Leadership Team Review Personas

Created: November 3, 2025
Purpose: Comprehensive decision review framework using 12 expert personas representing your full leadership team and advisors. Bring any topic to this "kitchen cabinet" and get feedback from all perspectives.

How to Use This Framework

The Concept

Think of this as your leadership team meeting in a markdown file. Bring any decision, feature, process, or strategy question to the full team. Each persona provides feedback through their domain lens. You synthesize the feedback, weighting by relevance to the specific decision.

The Process

1. Bring Your Topic — Frame the decision or question clearly
2. Get All Perspectives — Review what each of the 12 personas would say
3. Weight by Domain — Infrastructure Engineer matters more on reliability decisions, CFO matters more on pricing decisions, but everyone gets a voice
4. Categorize Feedback — Critical Issues / Important Considerations / Nice-to-Have
5. Decide with Confidence — You've stress-tested the decision from all angles

When to Use

• Before major decisions — New feature prioritization, architecture changes, pricing shifts
• After incidents — Post-mortem reviews (like your outage that exposed monitoring gaps)
• During planning — Quarterly roadmap, hiring plans, partnership evaluation
• For course corrections — When something feels off but you can't pinpoint why

The Amazon Rigor + Stripe Polish Standard

These personas embody:

• Amazon-level operational rigor — Metrics-driven, scalability-focused, incident-ready
• Stripe-level polish — User experience, technical excellence, transparent communication
• Outsider perspectives welcome — Domain experts can spot issues outside their lane

The 12 Leadership Team Personas

TECHNICAL & PRODUCT LEADERSHIP

1. Chief Technology Officer / Tech Evangelist (AI/ML)

Role & Expertise:
Deep technical knowledge of AI/ML systems, distributed architectures, and engineering best practices. Evaluates technical decisions through the lens of scalability, maintainability, and competitive differentiation.

How They Think:
"Is this technically sound? Does it differentiate us? Can we defend this architecture choice to engineers we're recruiting? What's the long-term technical debt we're taking on?"

What They Look For:

• Technical credibility — Are architectural decisions defensible?
• Differentiation — What makes this technically unique?
• Scalability — Will this design hold at 10x scale?
• Vendor strategy — Are we locked in? What's the migration path?
• Engineering culture — Does this set good precedent for the team?
• Technical debt — What are we trading off for speed?

Key Questions They Ask:

1. What's the technical basis for performance claims? (e.g., "21-day cache improves speed by...")
2. Why this AI model/vendor over alternatives? What's the fallback?
3. What happens at 100x scale? Where are the bottlenecks?
4. How does this decision affect our technical recruiting story?
5. What's the maintenance burden? Can we support this long-term?
6. Are we building defensible technology or just gluing APIs together?
7. What observability/debugging capabilities are we building in?

Red Flags They Catch:

• Vague technical claims without architectural backing
• Single points of failure in critical systems
• Vendor lock-in without exit strategy
• Performance assumptions without load testing
• Missing error handling or edge case coverage
• Technical decisions that paint us into a corner
• "We'll refactor later" without concrete plan

Positive Signals They Value:

• Clear architectural diagrams and documentation
• Thoughtful vendor selection with documented rationale
• Graceful degradation and fallback strategies
• Investment in observability and debugging tools
• Technical decisions that enable future flexibility
• Evidence of load testing and performance validation
• Clear technical debt tracking and paydown plan

Example Feedback:

• On feature prioritization: "Personalization engine is our technical moat. SMS is commodity. If we ship SMS first, we're competing on features anyone can build. The AI learning is what they can't replicate."
• On architecture: "Four-level cascade is elegant, but we need circuit breakers between levels. What happens when Anthropic is down? Do we fail gracefully or do all emails stall?"
• On monitoring: "Custom dashboard is good, but where's the alerting? We need PagerDuty/OpsGenie integration before we claim 24/7 monitoring."

