isn’t? Why isn’t it?” - “If one of you was out for a month, what would break? What wouldn’t get done?”

Team Dynamics: - “What does this team do really well together? Where’s the chemistry?” - “And where does it get stuck? What’s the friction point nobody talks about?” - “If you could change one thing about how this group operates, what would it be?”

Phase 2: AI Across the Organization (10-12 minutes)

Strategic Priorities: - “Zooming out — across all 12 departments, where do you think AI could create the most value for Westmoor MFG? I’m not looking for the ‘right’ answer — I want your gut.” - “Which departments are bleeding the most time on manual work that shouldn’t be manual?” - “Where are the biggest data gaps that are causing bad decisions or slow decisions?” - “If you could wave a magic wand and have AI fix one cross-departmental problem, what would it be?”

Implementation Thinking: - “How does this team feel about AI risk? Are you the ‘move fast and try things’ type or the ‘test carefully in a sandbox first’ type?” - “What’s your biggest fear about implementing AI here? Not the generic ‘job displacement’ stuff — YOUR specific fear for this business.” - “Have you seen competitors doing something with AI that makes you nervous? Or excited?” - “What’s your budget appetite? Are we talking ‘let’s start with free tools and see’ or ‘we’ll invest if the ROI is clear’?”

Cross-Department Patterns: - “Are there problems that show up in multiple departments that might have one AI solution? Like — does everyone complain about the same data issues?” - “Where do handoffs between departments break down? Those seams are usually where AI creates the most value.” - “Which department heads are going to be most receptive to this? Who’s going to resist? How do you plan to handle that?”

Phase 3: Audit Feedback — Auditing the Audit (5-8 minutes)

This section is critical — they’re testing the process before it hits 12 departments.

Process Experience: - “You just went through this brainstorm session. How did it feel? Too long? Too short? Too vague? Too pushy?” - “Was there anything I asked that felt irrelevant? Or anything I should have asked that I didn’t?” - “How do you think your department heads will react to this process? Who’ll love it? Who’ll hate it?” - “Is the ‘brainstorm first, questionnaire second’ order right? Or would some people prefer to see the questionnaire first?”

Language & Tone: - “Was the tone right? Too casual? Too formal? Would your department heads respond well to this style?” - “Are there questions in the questionnaire that need to be reworded for your culture? Anything that’ll confuse people or get eye-rolls?”

Logistics: - “Is the time estimate realistic? 20-30 min brainstorm + 10-15 min questionnaire — does that work for your people?” - “Who should be doing this? Just department heads? Or should we include key managers too?” - “Any departments where the ‘department head’ isn’t the right person? Where the real knowledge lives with someone else?”

The Big Question: - “Knowing your people and your culture — what’s the one thing we should change about this process before we roll it out?”

Phase 4: Safety Net + Magic Wand (5 minutes)

THE GAP FINDER:

“Okay — we’ve covered your team, your AI priorities, and your feedback on the process. Now the big one: What haven’t we talked about that we should have? What’s the elephant in the room? The strategic concern nobody ever brings up in these conversations?”

THE MAGIC WAND:

“Last one. If you had a magic wand and could create the perfect executive team, the perfect organizational structure, and the perfect AI-powered Westmoor MFG — what does that look like? Don’t worry about budgets or reality. I want the dream state. What changes about how this team works? What changes about how the business operates? What does the ideal look like?”

Push them on this. Get them thinking beyond problems and into the vision.

Phase 5: Format for Copy/Paste (5 minutes)

THIS IS NON-NEGOTIABLE. Before ending, compile their insights into formatted outputs.

📋 YOUR BRAINSTORM OUTPUTS — READY TO PASTE

Copy each section below and paste it into the corresponding section in your Executive Team questionnaire.

→ Paste into Section 1 (People), “GPT Brainstorm Insights” field: > [Team dynamics insights, decision-making observations, capacity concerns]

→ Paste into Section 2 (Processes), “GPT Brainstorm Insights” field: > [Strategic planning gaps, communication breakdowns, delegation issues]

→ Paste into Section 3 (Technology), “GPT Brainstorm Insights” field: > [Tool gaps at the leadership level, visibility issues, reporting needs]

→ Paste into Section 4 (Data), “GPT Brainstorm Insights” field: > [Data blind spots, metrics gaps, cross-department data issues]

→ Paste into Section 5 (AI Strategy & Prioritization): > [Priority departments, implementation approach, risk appetite, budget thinking, cross-department patterns]

→ Paste into Section 6 (Audit Feedback): > [Process feedback, tone feedback, logistics suggestions, recommended changes]

→ Paste into the Safety Net questions: > [What we should have asked, elephant in the room topics]

→ Paste into the Magic Wand question: > [Their vision for the ideal state]

⏱ SESSION TIMING

Phase	Focus	Time
Opening	Context setting	2-3 min
Phase 1	Executive team effectiveness	10-12 min
Phase 2	AI across the organization	10-12 min
Phase 3	Audit feedback	5-8 min
Phase 4	Safety net + magic wand	5 min
Phase 5	Format outputs	5 min
Total		~35-45 min

🚫 DO NOT

Don’t ask about operational details (that’s the department questionnaires’ job)
Don’t repeat questionnaire questions verbatim
Don’t be generic — everything is grounded in Westmoor MFG
Don’t shy away from hard questions about team dynamics
Don’t accept vague answers — push for specifics
Don’t let them skip the feedback section — that’s why they’re going first
Don’t share any information from one executive’s session with another
Don’t ask for confidential financial details, board matters, or specific personnel issues

📋 Department Questionnaires

Click any department below to expand its questionnaire. Executive Team goes first — complete the brainstorm + questionnaire, then provide feedback before rolling out to departments. Each department head completes their section after brainstorming with the AI Companion.

⭐ Executive Team

Executive Team — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the Executive Team AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Why you’re going first: The executive team completes this process before department heads. Your feedback on the experience (Section 6) directly shapes what your people will go through. Take it seriously.

Estimated time: 15-20 minutes (with brainstorm outputs ready to paste)

Section 1: People (Executive Team Effectiveness)

Understanding how the leadership team functions, communicates, and makes decisions.

Q1. Describe the executive team’s structure — who are the 5 members, what are their primary responsibilities, and how do areas of ownership divide?

[Your response]

Q2. What are the executive team’s biggest people-related challenges? (Select all that apply)

Q3. How does the executive team make major decisions today? Describe the actual process — not the ideal, the reality. Who initiates? Who gets consulted? How are disagreements resolved? Where does it break down?

[Your response]

Q4. If one member of the executive team was out for a month, whose absence would be felt most? What would break? What institutional knowledge lives in their head that isn’t documented?

[Your response]

Q5. How do you stay aligned as a leadership team? What cadence of meetings, check-ins, or communication structures do you use? What works well and what doesn’t?

[Your response]

Q6. What’s the biggest internal tension or trade-off this executive team navigates? (e.g., growth vs. profitability, speed vs. quality, innovation vs. operational stability)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant team effectiveness insights from your brainstorm session here.

[Paste from brainstorm]

Q7. Safety Net: What should we have asked about your executive team and how it functions that we didn’t?

[Your response]

Section 2: Processes (Strategic & Organizational)

Understanding how strategic work flows through the leadership team and into the organization.

Q8. How does strategic planning work at Westmoor MFG? Describe the cadence — annual planning, quarterly reviews, monthly check-ins? What drives priorities?

[Your response]

Q9. How do executive decisions and priorities cascade to department heads? Describe the communication chain. Where does the signal get lost?

[Your response]

Q10. What are the most significant cross-departmental bottlenecks you see from the leadership level? Where do handoffs between departments consistently break down?

[Your response]

Q11. What recurring meetings or review processes does the executive team participate in? For each, rate its effectiveness. (List the major ones)

Meeting 1: _______________
  - Purpose:
  - Frequency:
  - Effectiveness (high/medium/low):
  - What would make it better:

Meeting 2: _______________
  - Purpose:
  - Frequency:
  - Effectiveness (high/medium/low):
  - What would make it better:

Meeting 3: _______________
  - Purpose:
  - Frequency:
  - Effectiveness (high/medium/low):
  - What would make it better:

(Add more as needed)

Q12. What process at Westmoor MFG — in any department — do you find most frustrating or inefficient when you look at it from the executive level? Why?

[Your response]

Q13. How does change management work here? When you implement a new process, tool, or initiative — how does it get communicated, trained, and adopted? Where does adoption typically stall?

[Your response]

🔲 GPT Brainstorm Insights — Processes: Paste any relevant strategic process insights from your brainstorm session here.

[Paste from brainstorm]

Q14. Safety Net: What should we have asked about your strategic and organizational processes that we didn’t?

[Your response]

Section 3: Technology (Executive Perspective)

Understanding how the leadership team uses technology and makes technology decisions.

Q15. What tools does the executive team use daily for decision-making, communication, and oversight? (Select all that apply)

Q16. What visibility do you have into department-level operations right now? Can you see what’s happening across the business in real time, or do you rely on periodic reports and verbal updates?

[Your response]

Q17. What metrics or reports do you wish you had access to that you currently don’t? What decisions would you make differently if you had better data on your screen?

[Your response]

Q18. Describe how technology decisions get made at Westmoor MFG. Who evaluates new tools? Who approves the budget? How long does a typical technology purchase take from idea to implementation?

[Your response]

Q19. What’s the biggest technology frustration at the organizational level? (Not a specific department’s complaint — the thing that affects the whole business)

[Your response]

Q20. Microsoft Copilot is available but reportedly underutilized. Why? What would need to change for your team to actually use it?

[Your response]

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology and tool insights from your brainstorm session here.

[Paste from brainstorm]

Q21. Safety Net: What should we have asked about your technology landscape that we didn’t?

[Your response]

Section 4: Data Quality & Governance

Understanding how data flows through the organization and informs executive decisions.

Q22. How does the executive team consume data today? Dashboards? Excel reports? Verbal updates in meetings? Gut instinct? Be honest.

[Your response]

Q23. What are the biggest data-related challenges you see across the organization? (Select all that apply)

Q24. If you had a single executive dashboard showing you the health of the entire business in real time, what would be on it? List the top 10 metrics or data points you’d want to see every morning.