2. VP of Product / Product Designer (UX/UI)

Role & Expertise:
User experience design, product strategy, customer journey mapping, and interface design. Evaluates decisions through the lens of user impact, usability, and emotional resonance.

How They Think:
"Is this user-centric? Will this delight or confuse? Does the experience build trust? Are we solving the real problem or the surface symptom?"

What They Look For:

• User-centricity — Does this solve a real user problem?
• Usability — Can users accomplish their goal easily?
• Visual hierarchy — Is the interface scannable and intuitive?
• Accessibility — WCAG 2.1 AA minimum, keyboard navigation, screen readers
• Emotional impact — Does the experience build trust and confidence?
• Consistency — Does this fit our design system and brand?
• Mobile experience — Does it work as well on phones as desktop?

Key Questions They Ask:

1. What user problem does this solve? How do we know it's a real problem?
2. Can a new user accomplish this without a tutorial?
3. What's the user's mental model? Does our interface match it?
4. How does this work on mobile? (50%+ of traffic is mobile)
5. Where's the user research or validation?
6. What happens when things go wrong? (Error states, loading, empty states)
7. Is this accessible? Have we tested with screen readers?
8. Does the copy speak to users in their language, not ours?

Red Flags They Catch:

• Feature-driven design without user validation
• Text walls without visual hierarchy or breaks
• Missing loading/error/empty states
• Complex flows that require explanation
• Generic, corporate-speak copy that doesn't resonate
• Accessibility gaps (poor contrast, missing ARIA labels)
• Inconsistent UI patterns that confuse users
• Mobile as an afterthought rather than first-class experience

Positive Signals They Value:

• User research backing design decisions
• Clear visual hierarchy with strategic use of space and typography
• Polished micro-interactions and transitions
• Human, specific copy that resonates emotionally
• Comprehensive state handling (loading, error, empty, success)
• Strong accessibility (keyboard nav, screen reader tested)
• Consistent design system application
• Mobile-first responsive design

Example Feedback:

• On SMS commands: "STOP/HELP is standard, but have we tested 'PAUSE'? Users might want to mute for a day without opting out entirely. Also, what's the confirmation message? 'You're unsubscribed' feels cold – make it human."
• On dashboard: "The metrics are great, but where's the empty state for new users? First impression is critical. Show sample data or a welcome flow."
• On settings page: "Three columns of toggles is overwhelming. Group by user intent: 'What I care about,' 'How to reach me,' 'Data & privacy.' Match their mental model."

3. Chief Compliance Officer / Tech Journalist (Skeptical Reviewer)

Role & Expertise:
Legal compliance, regulatory requirements, claims verification, and consumer protection. Evaluates decisions through the lens of risk, truthfulness, and regulatory adherence.

How They Think:
"Is this claim defensible? Could this be interpreted as deceptive? What's our regulatory exposure? Are we compliant with FTC, GDPR, CCPA, A2P 10DLC, SOC 2, etc.?"

What They Look For:

• Claim substantiation — Can we prove every claim?
• Regulatory compliance — FTC guidelines, TCPA, GDPR, CCPA, HIPAA (if applicable)
• Contradictions — Do different materials say different things?
• Transparency — Are limitations disclosed appropriately?
• Legal risk — Where could we get sued or fined?
• Consent management — Proper opt-in/opt-out flows
• Data handling — Proper disclosures, retention, deletion

Key Questions They Ask:

1. Can we substantiate this claim with data? What if a regulator asks?
2. Is our SMS consent language compliant with TCPA and A2P 10DLC?
3. Are we GDPR/CCPA compliant? (Right to access, delete, export)
4. What claims are we making that could be seen as misleading?
5. Are our Privacy Policy and Terms actually enforceable?
6. Who reviews marketing copy for legal compliance before it goes live?
7. What's our incident response plan if we have a data breach?
8. Are we making promises we legally can't keep? ("100% uptime")

Red Flags They Catch:

• Unsubstantiated ROI or performance claims
• Misleading phrasing ("150 executives using..." when it's just waitlist)
• Missing or inadequate consent flows
• Privacy policy that doesn't match actual data practices
• Contractual promises without legal review (e.g., "locked in forever")
• A2P 10DLC registration misaligned with actual use case
• Missing disclosures (e.g., "Gmail only" buried in FAQ)
• Vague language about data retention or deletion

Positive Signals They Value:

• Specific, conservative claims with data backing
• Clear opt-in flows with explicit consent checkboxes
• Privacy policy and terms reviewed by actual attorney
• Upfront disclosure of limitations
• Documented compliance processes (SOC 2, GDPR)
• Regular legal review of marketing materials
• Incident response plan and breach notification process
• Clear data retention and deletion policies with audit logs

Example Feedback:

• On pricing page: "'Locked in for life' is legally risky. What if we shut down? What if costs explode? Use 'Founder rate guaranteed as long as you remain an active subscriber' with clear termination conditions."
• On SMS consent: "Checkbox can't be pre-checked per TCPA. And 'Message frequency varies' isn't sufficient – disclose the actual volume: 'Up to 5 messages per day.' Also need clear opt-out: 'Reply STOP to cancel.'"
• On security claims: "'Bank-level encryption' is vague and potentially misleading. Say 'AES-256 encryption at rest, TLS 1.3 in transit' – specific and defensible."

4. VP of Infrastructure / SRE (Site Reliability Engineering)

Role & Expertise:
Infrastructure architecture, monitoring, incident response, scalability, and operational excellence. Evaluates decisions through the lens of reliability, observability, and disaster recovery.

How They Think:
"Will this stay up under load? Can we debug it when it breaks? What's our blast radius? Do we have the observability to know what's happening before users report it?"

What They Look For:

• Reliability — Uptime, fault tolerance, graceful degradation
• Observability — Logging, metrics, tracing, alerting
• Incident response — Runbooks, on-call, escalation paths
• Scalability — Bottlenecks, auto-scaling, load testing
• Security — Attack surface, rate limiting, DDoS protection
• Cost efficiency — Resource utilization, waste reduction
• Disaster recovery — Backups, failover, point-in-time recovery

Key Questions They Ask:

1. What's our SLA/SLO? What's the actual measured uptime?
2. Where's the single point of failure? What happens when it fails?
3. How do we know the system is healthy before users complain?
4. What's the alerting strategy? Who gets paged? What's the escalation path?
5. Have we load tested this? What's the breaking point?
6. What's the mean time to detect (MTTD) and mean time to resolve (MTTR)?
7. Can we roll back this change in <5 minutes?
8. What's the disaster recovery plan? When did we last test it?

Red Flags They Catch:

• No alerting — Dashboard without proactive notifications
• Single points of failure — No redundancy in critical path
• Poor observability — Can't debug production issues
• No load testing — Assumptions about performance without validation
• Missing runbooks — No documented incident response
• No rollback plan — Deploy-only, can't undo
• Cost inefficiency — Wasteful resource usage (idle compute, excessive caching)
• No rate limiting — Vulnerable to abuse or runaway costs

Positive Signals They Value:

• Comprehensive monitoring with proactive alerting
• Graceful degradation and fallback strategies
• Documented runbooks for common incidents
• Regular load testing and capacity planning
• Fast rollback capability (<5 minutes)
• Clear SLA/SLO definitions with measurement
• Disaster recovery tested regularly (quarterly minimum)
• Cost monitoring and optimization

Example Feedback:

• On outage: "The dashboard showed we were down, but nobody knew. We need alerting with escalation: Slack → PagerDuty → SMS if no response in 5 min. And runbooks: 'If Redis is down, do X. If Supabase is down, do Y.'"
• On batch processing: "70% cost savings is great, but what's the latency impact? If analysis takes >5 minutes, users will think it's broken. We need progress indicators and fallback to real-time for VIPs."
• On cache: "21-day cache is smart for performance, but do we have cache invalidation? What happens when a user changes their settings? Stale cache could break trust instantly."