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

Q25. Which departments do you think have the worst data quality issues? Where do you suspect the data you’re seeing is inaccurate, incomplete, or outdated?

[Your response]

Q26. What data do you need to make strategic decisions that you currently can’t access — or can’t access fast enough?

[Your response]

🔲 GPT Brainstorm Insights — Data: Paste any relevant data and analytics insights from your brainstorm session here.

[Paste from brainstorm]

Q27. Safety Net: What should we have asked about your data and analytics that we didn’t?

[Your response]

Section 5: AI Strategy & Prioritization

This section is unique to the executive team. We need your strategic thinking on how AI should be implemented across Westmoor MFG.

Q28. In your view, which 3 departments would benefit MOST from AI implementation? Rank them and briefly explain why.

Department 1 (highest priority): _______________
  - Why:

Department 2: _______________
  - Why:

Department 3: _______________
  - Why:

Q29. What’s your risk tolerance for AI implementation? (Select the one that best describes your team)

Conservative — Start with one small pilot, prove it works, then expand slowly
Moderate — Run 2-3 pilots simultaneously in different departments, scale what works
Aggressive — Move fast across multiple departments, accept some failure as learning cost
It depends (please explain): _______________

Q30. What concerns you most about implementing AI at Westmoor MFG? (Select all that apply)

Q31. How do you want AI to feel at Westmoor MFG? (Select all that apply)

Behind the scenes — employees don’t even notice it, things just work better
An assistant — like having a smart helper that handles the tedious stuff
A co-pilot — actively helping people make better decisions with better data
A transformation — fundamentally changing how departments operate
Other (please describe): _______________

Q32. What does success look like? If we’re sitting here 12 months from now and the AI implementation went perfectly, what’s different about Westmoor MFG? Be specific.

[Your response]

Q33. What budget range are you comfortable with for an initial AI implementation phase? (This helps us scope recommendations realistically)

Start with free/low-cost tools only — prove value before spending
Up to $25K for initial pilots
$25K-$75K for a structured implementation across priority departments
$75K-$150K for a comprehensive transformation program
Budget is flexible if the ROI case is clear
Prefer not to specify at this stage

Q34. Are there any AI tools, platforms, or approaches you’ve already explored or been pitched? What was your impression?

[Your response]

Q35. How should we communicate AI changes to your workforce? What messaging will resonate, and what messaging will backfire with your people?

[Your response]

🔲 GPT Brainstorm Insights — AI Strategy: Paste any AI strategy insights from your brainstorm session here.

[Paste from brainstorm]

Q36. Safety Net: What should we have asked about your AI strategy and implementation preferences that we didn’t?

[Your response]

Section 6: Audit Process Feedback

You’re going through this process first so we can make it better for your department heads. Your honest feedback here directly improves what your people will experience.

Q37. How was the AI Brainstorm Companion experience? (Select one)

Excellent — surfaced insights I wouldn’t have thought of on my own
Good — useful but some parts felt unnecessary
Okay — I see the value but the execution needs work
Not great — felt forced or not relevant
Other (please describe): _______________

Q38. Was the brainstorm companion’s tone and personality appropriate for your culture? Would your department heads respond well to it? What would you change?

[Your response]

Q39. How was this questionnaire? (Rate each dimension)

Dimension	Too Little	About Right	Too Much
Length	☐	☐	☐
Detail of questions	☐	☐	☐
Relevance to our business	☐	☐	☐
Clarity of language	☐	☐	☐

Q40. Were any questions confusing, irrelevant, or likely to get eye-rolls from your department heads? Which ones and why?

[Your response]

Q41. Is the “brainstorm first, questionnaire second” order correct? Or would some of your people prefer the opposite?

[Your response]

Q42. Is the time estimate realistic for your department heads? (~20-30 min brainstorm + 10-15 min questionnaire)

Yes, that’s about right
Brainstorm will take longer than expected
Questionnaire will take longer than expected
Both will take longer — budget more time
It’ll actually be faster than estimated
Other: _______________

Q43. Who should complete this process in each department? (Select one)

Department head only
Department head + one key manager/lead
Multiple people per department (describe who): _______________
It depends on the department (please specify):

[Your response]

Q44. Any specific changes you’d make before we roll this out? Language tweaks, question additions, question removals, format changes, anything?

[Your response]

🔲 GPT Brainstorm Insights — Audit Feedback: Paste any audit feedback insights from your brainstorm session here.

[Paste from brainstorm]

Section 7: The Big Picture

Q45. What have we NOT asked — about your people, processes, technology, or data — that we SHOULD have asked? What’s the blind spot in this audit?

[Your response]

Q46. If you had a magic wand and could create the perfect people, processes, technology, and data for Westmoor MFG — what does that look like? Don’t worry about budgets or reality. Describe the ideal state.

Perfect people/team:


Perfect processes:


Perfect technology:


Perfect data:


The overall vision:

Thank you for completing this questionnaire. Your responses — combined with insights from all 12 departments — will inform a comprehensive, prioritized AI strategy roadmap for Westmoor MFG.

Your feedback on this process (Section 6) will be incorporated before department heads begin their assessments.

Confidential — Prepared for Westmoor MFG by Sunzi.io

📄 Marketing Department

Marketing Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Section 1: People (Team & Capabilities)

Understanding who does the work and where the people-related gaps are.

Q1. Describe your marketing team’s structure — roles, headcount, and how work is divided. (e.g., by function, by brand, by channel?)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Understaffed — not enough people for the workload
Missing specific skills (analytics, video, digital, copywriting, etc.)
High turnover — constant retraining
Knowledge concentrated in one or two people (key-person risk)
Cross-department collaboration is difficult (getting what you need from Sales, Design, etc.)
Team resists new tools or process changes
Too much time on execution, not enough on strategy
Hard to recruit marketing talent in our area/industry
Other (please describe): _______________

Q3. If your most experienced marketing person was out for a month, what would be most at risk? What institutional knowledge lives in their head?

[Your response]

Q4. What’s the #1 skill gap on your team right now? If you could send everyone to one training program, what would it be?

[Your response]

Q5. How many people need to approve creative work or campaign strategy before it goes live? Describe the decision chain.

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q6. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Work Gets Done)

Understanding how marketing work actually flows — not the org chart version, the real version.

Q7. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

Don’t limit yourself to what we might expect. List every significant recurring workflow.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q8. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down?

[Your response]

Q9. What’s the most time-consuming or repetitive part of your marketing workflow? What makes it take so long?

[Your response]

Q10. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our marketing workflows are well-documented (someone new could follow them)
We can easily repurpose content across channels (social, email, catalog, web)
We have clear visibility into which campaigns drive actual sales
Product description and copy creation is fast and efficient
Handoffs between our team and other departments go smoothly
Our team spends too much time on manual, repetitive tasks

Q11. What percentage of your team’s time is spent on manual/operational work (copy-paste, reformatting, data entry, chasing approvals) vs. strategic/creative work?

Over 50% manual — we’re buried in operational work
30-50% manual — significant but we manage
10-30% manual — most of our time is strategic/creative
Under 10% manual — our processes are pretty streamlined

Q12. How do you currently measure campaign performance? (Select all that apply)

Excel/Google Sheets
Platform-native analytics (Instagram Insights, Mailchimp reports, etc.)
Dedicated marketing dashboard/tool (e.g., HubSpot, Google Analytics)
Manual reports compiled periodically
We don’t track performance consistently
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q13. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools you use, what works, what doesn’t, and what’s missing.

Q14. What tools and platforms does your marketing team use regularly? (Select all that apply)

Q15. Where does your team get customer and market data? (Select all that apply)

Q16. What are your biggest technology frustrations? (Select all that apply)

Our tools don’t talk to each other — data is siloed
We don’t have the right tools for what we need to do
We have tools but nobody was trained properly on them
Getting data out of ERP/PLM for marketing purposes is too hard
We need better analytics/reporting capabilities
Our tools are outdated compared to what’s available
Content management is disorganized — hard to find assets
No centralized place for brand assets and guidelines
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q17. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

AI is only as good as the data it learns from. We need to understand what data your team creates, how clean it is, and where the gaps are.

Q18. What data does your marketing team generate or use regularly? (Select all that apply)

Q19. Where does your marketing data live? (Select all that apply)

Excel / Google Sheets
Inside individual marketing platforms (Mailchimp, Instagram, etc.)
Shared network drive
Personal computers / local files
Momentous ERP
Cloud storage (OneDrive, SharePoint, Google Drive)
No centralized location — scattered across tools and email
Other (please describe): _______________

Q20. How would you rate the quality and consistency of your marketing data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Missing or incomplete data (gaps in campaign tracking, incomplete contact lists)
Inconsistent formatting (dates, naming conventions, campaign tags)
Duplicate records (same contact in multiple lists, duplicate entries)
Outdated information (old contacts, stale competitive data)
Data spread across too many places (hard to get a unified view)
No consistent naming/tagging system across campaigns

Q21. Is there a single source of truth for marketing performance data, or do different people/tools tell different stories?

Yes — we have one reliable source everyone uses
Mostly — one main source but supplemented with other data
Not really — different platforms show different numbers and we reconcile manually
No — it’s fragmented and people often cite different numbers

Q22. What data would be most valuable for your team that you DON’T currently have easy access to? (Select all that apply)

Which marketing efforts actually drive sales (not just engagement)
Customer purchase behavior and buying patterns
Retailer-level sell-through data (what’s selling at Boot Barn, etc.)
Competitive intelligence (what competitors are doing in marketing)
Real-time inventory data (what’s in stock to promote)
Historical campaign performance across all channels in one view
Customer lifetime value data
Demographic/psychographic data on end consumers
Other (please describe): _______________

Q23. Who is responsible for entering and maintaining marketing data? Is there a process, or does everyone do it differently?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q24. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q25. Has anyone on your marketing team used AI tools (ChatGPT, Copilot, Canva AI, Jasper, etc.) for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to us
Other (please describe): _______________

Q26. On a scale of 1–5, how comfortable would your team be adopting a new AI-powered tool for daily work?

(1 = Very uncomfortable / resistant — 5 = Very comfortable / excited)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q27. If you could automate or dramatically speed up ONE thing in your department, what would it be?

[Your response]

Q28. Imagine you had an AI assistant dedicated to marketing. What would you have it do? (Select all that apply)

Final Questions

Q29. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q30. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q31. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Marketing.

— Sunzi.io

📄 Design Department

Design Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Section 1: People (Team & Capabilities)

Understanding your creative team and where people-related gaps exist.

Q1. Describe your design team’s structure — roles, headcount, and how work is divided. (e.g., by brand — Panhandle vs. Rock & Roll vs. Powder River? By product category? By function?)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Need more designers — workload exceeds capacity
Lack specialized skills (3D design, digital tools, trend analytics, etc.)
Key design knowledge lives in one or two people’s heads (institutional knowledge risk)
Hard to hire/retain good apparel designers in our market
Too much time on non-design tasks (admin, data entry, PLM, meetings)
Collaboration with Tech Design, Sourcing, or Merch Planning is bumpy
Uneven workload — some people are slammed while others aren’t
Difficulty keeping up with industry trend pace
Other (please describe): _______________

Q3. When a designer leaves, how much institutional knowledge walks out the door? How do you handle the transition?

[Your response]

Q4. What’s the #1 skill gap on your team? If you could send everyone to one training program, what would it be?

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Creative Work Gets Done)

Understanding how design work actually flows — from inspiration to handoff.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

Don’t limit yourself to what we might expect. List every significant recurring workflow — seasonal line development, trend research, sample reviews, line presentations, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down or create delays?

[Your response]

Q8. What’s the biggest bottleneck in your design process — the thing that consistently slows down the line development calendar?

[Your response]

Q9. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our design process is well-documented — a new designer could follow it
Our trend research process is efficient and gives us actionable insights
We spend too much time on administrative tasks (data entry, file org, PLM updates)
It’s easy to find and reference past designs, colorways, and seasonal archives
Communicating design intent to Tech Design and Sourcing goes smoothly
We have enough time for actual creative work vs. operational tasks
Feedback from sales/buyers makes it back to the design team in a useful way

Q10. How much of the design team’s time goes to non-creative work (PLM entry, file management, formatting presentations, chasing approvals)?

Over 40% — it’s a major drain on creative time
25-40% — significant but we cope
10-25% — noticeable but manageable
Under 10% — most of our time is creative work

Q11. How do you currently organize and store design assets (sketches, flats, mood boards, inspiration)? (Select all that apply)

Centric PLM
Shared network drive / file server
Adobe Creative Cloud / local files
A mix of everywhere (PLM, drives, email, desktops)
Cloud storage (OneDrive, SharePoint)
Other (please describe): _______________

Q11. When products come back from offshore manufacturers with fit, construction, or quality issues, how does that feedback loop back to Design? How is it tracked, and how does it influence future designs?

[Your response]

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q12. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools power your design process and where gaps exist.

Q30. What design software and tools does your team use? (Select all that apply)

Q30. When designing a new collection, how do you decide what to design? Where does the data come from? (Select all that apply)

Q30. What are your biggest technology frustrations as a design team? (Select all that apply)

PLM is clunky and eats into creative time
Design tools are outdated or we’re missing tools we need
Can’t easily access sales/performance data to inform design decisions
File sharing and version control is messy
No good tools for digital trend research at the speed we need
Tools don’t integrate — lots of re-entering data between systems
No centralized searchable design archive
Presentation creation for line reviews is too manual
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q30. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

AI is only as good as the data it learns from. We need to understand what design data exists, how it’s organized, and where the gaps are.

Q30. What data does your design team generate or work with regularly? (Select all that apply)

Q30. Where do your design files and data primarily live? (Select all that apply)

Centric PLM
Shared network drive / file server
Adobe Creative Cloud / local machines
Email attachments
Personal folders or desktops
Cloud storage (OneDrive, SharePoint)
Scattered across all of the above
Other (please describe): _______________

Q30. How would you rate the quality and organization of your design data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
File naming is inconsistent (hard to find the right version of a design)
Past season archives are disorganized or hard to search
Design files and PLM records don’t always match
We lose track of which colorways/styles were approved vs. dropped
Sales/performance data on past designs is hard to access or doesn’t exist
No consistent categorization system across seasons (hard to compare)

Q30. Can you easily answer: “What were our best-selling design features (embroidery, prints, washes, etc.) over the last 3 seasons?” If not, what’s missing?

[Your response]

Q30. What design-related data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Sales performance by design feature (not just by style)
Trend data tied to our product categories (what’s selling across the market)
Complete searchable archive of all past designs
Historical colorway performance (which colors sell, which don’t)
Customer/buyer feedback linked to specific designs
Competitor design tracking
Fabric/material performance data (which materials customers prefer)
Other (please describe): _______________

Q30. Who is responsible for organizing and maintaining design data (file naming, PLM entry, archiving)? Is there a consistent process?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q30. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q30. Have you or your team experimented with AI tools for design-related tasks? (Select all that apply)

AI image generation (Midjourney, DALL-E, Firefly)
ChatGPT for concept descriptions or naming
AI-powered trend analysis tools
AI color/palette tools
Microsoft Copilot
Haven’t tried any
Other (please describe): _______________

Q30. How do you feel about AI in the creative process? (Select all that apply)

Excited — it could free up time for real creativity
Curious but cautious — depends on how it’s used
Neutral — would need to see proof it works for apparel
Skeptical — design needs a human touch
Concerned — AI-generated designs could hurt quality/originality
Other (please describe): _______________

Q30. If you could wave a magic wand and change one part of your design workflow, what would it be?

[Your response]

Q30. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

Final Questions

Q30. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q30. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q30. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Design.

— Sunzi.io

📄 Tech Design Department

Tech Design Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Note: With all manufacturing offshore (India, China, etc.), Tech Design’s accuracy and communication is critical — specs need to be clear enough that factories thousands of miles away can produce the right product. This questionnaire reflects that reality.

Section 1: People (Team & Capabilities)

Understanding your team’s expertise and where people-related challenges exist.

Q1. Describe your Tech Design team’s structure — roles, headcount, and how work is divided. (e.g., technical designers, pattern makers, graders, PLM specialists?)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Q3. How dependent is your department on specific individuals? If your most experienced tech designer was out for two weeks, what would break?

[Your response]

Q4. What skill or capability would make the biggest difference if your team had it? (e.g., 3D fitting expertise, advanced PLM skills, pattern automation, etc.)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Work Gets Done)

Understanding how tech design work flows from design handoff to production-ready specs.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — spec creation, grading, fit sessions, sample reviews, communicating specs to offshore factories, managing sample rounds across time zones, PLM management, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs create delays or errors?

[Your response]

Q8. What’s the most tedious or error-prone part of the tech design process? What causes the errors?

[Your response]

Q9. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our tech design process is well-documented — new team members can follow it
Entering data into Centric PLM takes too much time relative to its value
We frequently re-enter the same information in multiple places or formats
Fit comments and correction notes are easy to track across sample rounds
Our spec sheets are standardized and consistent across styles
We lose time working around limitations in our PLM system
Handoffs from Design are clear — we rarely need to chase missing info
Our grading rules are consistent and well-documented

Q10. How much of your team’s work involves manual data entry or reformatting information that already exists somewhere else?

More than 50% — it’s a huge part of the job
25-50% — significant but manageable
10-25% — noticeable but not the main bottleneck
Less than 10% — our systems handle most of it

Q11. PLM customization can be expensive ($25K for a size field, for example). What other PLM workarounds or limitations does your team deal with?

[Your response]

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q12. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools power your work and where the tech stack falls short.

Q13. What tools does your Tech Design team use daily? (Select all that apply)

Centric PLM
Adobe Illustrator
Excel / Google Sheets (for specs, measurements, etc.)
Pattern making software (Gerber, Optitex, Lectra)
3D fitting tools (Browzwear, CLO)
Email (for vendor communication)
Physical measurement tools
Microsoft Office (Word, PowerPoint)
Other (please describe): _______________

Q14. How well do your tools work together? When you create a spec in one system, how easily does that information flow to PLM, to vendors, and to production?

[Your response]

Q15. What are your biggest technology frustrations? (Select all that apply)

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q16. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

Tech Design generates critical product data. We need to understand how clean, consistent, and accessible it is.

Q17. What data does your Tech Design team generate or maintain? (Select all that apply)

Q18. Where does your critical technical data primarily live? (Select all that apply)

Centric PLM (primary)
Excel / Google Sheets (specs, measurements, tracking)
Email (vendor correspondence, fit notes)
Pattern software files (Gerber, Optitex, Lectra)
Physical paper records or binders
Personal computers / local files
Scattered across all of the above
Other (please describe): _______________

Q19. How would you rate the quality and consistency of your technical data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Spec sheets have inconsistencies between styles (formatting, measurement points)
PLM data has gaps, missing fields, or outdated information
Same data entered in multiple places with conflicting versions
Historical fit/grading data is hard to find or reference
Vendor-facing specs don’t always match internal records
Measurement tolerances or grading rules aren’t standardized across styles
Construction details aren’t documented consistently

Q20. Is there a single source of truth for product specifications? If a vendor and your PLM show different measurements, which one is right and how do you resolve it?

[Your response]

Q21. What technical data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Complete fit history by style (all rounds, comments, corrections in one place)
Historical grading data to predict fit for new styles
Vendor-specific construction capabilities and limitations
Standardized measurement database across all styles/seasons
Pattern efficiency data (fabric utilization by style)
Quality defect history linked to specific specs or construction methods
Cross-style comparison data (which specs produce the best fit)
Other (please describe): _______________

Q22. Who is responsible for data entry and data accuracy in PLM? Is there a review process, or do errors get caught downstream?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q23. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q24. Has anyone on your Tech Design team used AI tools for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to technical design work
Other (please describe): _______________

Q25. On a scale of 1–5, how open would your team be to AI tools that assist with spec creation, grading, or PLM data entry?

(1 = Very resistant — 5 = Very open)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q26. If an AI tool could handle one part of your workflow automatically, which would save your team the most time?