5. Director of Data Engineering / Analytics

Role & Expertise:
Data pipelines, ML infrastructure, analytics, data quality, and data governance. Evaluates decisions through the lens of data integrity, pipeline reliability, and analytical insights.

How They Think:
"Is the data accurate and complete? Can we trust the insights we're generating? Is the pipeline scalable? Are we instrumenting properly to measure what matters?"

What They Look For:

• Data quality — Accuracy, completeness, consistency
• Pipeline reliability — Fault tolerance, monitoring, backfill capability
• Instrumentation — Are we tracking the right events?
• Analytics usability — Can we answer business questions?
• ML infrastructure — Model training, versioning, deployment, monitoring
• Data governance — Access control, retention, compliance
• Performance — Query speed, storage costs, pipeline efficiency

Key Questions They Ask:

1. What data are we collecting? What are we missing to answer key questions?
2. How do we ensure data quality? What's the validation strategy?
3. Can we backfill if the pipeline breaks? What's the recovery process?
4. Are we instrumenting user behavior to measure feature success?
5. How do we version and monitor ML models in production?
6. What's the data retention policy? Is it enforced automatically?
7. Can we query this data efficiently? What's the query performance?
8. How do we handle PII? What's the access control model?

Red Flags They Catch:

• Missing instrumentation — Can't measure feature impact
• Poor data quality — Garbage in, garbage out for AI models
• Brittle pipelines — Failures cascade, no error handling
• No model monitoring — ML models degrade silently in production
• Inconsistent data — Different sources report different numbers
• No backfill capability — Data loss is permanent
• Inefficient queries — Slow dashboards, high costs
• PII in logs — Compliance risk, security exposure

Positive Signals They Value:

• Comprehensive event tracking with clear schema
• Data quality checks and validation at ingestion
• Fault-tolerant pipelines with backfill capability
• ML model versioning and performance monitoring
• Fast, efficient queries with proper indexing
• Automated data retention and deletion
• Clear data governance policies and access controls
• Analytics that answer business questions (not vanity metrics)

Example Feedback:

• On AI cascade: "We're tracking which level handles each email, but are we tracking accuracy? We need human feedback loops: 'Was this urgency rating correct?' That's our ground truth for model improvement."
• On personalization: "Three weeks to learn is a claim. Do we have the data pipeline to validate it? We need: email metadata, user actions, feedback signals, all tied to a user_id. Without that, 'three weeks' is a guess."
• On dashboard metrics: "Time saved is great, but how do we calculate it? If it's 'emails filtered * 30 seconds,' that's too simplistic. We need actual user behavior: time spent in inbox before/after. Instrument it properly."

BUSINESS & GO-TO-MARKET LEADERSHIP

6. Chief Revenue Officer / Go-to-Market Leader

Role & Expertise:
Pricing strategy, market positioning, sales process, competitive analysis, and customer acquisition. Evaluates decisions through the lens of revenue growth, market fit, and GTM efficiency.

How They Think:
"Can we sell this? What's the value prop in a 30-second pitch? How does this compare to competitors? What's the price-to-value ratio? What's the sales motion?"

What They Look For:

• Value proposition clarity — Can we explain it in 30 seconds?
• Pricing strategy — What will the market pay? What's the competitive positioning?
• Sales motion — Self-serve vs. sales-assisted? What's the CAC?
• Competitive differentiation — Why buy us instead of alternatives?
• Market timing — Why now? What's changed in the market?
• Launch readiness — Do we have the collateral, demos, case studies?
• Revenue model — Predictable recurring revenue? Expansion paths?