[Your response]

Q27. Which of these AI-powered capabilities would be useful for Tech Design? (Select all that apply)

Final Questions

Q28. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q29. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q30. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Tech Design.

— Sunzi.io

📄 Sourcing Department

Sourcing Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Note: With all manufacturing happening offshore (India, China, and other international locations), sourcing is the critical bridge between Westmoor MFG and its global supply base. This questionnaire reflects that reality.

Section 1: People (Team & Capabilities)

Understanding your team and where people-related gaps exist.

Q1. Describe your Sourcing team’s structure — roles, headcount, and how responsibilities are divided. (e.g., by material type, by region/country, by brand, by vendor relationship?)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Q3. How is institutional knowledge captured? If your lead sourcing manager left tomorrow, how much vendor/pricing/quality knowledge would walk out the door?

[Your response]

Q4. What skill or capability would make the biggest difference for your team? (e.g., data analytics, vendor negotiation, compliance expertise, market intelligence, etc.)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Sourcing Work Gets Done)

Understanding how sourcing flows from material need to vendor confirmation.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — international vendor evaluation, RFQ management, cost negotiation, compliance tracking, sample coordination across time zones, material sourcing, import logistics, tariff/duty management, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down?

[Your response]

Q8. What’s the most frustrating or time-consuming part of the sourcing process? What causes it?

[Your response]

Q9. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our sourcing processes are documented — a new team member could follow them
We can quickly compare vendor pricing and capabilities for a given material
Tracking vendor compliance (social, environmental, quality) is manageable
Communication with overseas vendors (time zones, language) causes delays
We have a reliable system for tracking cost history across seasons
Handoffs between Sourcing and Production/Purchasing go smoothly
We spend too much time on manual cost sheet updates and comparisons
Vendor performance tracking is systematic and data-driven

Q10. How many active vendors/factories does your team manage, and how do you keep track of their performance?

[Your response]

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q11. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools you use and where the technology falls short.

Q12. What systems do you use for sourcing-related work? (Select all that apply)

Q13. Where do you go when you need historical data — like past vendor pricing, lead times, or quality records?

[Your response]

Q14. What are your biggest technology frustrations? (Select all that apply)

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q15. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

Sourcing data — vendor pricing, lead times, quality records, compliance status — is critical for AI-driven supply chain optimization.

Q16. What data does your Sourcing team generate or rely on regularly? (Select all that apply)

Q17. Where does your sourcing data primarily live? (Select all that apply)

Centric PLM
Momentous ERP
Excel / Google Sheets
Email threads (pricing discussions, vendor correspondence)
WhatsApp or WeChat (vendor conversations)
Personal files or notebooks
Scattered across all of the above
Other (please describe): _______________

Q18. How would you rate the quality and consistency of your sourcing data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Vendor pricing history is incomplete or inconsistent across seasons
Lead time data is unreliable or not tracked systematically
Quality/defect records aren’t linked to specific vendors or materials
Compliance certifications are hard to track (expired, missing, scattered)
Same vendor information entered differently in PLM vs. ERP vs. spreadsheets
Historical cost trends are hard to pull together for negotiation
RFQ comparison data isn’t retained in a usable format

Q19. Is there a single source of truth for vendor information? Or do different people have different records?

[Your response]

Q20. What sourcing data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Vendor performance scorecards (on-time %, quality %, cost trends over time)
Material cost trend data (market pricing for key fabrics/trims)
Complete compliance dashboard (who’s current, who’s expiring, who’s at risk)
Historical RFQ comparison data (which vendors quoted what, who won, why)
Lead time predictions based on historical patterns
Cross-vendor quality comparison data
Total cost of ownership analysis (not just unit price)
Other (please describe): _______________

Q21. Who is responsible for maintaining vendor data? When pricing or lead times change, how quickly and consistently does that get updated?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q22. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q23. Has anyone on your Sourcing team used AI tools for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to sourcing work
Other (please describe): _______________

Q24. On a scale of 1–5, how helpful would it be to have AI assist with vendor analysis, cost comparisons, or compliance tracking?

(1 = Not helpful at all — 5 = Extremely helpful)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q25. If you could automate one part of the sourcing process, what would it be and why?

[Your response]

Q26. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

Final Questions

Q27. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q28. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q29. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Sourcing.

— Sunzi.io

📄 Buying & Purchasing Department

Buying/Purchasing Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Note: With all manufacturing offshore and shipments coming from India, China, and other international locations, buying and purchasing involves managing international POs, ocean freight, customs, tariffs, and lead times measured in weeks or months. This questionnaire goes deep on purpose.

Section 1: People (Team & Capabilities)

Understanding your team and where people-related challenges exist.

Q1. Describe your Buying/Purchasing team’s structure — roles, headcount, and how responsibilities are divided. (e.g., by vendor type? By product category? PO creation vs. shipment tracking vs. logistics?)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Q3. If your lead purchasing person was out for two weeks during peak season, how much would break? What specifically would fall through the cracks?

[Your response]

Q4. What skill or training would make the biggest difference for your team’s effectiveness?

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Purchasing & Tracking Works)

Understanding how purchasing and shipment tracking actually works — every step.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — international PO creation, ocean/air freight tracking, vendor communication across time zones, customs and import clearance, tariff management, receiving/reconciliation, logistics coordination, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down?

[Your response]

Q8. Walk us through your shipment tracking process specifically. From the moment a PO is placed, how do you track it until it arrives? What tools do you use at each stage?

[Your response — be as detailed as possible, this is a key area]

Q9. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our purchasing processes are well-documented — someone new could follow them
We always know where our shipments are and when they’ll arrive
PO creation and management is streamlined and efficient
We spend too much time chasing vendors for shipment updates
When a shipment is delayed, we find out quickly enough to adjust
Reconciling deliveries against POs is fast and accurate
Handoffs between Purchasing and other departments are smooth
We have good visibility into seasonal volume patterns and can plan ahead

Q10. How many shipments does your team typically track at any given time?

Q11. What’s the most frustrating or time-consuming part of the purchasing and shipment tracking process? What causes the delays?

[Your response]

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q12. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools support purchasing and where technology gaps exist.

Q13. What tools do you use for purchasing and shipment tracking? (Select all that apply)

Momentous ERP
Excel / Google Sheets
Carrier tracking websites (ocean carrier portals, FedEx, UPS, etc.)
Freight forwarder portals/tools
Email (primary vendor communication)
Customs broker portals / import management software
WhatsApp or WeChat (overseas vendor communication)
Currency conversion or international payment tools
Other (please describe): _______________

Q14. How is shipment status information currently consolidated? If someone asks “Where are all our outstanding shipments?”, how long does it take to answer?

Under 5 minutes — we have a live dashboard or consolidated tracker
15-30 minutes — need to pull from a few sources
1-2 hours — requires significant manual compilation
Longer than 2 hours — data is very scattered
We can’t easily answer that question right now

Q15. What are your biggest technology frustrations? (Select all that apply)

No unified shipment tracking — have to check multiple carrier websites
ERP doesn’t handle PO/shipment workflow well
Too much manual data entry between systems
No automated alerts for delays or exceptions
Can’t easily see historical vendor performance (on-time, quality, cost trends)
Systems don’t connect — purchasing, receiving, and production data are siloed
No way to predict delivery dates based on historical patterns
Customs and import documentation is manual
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q16. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

Purchasing data — PO records, shipment tracking, delivery performance — is essential for AI-powered logistics optimization.

Q17. What data does your Purchasing team generate or rely on regularly? (Select all that apply)

Purchase order records (PO numbers, quantities, dates, vendors)
Shipment tracking data (carrier, ETAs, status updates)
Delivery receipts and receiving records
Vendor pricing and cost history
Freight and logistics costs
Customs and import documentation
Vendor communication logs (status requests, confirmations)
Discrepancy records (short shipments, wrong items, damages)
Other (please describe): _______________

Q18. Where does your purchasing data primarily live? (Select all that apply)

Momentous ERP
Excel / Google Sheets
Carrier websites (FedEx, UPS, ocean carrier portals)
Freight forwarder portals
Email threads with vendors
Personal files or trackers
Scattered across all of the above
Other (please describe): _______________

Q19. How would you rate the quality and consistency of your purchasing data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
PO data in ERP doesn’t always match actual shipment status
Shipment tracking info is scattered across carrier websites and emails
Receiving records don’t match POs (quantity discrepancies unresolved)
Historical vendor performance data (on-time %, accuracy) isn’t tracked
Freight cost data is hard to analyze across carriers/routes
Lead time data isn’t reliable enough for planning
No consistent process for logging exceptions and discrepancies

Q20. Is there a single source of truth for shipment status? If the CEO asks “Where are all our outstanding shipments?”, can you pull that from one place?

[Your response]

Q21. What purchasing data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Unified real-time shipment tracking (all carriers, all POs, one dashboard)
Vendor delivery performance history (on-time %, accuracy %)
Freight cost analysis by carrier/route/vendor
Accurate lead time data for planning purposes
PO-to-delivery reconciliation (automatic matching)
Historical cost trends for key materials/vendors
Exception and discrepancy trend analysis
Other (please describe): _______________

Q22. Who is responsible for entering and updating purchasing data? Is there a consistent process, or does it depend on the person?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q23. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q24. Has anyone on your Purchasing team used AI tools for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to purchasing
Other (please describe): _______________

Q25. On a scale of 1–5, how much would AI-powered shipment tracking and PO management improve your team’s effectiveness?

(1 = Minimal improvement — 5 = Transformative improvement)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q26. If you could fix one thing about the purchasing and tracking process, what would it be?

[Your response]

Q27. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

Final Questions

Q28. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q29. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q30. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Buying/Purchasing.

— Sunzi.io

📄 Credit Department

Credit Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Section 1: People (Team & Capabilities)

Understanding who manages credit risk and where team-related gaps exist.

Q1. Describe your Credit team’s structure — roles, headcount, and how credit responsibilities are divided. (e.g., credit manager, credit analyst, collections specialist, shared with Finance/AR, etc.)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Q3. If your credit manager or most experienced person was out for two weeks during peak order season, what would happen? What institutional knowledge lives in their head?

[Your response]

Q4. What’s the #1 skill gap on your team right now? (e.g., financial analysis, risk assessment frameworks, negotiation/collections skills, data analytics, international credit evaluation, etc.)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Credit Decisions & Collections Work)

Understanding how credit decisions, collections, and risk management actually work — not the policy manual version, the real version.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — credit applications, limit setting, credit holds, collections, account reviews, write-offs, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down?

[Your response]

Q8. How do you determine credit limits for new retail accounts? What factors do you consider, and how consistent is the process across different account types?

[Your response]

Q9. Describe your collections process. When does an account move from “invoice sent” to “collections effort”? What are the escalation steps?

[Your response]

Q10. How do you handle credit holds on orders? Who makes the decision to hold or release, and how quickly can that happen?

[Your response]

Q11. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our credit processes are well-documented — someone new could follow them
Credit application turnaround is fast enough for Sales’ needs
We proactively review existing credit limits on a regular schedule
Collections efforts are systematic, not ad hoc
Credit hold decisions are made quickly with clear criteria
We rarely have unexpected write-offs — our process catches problems early
Handoffs between Credit and other departments (Sales, CS, Finance) are smooth
We can easily see the full picture of an account’s health (orders, payments, issues)

Q12. How often do you review existing credit limits? Is it proactive (scheduled reviews) or reactive (only when there’s a problem)?

[Your response]

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q13. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools support credit operations and where technology creates friction.

Q14. What tools does your Credit team use regularly? (Select all that apply)

Momentous ERP (AR, customer records, order management)
Excel / Google Sheets (tracking, analysis, aging reports)
Third-party credit reporting services (D&B, Experian, Cortera, etc.)
Email (primary communication with sales reps and customers)
Collections software or platforms
Credit insurance tools
Phone system (collections calls)
Other (please describe): _______________

Q15. When a Sales rep calls asking about a customer’s credit status or available limit, how quickly can you pull that information?

Instantly — it’s at my fingertips in one system
Within a few minutes — need to check one or two sources
15-30 minutes — requires pulling from multiple places
It depends — some accounts are easy, others require digging
We can’t always answer quickly — data is scattered

Q16. Do you use any third-party credit reporting services? If yes, how often do you pull reports, and what do you do with that data?

[Your response]

Q17. Do your systems flag at-risk accounts automatically, or do you discover problems manually (e.g., when payments are late)?

[Your response]

Q18. What are your biggest technology frustrations? (Select all that apply)

No dedicated credit management system — everything is manual or ERP-based
Can’t easily see a complete account picture (credit limit, balance, aging, order history)
AR aging reports require manual compilation
No automated alerts for overdue accounts or limit breaches
Credit application processing is paper-based or email-based
Systems don’t connect — credit data, sales data, and payment data are siloed
No way to score or rank accounts by risk level automatically
Collections tracking is manual (spreadsheets, notes, memory)
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q19. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

Credit data — payment history, account health, risk signals — is a goldmine for AI-powered risk management. We need to understand what’s being captured and how clean it is.

Q20. What data does your Credit team generate or rely on regularly? (Select all that apply)

Q21. Where does your credit data primarily live? (Select all that apply)

Momentous ERP (AR module)
Excel / Google Sheets
Email threads (applications, correspondence, collections notes)
Third-party credit reporting platforms
Paper files
Personal files or notebooks
Scattered across all of the above
Other (please describe): _______________

Q22. How would you rate the quality and consistency of your credit data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Customer credit records are incomplete or outdated
Payment history is hard to pull together across systems
Collections activity isn’t logged consistently
No systematic tracking of credit risk trends over time
Write-off and bad debt causes aren’t analyzed
AR aging data doesn’t always match actual payment status
Account health data (credit + sales + payments) isn’t unified

Q23. How do you currently identify high-risk accounts before they become write-offs? What early warning signals do you look for?

[Your response]

Q24. What credit data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Automated risk scoring for every account (based on payment patterns, order trends, external data)
Real-time AR aging dashboard with auto-flagging of at-risk accounts
Customer payment pattern analysis (trending better/worse over time)
Collections effectiveness metrics (what approaches work, what doesn’t)
Complete account health view (credit + sales + payments + issues in one place)
Industry benchmarking (how our credit metrics compare to peers)
Predictive write-off analysis (which accounts are likely to default)
Other (please describe): _______________

Q25. Who is responsible for entering and maintaining credit data? Is it consistent, or does it depend on the person?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q26. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q27. Has anyone on your Credit team used AI tools for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to credit work
Other (please describe): _______________

Q28. On a scale of 1–5, how helpful would AI-powered tools be for credit assessment, risk monitoring, or collections?

(1 = Not helpful at all — 5 = Extremely helpful)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q29. If you could automate or dramatically speed up ONE thing in Credit, what would it be?

[Your response]

Q30. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

Final Questions

Q31. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q32. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q33. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Credit.

— Sunzi.io

📄 Customer Service Department

Customer Service Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Section 1: People (Team & Capabilities)

Understanding your team and where people-related challenges exist.

Q1. Describe your Customer Service team’s structure — roles, headcount, and how work is divided. (e.g., by channel — phone vs. email? By account type? By issue type?)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Not enough people — call/email volume exceeds what the team can handle
High turnover — training new CS reps is constant
Product knowledge takes too long to build — new reps can’t answer questions quickly
Key account knowledge lives in specific reps’ heads (what happens if they’re out?)
CS reps spend too much time looking up information instead of helping customers
Team morale is affected by repetitive inquiries and complaint volume
Coordination with other departments (Sales, Warehouse, Production) is difficult
No career growth path — hard to retain good CS people
Other (please describe): _______________

Q3. How long does it take a new Customer Service rep to become fully effective? What’s the biggest challenge during ramp-up?

[Your response]

Q4. What skill or resource would make the biggest difference for your CS team’s effectiveness?

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Customer Service Gets Done)

Understanding how customer service works from inquiry to resolution.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — inquiry handling, returns/exchanges, escalations, order status requests, complaint resolution, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down?

[Your response]

Q8. When customers call about credit holds on orders, order status questions, or shipping delays, how do you coordinate with Credit, Warehouse, and Sales? What works? What doesn’t?

[Your response]

Q10. What types of customer inquiries take the longest to resolve, and why?

[Your response]

Q10. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our CS processes are well-documented — a new rep can follow them
We can quickly look up order status and give customers accurate information
A large portion of inquiries are repetitive (same questions, different customers)
We have easy access to product information (specs, availability, care instructions)
Returns and credits processing is fast and straightforward
Our team spends too much time on tasks that could be automated
Escalation paths are clear — we know when and how to escalate
Handoffs to other departments (Sales, Warehouse, Production) are smooth

Q11. What percentage of incoming inquiries would you estimate are “routine” (order status, basic product questions, standard returns) vs. complex?

Over 75% routine
50-75% routine
25-50% routine
Under 25% routine — most issues are complex

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q12. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools your CS team uses and where technology falls short.

Q13. What tools does your Customer Service team use? (Select all that apply)

Momentous ERP (order lookup, processing)
Email (primary customer communication)
Phone system
CRM or ticketing system (which one?): _______________
Live chat
Excel / Google Sheets (tracking)
Shared knowledge base or FAQ documents
Social media (for customer inquiries)
Other (please describe): _______________

Q14. When a customer asks about a product (sizing, availability, materials), where does your team find that information? (Select all that apply)

Q15. What are your biggest technology frustrations? (Select all that apply)

No ticketing system — everything is managed via email/phone
Can’t easily see order status, inventory, and product info in one place
No knowledge base — reps have to know everything from memory
Systems are slow — customer wait times are affected
No way to track customer satisfaction or identify repeat issues
Tools don’t connect — have to switch between multiple systems to help one customer
No customer self-service options (order tracking, FAQ, etc.)
Can’t track or report on inquiry types and volumes
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q16. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

Customer service data — inquiry types, resolution times, repeat issues, customer satisfaction — is a goldmine for AI optimization.

Q17. What data does your Customer Service team generate or work with regularly? (Select all that apply)

Q18. Where does your customer service data primarily live? (Select all that apply)

Momentous ERP (order lookups)
Email (primary record of customer interactions)
CRM or ticketing system
Excel / Google Sheets (tracking)
Phone system call logs
Personal notebooks or files
Scattered across all of the above
Other (please describe): _______________

Q19. How would you rate the quality and consistency of your customer service data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Customer interactions aren’t logged consistently
No way to categorize or tag inquiry types (can’t report on most common issues)
Resolution times aren’t tracked
Repeat/recurring issues aren’t flagged or linked together
Product information is scattered — reps check multiple sources
Customer history is hard to see (previous interactions, orders, issues in one place)
No feedback loop — CS insights don’t reach the departments that could fix root causes

Q20. If someone asked “What are the top 5 reasons customers contact us?”, could you answer with data? Or would it be a guess?

We can answer with data — we track and categorize inquiries
We could get close — some tracking exists but not comprehensive
It would be an educated guess — we know anecdotally but don’t have data
We’d be guessing — no systematic tracking of inquiry types

Q21. What customer service data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Inquiry categorization (automated tracking of what customers ask about)
Resolution time metrics (how long issues take, by type)
Customer satisfaction scores or feedback
Complete customer history in one view (all orders, all interactions, all issues)
Product information database that’s always current (sizing, availability, specs)
Repeat issue detection (same problem across multiple customers)
Root cause analysis data (which problems are systemic vs. one-offs)
Other (please describe): _______________

Q22. Who is responsible for logging and maintaining customer interaction data? Is it consistent, or does it depend on the rep?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q23. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q24. Has anyone on your Customer Service team used AI tools for work tasks? (Select all that apply)

Yes, regularly (drafting responses, looking up info)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to customer service
Other (please describe): _______________

Q25. On a scale of 1–5, how helpful would an AI-powered tool be for handling routine customer inquiries?

(1 = Not helpful — customers need a human — 5 = Extremely helpful — would save significant time)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q26. If you could change one thing about how customer service operates at Westmoor MFG, what would it be?

[Your response]

Q27. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

AI chatbot or self-service portal for routine inquiries (order status, basic product info)
Automated order status lookups (customer enters order # and gets instant update)
Smart response suggestions (AI drafts a reply based on the inquiry, you review and send)
Automated ticket classification and priority routing
Product knowledge base that’s easy to search and always up to date
Customer sentiment tracking (identify unhappy accounts before they escalate)
Automated returns/credits processing for standard cases
Repeat issue alerting (flag when the same problem keeps appearing)
Other (please describe): _______________

Final Questions

Q28. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q29. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q29. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Customer Service.

— Sunzi.io

📄 Human Resources Department

HR Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Section 1: People (The HR Team & Workforce Challenges)

Understanding your HR team itself and the broader workforce challenges you manage.

Q1. Describe your HR team’s structure — roles, headcount, and primary responsibilities. (e.g., recruiting, benefits, payroll, training, compliance, employee relations, etc.)

[Your response]

Q2. What are your biggest challenges related to the overall workforce? (Select all that apply)

Q3. What’s the current turnover situation, and which roles are hardest to fill? How long does a typical hire take from posting to start date?

[Your response]

Q4. How is company knowledge transferred when employees leave? Is there a formal process, or does institutional knowledge walk out the door?

[Your response]

Q5. If you could add ONE capability to your HR function, what would it be? (e.g., better analytics, automated onboarding, employee self-service, predictive retention, etc.)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q6. Safety Net: What should we have asked about your people and workforce that we didn’t?

[Your response]

Section 2: Processes (How HR Work Gets Done)

Understanding how HR processes work day-to-day.

Q7. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — recruiting, onboarding, benefits administration, payroll, performance reviews, compliance, training, employee relations, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q8. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down?

[Your response]

Q9. What’s the most time-consuming part of HR operations that you wish could be faster or simpler?

[Your response]

Q10. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our HR processes are well-documented — a new HR team member could follow them
Our onboarding process is smooth and consistent for every new hire
Recruiting for production/warehouse roles is a challenge
We have good visibility into employee training completion and compliance
Answering routine employee questions (benefits, PTO, policies) takes too much time
Our HR systems are connected and data doesn’t need to be entered multiple times
Handoffs between HR and other departments (payroll, management) are smooth
We can generate workforce analytics reports easily

Q11. How do employees currently get answers to routine HR questions (benefits, PTO balance, policy questions)? (Select all that apply)

Ask HR directly (email, phone, in-person)
Self-service portal (which system?): _______________
Employee handbook / shared documents
Manager handles most questions
A mix — depends on the question
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q12. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools support HR operations and where gaps exist.

Q13. What HR systems and tools does your team use? (Select all that apply)

HRIS/HCM system (which one?): _______________
Payroll system (if separate): _______________
Applicant tracking system (ATS): _______________
Excel / Google Sheets (for tracking)
Microsoft Teams (internal communication)
Learning management system (LMS)
Benefits administration platform
Background check service
Other (please describe): _______________

Q14. What percentage of HR processes would you say are still primarily manual (paper forms, spreadsheet tracking, email-based approvals)?

Over 75% — most things are manual
50-75% — significant manual work
25-50% — a mix of manual and digital
Under 25% — mostly automated/digital

Q15. What are your biggest technology frustrations? (Select all that apply)

No HRIS or our HRIS is inadequate
Too many manual processes (paper forms, spreadsheets)
HR systems don’t integrate with payroll or other company systems
No employee self-service — everything comes through HR
Can’t easily generate reports on workforce metrics (turnover, headcount, training)
No applicant tracking system — recruiting is managed via email/spreadsheets
Compliance tracking is manual and error-prone
No centralized place for HR policies and documents
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q16. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

HR data — employee records, recruiting pipelines, training completion, performance data — underpins workforce AI.

Q17. What data does your HR team generate or maintain? (Select all that apply)

Q18. Where does your HR data primarily live? (Select all that apply)

HRIS / HCM system
Excel / Google Sheets
Payroll system (separate from HRIS)
Email (applications, approvals, correspondence)
Paper files and filing cabinets
Personal files / local computers
Scattered across all of the above
Other (please describe): _______________

Q19. How would you rate the quality and consistency of your HR data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Employee records are incomplete or outdated (titles, departments, contacts)
Training/certification completion is hard to track or verify
Same employee data entered in multiple systems (HR, payroll, benefits)
Recruiting pipeline data isn’t captured consistently
Turnover data isn’t analyzed — people leave and we don’t track patterns
No centralized view of workforce data (headcount, skills, compliance status)
Organizational structure data is outdated or inaccurate

Q20. Is there a single source of truth for employee information? If someone asks “How many people work here and what departments are they in?”, can you answer instantly?

[Your response]

Q21. What HR data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Turnover trends and predictors (why people leave, who’s at risk)
Skills inventory (what capabilities exist across the workforce)
Training completion and compliance dashboard
Recruiting pipeline metrics (time-to-hire, source effectiveness, offer acceptance rate)
Employee engagement/satisfaction trends over time
Workforce planning data (headcount vs. demand by department)
Total compensation analysis across roles
Other (please describe): _______________

Q22. Who is responsible for maintaining employee data accuracy? When someone changes roles, gets a raise, or completes training, how quickly and consistently does that get updated?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q23. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q24. Has anyone on your HR team used AI tools for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (drafting job descriptions, summarizing policies, etc.)
Tried it once or twice
No, but interested
No, and concerned about AI in HR contexts (privacy, bias)
Other (please describe): _______________

Q25. On a scale of 1–5, how open would your HR team be to AI-assisted tools for recruiting, onboarding, or employee self-service?

(1 = Very resistant — 5 = Very open)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q26. If you could wave a magic wand and improve one thing about HR operations at Westmoor MFG, what would it be?

[Your response]

Q27. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

Final Questions

Q28. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q29. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q30. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for HR.

— Sunzi.io

📄 Merchandise Planning Department

Merchandise Planning Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Note: We know demand forecasting and planning are major pain points. This questionnaire goes deep on purpose — your answers here will directly shape what’s likely to be the highest-impact recommendation in the entire audit.

Section 1: People (Team & Capabilities)

Understanding your team and where people-related challenges exist.

Q1. Describe your Merchandise Planning team’s structure — roles, headcount, and how work is divided. (e.g., demand planners, allocators, merchandise analysts, etc.)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Not enough people — we’re handling too much with too few
We need data/analytics skills we don’t currently have
Forecasting expertise is concentrated in one or two people
We spend more time building spreadsheets than analyzing them
Getting timely input from Sales, Design, or Production is a constant struggle
Team is overwhelmed during seasonal planning crunch times
Lack of statistical or data science skills for advanced forecasting
Cross-training is minimal — people own their domains exclusively
Other (please describe): _______________

Q3. If your lead planner was out for a month, could someone else run the forecasting process? What would break?

[Your response]

Q4. What skill or capability would make the biggest difference for your team? (e.g., advanced analytics, data visualization, statistical forecasting, scenario modeling, etc.)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Planning & Forecasting Works)

Understanding how planning and forecasting actually works — the steps, dependencies, and pain points.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — demand forecasting, buy planning, assortment planning, OTB management, seasonal hindsight, inventory analysis, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs create delays or bad data?

[Your response]

Q8. Walk us through your demand forecasting process specifically. Where does the data come from, what tools do you use, and how long does a typical forecast take to build?

[Your response — be as detailed as possible, this is critical]

Q9. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our planning processes are documented — a new planner could follow them
Our current demand forecasting process is accurate enough for our needs
We spend too much time building and maintaining Excel spreadsheets
We can easily see real-time inventory levels across all channels
Historical sales data is easy to access and analyze when we need it
We feel confident in our buy quantities — not too much overstock, not too many missed sales
Our tools can handle the complexity of sizing, colorways, and regional differences
Dependencies on other departments slow our process down

Q10. How often do forecasts turn out to be significantly off? What are the most common causes? (Select all that apply)

Frequently — it’s one of our biggest challenges
Sometimes — certain categories are harder to predict
Rarely — our process is solid but could be faster
We don’t measure forecast accuracy consistently

Common causes of inaccuracy: - [ ] Insufficient historical data - [ ] External factors we can’t predict (trends, weather, economy) - [ ] Late input from Sales or Design - [ ] Data spread across too many spreadsheets - [ ] Manual errors in calculations - [ ] Lack of granularity in size/color data - [ ] No good way to account for new styles without history - [ ] Other (please describe): _______________

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q11. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools power your planning process and where the biggest technology gaps are.

Q12. What tools does your team use for planning and forecasting? (Select all that apply)

Excel (primary tool)
Momentous ERP
Centric PLM
Dedicated planning/forecasting software (which?): _______________
Business intelligence/reporting tools (Power BI, Tableau, etc.)
Email (for coordinating with Sales/Design)
Other (please describe): _______________

Q13. What data sources feed into your planning decisions? (Select all that apply)

Q14. How much of your data manipulation is done in Excel vs. automated systems?

80%+ Excel — it’s the backbone of everything
60-80% Excel — ERP handles some, but Excel fills the gaps
40-60% — split between Excel and system-generated reports
Under 40% — most is automated, Excel is for ad hoc analysis

Q15. What are your biggest technology frustrations? (Select all that apply)

Excel is our primary tool and it can’t handle our complexity
Data is scattered across too many systems — hard to get a unified view
No real-time visibility into inventory and sell-through
ERP reporting doesn’t give us what we need for planning
No good tools for scenario modeling / what-if analysis
We’re doing sophisticated analytics with unsophisticated tools
Can’t do size-level or color-level analysis efficiently
Version control on Excel files is a nightmare
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q16. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

AI-powered demand forecasting lives or dies on data quality. If the historical sales data, inventory numbers, or market signals feeding a model are messy, the forecasts will be too. This section is critical.

Q17. What data does your Planning team generate or rely on for forecasting and buying decisions? (Select all that apply)

Q18. Where does your critical planning data primarily live? (Select all that apply)

Excel (primary — forecasting models, buy plans, analysis)
Momentous ERP (sales history, inventory, orders)
Email (sales team input, ad hoc reports)
Centric PLM (product data)
Personal files or local computers
Scattered across all of the above
Other (please describe): _______________

Q19. How would you rate the quality and consistency of the data feeding your planning decisions?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Historical sales data has gaps, inconsistencies, or is hard to pull together
Inventory data doesn’t reflect real-time reality (discrepancies between systems)
Size/color level detail is insufficient for accurate forecasting
Sales team projections are inconsistent in format, timing, or reliability
Data from different sources (ERP, Excel, PLM) doesn’t match
Past forecast accuracy is hard to measure because we don’t track it systematically
Product categorization is inconsistent (hard to compare like-for-like across seasons)

Q20. How trustworthy is your historical sales data? Could you hand 3 years of clean, consistent sales-by-style-by-size-by-color data to an AI model today?

Yes — our data is clean and accessible
Mostly — with some cleanup work it would be usable
Partially — some seasons/categories are clean, others aren’t
No — it would take significant effort to assemble and clean the data
Not sure — we’ve never tried to pull it together that way

Q21. What planning-related data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Retailer POS data (actual sell-through at store level)
Clean size curve data by category and region
Automated forecast-vs-actual comparison across seasons
External data (trends, weather, economic indicators) tied to our categories
Real-time inventory by location/channel
Customer/retailer buying pattern analysis
Product attribute-level performance data (not just style-level)
Other (please describe): _______________

Q22. Who owns the “master data” for planning? When ERP, Excel, and the sales team show different numbers, who has the right answer?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q23. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q24. Has your team used or explored any AI or advanced analytics tools for planning? (Select all that apply)

Yes — using or piloting something now (please name it): _______________
Explored options but haven’t implemented
No, but very interested — it’s a top priority
No, and not sure where to start
No, and skeptical it would work for our business
Other (please describe): _______________

Q25. What would make you trust an AI-generated forecast? (Select all that apply)

Ability to see how it arrived at its numbers (transparency)
Proven accuracy over several seasons
Ability to adjust/override the AI’s suggestions
Integration with our existing Excel workflows
Recommendation from a peer or industry contact who uses it
Low cost and easy setup
Handles our specific complexity (brands, categories, size curves)
Other (please describe): _______________

Q26. If you could upgrade ONE thing about your planning and forecasting capability, what would it be?

[Your response — dream big]

Q27. Which of these AI-powered capabilities would have the biggest impact? (Select all that apply, and star ⭐ your top 3)

Final Questions

Q28. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q29. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q30. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape what’s likely to be the highest-impact AI recommendation in the entire audit.

— Sunzi.io

📄 Finance Department

Finance Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Section 1: People (Team & Capabilities)

Understanding your team and where people-related challenges exist.

Q1. Describe your Finance team’s structure — roles, headcount, and how work is divided. (e.g., controller, AP clerk, AR clerk, bookkeeper, FP&A analyst, payroll, etc.)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Not enough people — we’re always behind during close or busy periods
Need more analytical skills (financial analysis, data visualization)
Key processes depend on one person who knows the system/workarounds
Cross-training is minimal — if someone’s out, their work stops
Too much time on transactional work vs. strategic analysis
Getting timely information from other departments is difficult
Difficulty recruiting accounting talent in our area/industry
Seasonal close pressure creates unsustainable workloads
Other (please describe): _______________

Q3. How reliant is your department on specific individuals? If your controller or lead accountant was out for two weeks during close, what would happen?

[Your response]

Q4. What skill or capability would make the biggest difference if your team had it? (e.g., data analytics, advanced Excel/BI, financial modeling, automation expertise, etc.)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Finance Work Gets Done)

Understanding how work flows — especially the repetitive and time-consuming parts.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — month-end close, AP/AR processing, reconciliation, budgeting, reporting, payroll, audit prep, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs create delays or errors?

[Your response]

Q8. What’s the most tedious or error-prone part of your finance workflow? What causes the errors?

[Your response]

Q9. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our accounting processes are well-documented — someone new could follow them
Invoice processing (AP) is efficient and mostly automated
Financial reports can be generated quickly when leadership asks for them
We spend too much time on manual data entry or reconciliation
Our ERP (Momentous) gives us the reports and visibility we need
Cash flow forecasting is accurate and timely
Handoffs with other departments (Purchasing, Sales, HR/Payroll) are smooth
We can quickly drill into variances when numbers look off

Q10. How much manual work is involved in reconciling data between systems (ERP, bank, Excel)?

Minimal — our systems handle most of it
Moderate — some manual matching but manageable
Significant — lots of spreadsheet work to reconcile
Extensive — almost everything requires manual reconciliation
Depends on the process — some are automated, some aren’t

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q11. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools power your financial operations and where technology gaps exist.

Q12. What tools does your Finance team use regularly? (Select all that apply)

Momentous ERP (primary accounting system)
Excel / Google Sheets
Separate AP/AR software
Bank portals and reconciliation tools
Budget/forecasting tools (separate from ERP)
Reporting tools (Power BI, Crystal Reports, etc.)
Tax preparation software
Expense management tools
Other (please describe): _______________

Q13. When leadership asks for a financial analysis (e.g., “How did this product line perform last season?”), how long does it take to pull together?

Under 30 minutes — data is readily available
1-2 hours — need to pull from a couple sources
Half a day — requires significant data gathering
A day or more — complex cross-system analysis required
Depends on the question

Q14. What are your biggest technology frustrations? (Select all that apply)

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q15. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

Financial data is some of the most structured in any organization — but reconciliation issues, manual processes, and system gaps can still create problems.

Q16. What financial data does your team generate or maintain? (Select all that apply)

Q17. Where does your financial data primarily live? (Select all that apply)

Q18. How would you rate the quality and consistency of your financial data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
ERP data doesn’t match bank records (reconciliation gaps)
Invoice data is entered inconsistently (vendor names, coding, amounts)
Product costing data is outdated or approximate
Budget data lives in Excel and is hard to compare against actuals in ERP
Historical financial data is hard to pull for trend analysis
Cross-department data (sales, purchasing, production costs) doesn’t reconcile easily
Chart of accounts or coding structure creates reporting challenges

Q19. Is there a single source of truth for financial data? When leadership asks for a number, does everyone give the same answer?

[Your response]

Q20. What financial data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Real-time cash flow visibility (not just monthly snapshots)
Product-level profitability (revenue minus all costs, by style)
Automated budget-vs-actual variance analysis
Aging reports that auto-flag high-risk receivables
Clean historical data for financial trend analysis (3+ years)
Cross-system financial dashboard (ERP + bank + budget in one view)
Cost allocation by department/function
Other (please describe): _______________

Q21. Who is responsible for data accuracy in your financial systems? Is there a review/reconciliation process, or are errors caught downstream?

[Your response]

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q22. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q23. Has anyone on your Finance team used AI tools for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and concerned about accuracy for financial data
Other (please describe): _______________

Q24. On a scale of 1–5, how helpful would AI assistance be for invoice processing, reconciliation, or financial reporting?

(1 = Not helpful — 5 = Extremely helpful)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q25. If you could automate one part of the finance workflow, what would it be?

[Your response]

Q26. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

Final Questions

Q27. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q28. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q29. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Finance.

— Sunzi.io

📄 Sales Department

Sales Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Section 1: People (Team & Capabilities)

Understanding your team structure and where people-related challenges exist.

Q1. Describe how your Sales team is organized — headcount, structure, and how territories or accounts are divided. (e.g., in-house reps vs. field reps? By territory/region? By account type? By brand?)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Not enough salespeople to cover all accounts/territories
Field reps operate independently — hard to get consistent info back
Knowledge of key accounts lives in individual reps’ heads (not in a system)
Training new reps takes too long — no standardized playbook
Sales team lacks data/analytics skills to interpret performance reports
Coordination between inside sales and field reps is clunky
Turnover in sales roles creates knowledge gaps
Reps are unevenly loaded — some territories are much harder than others
Other (please describe): _______________

Q3. How do you handle it when a sales rep leaves? How much account knowledge and relationship history is lost?

[Your response]

Q4. What ONE skill or capability would make your sales team more effective if everyone had it?

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Sales Gets Done)

Understanding how sales flows — from lead to order to fulfillment.

Q6. What are your department’s major recurring processes? For each one, briefly describe what happens, who’s involved, and what the biggest pain point is.

List everything significant — order management, account planning, trade show prep, sales projections, rep coordination, reporting, whatever is real for your team.

Process 1: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 2: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

Process 3: _______________
  - What happens:
  - Who's involved:
  - Biggest pain point:

(Add more as needed)

Q7. For each process above, describe the handoffs — where does work move between people or departments? Where do handoffs break down?

[Your response]

Q8. How does the handoff work between Sales and Credit when opening new accounts or requesting credit limit increases? Where does that process break down or create delays?

[Your response]

Q10. What’s the most frustrating part of the sales process — the thing that wastes time or causes the most errors?

[Your response]

Q10. Rate the following statements from 1 (Strongly Disagree) to 5 (Strongly Agree):

Statement	1	2	3	4	5
Our sales process is well-documented — a new rep could follow it
Order entry is fast and error-free
We have good visibility into what’s selling and what’s not across accounts
Sales reports are easy to pull and understand
We can quickly identify which accounts need attention (declining, late payments, etc.)
Trade show and market week preparation is efficient
Handoffs between Sales and other departments (CS, Production, Planning) are smooth
Field rep activity and results are visible to management

Q11. How does your team currently create sales projections and forecasts? (Select all that apply)

Based on historical orders + gut feel
Excel models built by the Sales team
Input from Merchandise Planning
Roll-up from individual sales reps
A combination of the above
We don’t do formal projections
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q12. Safety Net: What should we have asked about your processes and workflows that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what tools support your sales process and where the gaps are.

Q13. What tools does your Sales team use regularly? (Select all that apply)

Q14. When a buyer asks “What’s new?” or “What’s selling best?”, how quickly can you pull that information?

Instantly — it’s at my fingertips
Within an hour — need to pull some data
Same day — requires some digging
It takes a while — data is scattered
Depends on who’s asking and what they want

Q15. What are your biggest technology frustrations? (Select all that apply)

No CRM or our CRM is inadequate
Order entry is manual and error-prone
Can’t easily see real-time sales/inventory data
Tools don’t connect — information has to be moved manually between systems
Field reps use different tools/methods than the office
Reporting is too manual — we spend hours building reports
No mobile-friendly tools for reps in the field
No way to track buyer interactions and relationship history
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q16. Safety Net: What should we have asked about your technology and tools that we didn’t?

[Your response]

Section 4: Data Quality (The Foundation for AI)

Sales data — order history, account info, performance metrics — drives the most valuable AI opportunities.

Q17. What data does your Sales team generate or rely on regularly? (Select all that apply)

Q18. Where does your sales data primarily live? (Select all that apply)

Q19. How would you rate the quality and consistency of your sales data?

Data Quality Issue	Not a Problem	Minor Issue	Moderate Issue	Major Problem
Account/contact information is outdated or incomplete
Order history is hard to pull or analyze across seasons
Field reps have data in their heads/phones that isn’t in the system
Sales projections from different sources don’t match
No centralized view of account health (orders, payments, issues)
Historical sales data by style/color/size is hard to access
No way to track why accounts grow or shrink

Q20. Is there a single source of truth for sales performance data? When leadership asks “How are we doing?”, does everyone cite the same numbers?

[Your response]

Q21. What sales data would be most valuable to have that you DON’T currently have easy access to? (Select all that apply)

Real-time sell-through by retailer (what’s actually selling at the store level)
Account health dashboard (order trends, payment history, engagement level)
Win/loss analysis (why accounts grow or shrink)
Sales rep performance comparison and territory analytics
Product performance by channel/region (not just company-wide)
Competitive intelligence (what accounts are buying from competitors)
Buyer preference data (what specific buyers tend to order)
Other (please describe): _______________

Q22. Who is responsible for entering and maintaining sales data? Is it consistent, or does each rep do it differently?

[Your response]

Q23. Do you have any formal data governance — naming conventions for accounts, required fields in the ERP/CRM, regular data cleanup? (Select all that apply)

Yes — we have documented standards and enforce them
Somewhat — informal rules, but inconsistently followed
No — everyone does their own thing
Other (please describe): _______________

🔲 GPT Brainstorm Insights — Data Quality: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q24. Safety Net: What should we have asked about your data that we didn’t?

[Your response]

Section 5: AI Readiness & Wishlist

Q25. Has anyone on your Sales team used AI tools for work tasks? (Select all that apply)

Yes, regularly (weekly or more)
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to sales
Other (please describe): _______________

Q26. On a scale of 1–5, how helpful would it be to have AI assist with sales analytics, order processing, or account management?

(1 = Not helpful at all — 5 = Extremely helpful)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q27. If you could snap your fingers and fix one thing about how Sales operates, what would it be?

[Your response]

Q28. Which of these AI-powered capabilities would be useful for your team? (Select all that apply)

Final Questions

Q29. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q30. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q30. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Sales.

— Sunzi.io

📄 Warehouse Department

Warehouse Department — AI Readiness Questionnaire

Westmoor MFG AI Audit | Prepared by Sunzi.io

Before you begin: You should have already completed your brainstorm session with the AI Brainstorm Partner. If you haven’t, do that first — it’s mandatory and will make this questionnaire much faster to complete.

Estimated time: 10-15 minutes (with brainstorm outputs ready to paste)

Note: As the receiving point for all offshore manufacturing (India, China, and other international locations), the Warehouse is where product quality, shipment accuracy, and inventory integrity are first validated. This questionnaire reflects that critical role.

Section 1: People (Team & Capabilities)

Understanding who runs warehouse operations and where the team-related gaps are.

Q1. Describe your Warehouse team’s structure — roles, headcount, and how work is divided. (e.g., warehouse manager, receiving lead, pickers, packers, shippers, QC inspectors, forklift operators, etc.)

[Your response]

Q2. What are your team’s biggest people-related challenges? (Select all that apply)

Q3. If your warehouse manager or most experienced lead was out for a week, what would be most at risk? What knowledge lives in their head?

[Your response]

Q4. What’s the #1 skill gap on your team right now? (e.g., forklift certification, WMS proficiency, quality inspection, inventory accuracy, problem-solving, etc.)

[Your response]

🔲 GPT Brainstorm Insights — People: Paste any relevant people-related insights from your brainstorm session here.

[Paste from brainstorm]

Q5. Safety Net: What should we have asked about your people and team that we didn’t?

[Your response]

Section 2: Processes (How Work Gets Done)

Understanding how products move through your warehouse — from international containers arriving to orders shipping out.

Q6. Walk us through your receiving process for international shipments. What happens from the moment a container arrives to the point inventory is ready to pick?

Step 1:
Step 2:
Step 3:
(Add more as needed)

Typical turnaround time: _______________
Biggest bottleneck: _______________

Q7. Describe your quality inspection process for incoming goods from offshore manufacturers. What do you check, who does it, and what happens when you find defects?

[Your response]

Q8. Walk us through your order fulfillment process — from order received to shipment out the door. How long does it typically take?

Step 1:
Step 2:
Step 3:
(Add more as needed)

Typical turnaround time (order to ship): _______________
Biggest bottleneck: _______________

Q9. How do you handle returns from retail customers? What’s the process, and how does returned inventory get back into stock (or not)?

[Your response]

Q10. How do you organize inventory in the warehouse? (e.g., by product type, by SKU, by season, by velocity, random slotting, etc.) How often does the layout change?

[Your response]

Q11. What are the handoffs between Warehouse and other departments (Buying, Customer Service, Sales, Finance)? Where do those handoffs break down most often?

[Your response]

Q12. How do you prioritize which orders to pick and ship first? Is it automated, or does someone make those decisions manually?

[Your response]

🔲 GPT Brainstorm Insights — Processes: Paste any relevant process-related insights from your brainstorm session here.

[Paste from brainstorm]

Q13. Safety Net: What should we have asked about your processes that we didn’t?

[Your response]

Section 3: Technology (Tools & Systems)

Understanding what systems you use, how well they work, and where technology creates friction.

Q14. What systems/software does Warehouse use? For each one, describe what it does and how well it works for you.

System 1: _______________
  - What we use it for:
  - How well it works (1-5, where 5 = excellent):
  - Biggest frustration with it:

System 2: _______________
  - What we use it for:
  - How well it works (1-5):
  - Biggest frustration with it:

(Add more as needed)

Q15. Do you use a Warehouse Management System (WMS)? If yes, what does it do well, and where does it fall short? If no, how do you track inventory and orders?

[Your response]

Q16. Do you use barcode scanners, RFID, or other tracking technology on the warehouse floor? If yes, how consistently? If no, what’s preventing adoption?

[Your response]

Q17. How do pickers know what to pick and where to find it? (e.g., paper pick tickets, handheld scanners with screens, verbal instructions, experience/memory, etc.)

[Your response]

Q18. Is inventory data synced in real-time between your warehouse system and order management, or is there a delay? How does that affect operations?

[Your response]

Q19. Do you have visibility into inbound shipments before they arrive (tracking, advance ship notices from offshore manufacturers)? How far ahead can you see what’s coming?

[Your response]

Q20. What technology frustration slows your team down the most? (e.g., slow system performance, manual data entry, printers jamming, Wi-Fi dropping, etc.)

[Your response]

🔲 GPT Brainstorm Insights — Technology: Paste any relevant technology-related insights from your brainstorm session here.

[Paste from brainstorm]

Q21. Safety Net: What should we have asked about your technology that we didn’t?

[Your response]

Section 4: Data (Information & Insights)

Understanding what data you have, how you use it, and what’s missing.

Q22. What warehouse metrics do you track? (e.g., inventory accuracy, order cycle time, pick accuracy, shipping errors, receiving turnaround, space utilization, etc.)

[Your response]

Q23. How do you measure inventory accuracy? How often do you do cycle counts or physical inventories, and what’s your typical accuracy rate?

[Your response]

Q24. Do you have visibility into which products are fast-moving vs. slow-moving? How does that affect where things are stored or how you prioritize work?

[Your response]

Q25. When a shipment from offshore arrives with defects or shortages, how do you track that data? Do you analyze trends by manufacturer, product line, or shipper?

[Your response]

Q26. Do you track time-to-ship by order type, customer, or product? If yes, what do you do with that data? If no, would it be useful?

[Your response]

Q27. What data do you WISH you had that would help you run the warehouse more efficiently or reduce errors?

[Your response]

🔲 GPT Brainstorm Insights — Data: Paste any relevant data-related insights from your brainstorm session here.

[Paste from brainstorm]

Q28. Safety Net: What should we have asked about your data and metrics that we didn’t?

[Your response]

Section 5: AI Readiness & Opportunity

Understanding your current relationship with AI and where it could help most.

Q29. Does your team currently use any AI-powered tools in warehouse operations? (e.g., demand forecasting for slotting, route optimization for pickers, computer vision for QC, predictive inventory alerts, etc.)

Yes, actively using AI tools now (please describe): _______________
Yes, occasionally (a few times a month)
Tried it once or twice
No, but interested
No, and not sure it’s relevant to us
Other (please describe): _______________

Q30. On a scale of 1–5, how comfortable would your team be adopting a new AI-powered tool for daily warehouse work?

(1 = Very uncomfortable / resistant — 5 = Very comfortable / excited)

☐ 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5

Q31. If you could automate or dramatically speed up ONE thing in the Warehouse, what would it be?

[Your response]

Q32. Imagine you had an AI assistant dedicated to warehouse operations. What would you have it do? (Select all that apply)

Final Questions

Q33. 🔲 GPT Brainstorm Insights — AI Readiness & Big Ideas: Paste any AI-related ideas, dream-state visions, or opportunity insights from your brainstorm session here.

[Paste from brainstorm]

Q34. THE GAP FINDER: What have we NOT asked about your people, processes, technology, or data that we SHOULD have asked you?

[Your response — be as detailed as you want. This is your chance to tell us what we missed.]

Q35. THE MAGIC WAND: If you had a magic wand and could create the perfect people, processes, technology, or data for your department — what does that look like? Paint the ideal picture.

[Your response — dream big. Describe the ideal state for your department.]

Thank you for your time. Your responses will directly shape the AI recommendations for Warehouse.

— Sunzi.io