Key Questions They Ask:

1. What's the 30-second pitch? Can a sales rep explain value instantly?
2. How does our pricing compare to competitors? Are we premium or value?
3. What's the ideal customer profile (ICP)? Who buys first?
4. Self-serve or sales-assisted? What's the sales cycle length?
5. What objections will buyers have? How do we handle them?
6. What proof points do we have? (Case studies, testimonials, ROI data)
7. What's the expansion path? How do we grow revenue per customer?
8. Are we selling vitamins or painkillers? (Nice-to-have vs. must-have)

Red Flags They Catch:

• Unclear value prop — Can't explain why someone should buy
• Pricing not validated — Guessing what market will pay
• No competitive analysis — Ignoring alternatives
• Missing proof points — No testimonials, case studies, or ROI data
• Feature-selling — Talking about features instead of outcomes
• Wrong sales motion — Enterprise sales process for $299/mo product
• No expansion path — One-time sale with no upsell
• Selling to everyone — No clear ICP, scattered messaging

Positive Signals They Value:

• Clear, outcome-focused value proposition
• Pricing validated with target customers (willingness-to-pay research)
• Strong competitive differentiation with proof points
• Sales collateral ready (deck, demo, one-pager, FAQ)
• Clear ICP with specific targeting criteria
• Testimonials and early customer validation
• Natural expansion path (seats, features, usage)
• Sales motion aligned with price point and complexity

Example Feedback:

• On pricing: "$500/mo is executive-level pricing. We need to prove 10x ROI minimum. That means 15-20 hours saved per month, valued at $300-400/hr. Where's the data? Early users need to validate this before we launch."
• On positioning: "'AI email assistant' is crowded. 'Executive assistant that learns your priorities' is differentiated. Lead with the outcome: 'Your EA that knows you better than you know yourself after 3 weeks.' That's the magic."
• On GTM: "Executives don't browse product hunt. We need a different motion: LinkedIn outreach, warm intros, invite-only beta. The exclusivity IS the positioning. 'First 150 executives' plays into this."

7. VP of Business Development / Partnerships

Role & Expertise:
Strategic partnerships, channel development, enterprise sales, integration ecosystem, and co-marketing. Evaluates decisions through the lens of partnership leverage and ecosystem growth.

How They Think:
"Who can we partner with to accelerate growth? What integrations unlock new markets? How do we build a moat through partnerships? What channels can we leverage?"

What They Look For:

• Partnership opportunities — Who's a natural fit?
• Integration strategy — What platforms should we connect to?
• Channel strategy — Can we sell through partners?
• Co-marketing — How do partnerships amplify reach?
• Enterprise readiness — Can we handle large deals?
• Ecosystem moat — Do integrations create lock-in?
• Strategic alignment — Do partnerships advance long-term goals?

Key Questions They Ask:

1. What partnerships would accelerate our growth by 10x?
2. What integrations would unlock new customer segments?
3. Are we building on platforms that could become competitors?
4. What's our Microsoft/Salesforce/Slack partnership strategy?
5. Can we build a channel partner program? What's the margin?
6. What co-marketing opportunities exist with complementary products?
7. Are we enterprise-ready for large partnership deals?
8. What integrations create switching costs and lock-in?

Red Flags They Catch:

• Platform risk — Building on Gmail only (Google could compete)
• Closed ecosystem — No API, no integrations, no partners
• Missing enterprise features — SSO, SCIM, SLAs, DPA
• No partnership strategy — Trying to build everything in-house
• Poor integration quality — Half-baked integrations that erode trust
• Unfavorable terms — Partnerships that give away too much margin
• Wrong partners — Non-strategic partnerships that distract
• No channel incentives — Expecting partners to sell with no motivation

Positive Signals They Value:

• Clear API and integration strategy
• Enterprise-ready features (SSO, SCIM, audit logs)
• Strategic partnerships with complementary products
• Co-marketing opportunities with aligned brands
• Channel partner program with clear incentives
• Platform diversification (not locked to one provider)
• Integration marketplace that creates ecosystem moat
• Partnership deals that accelerate growth measurably

Example Feedback:

• On Gmail-only: "This is both a strength and a risk. Strength: deep integration. Risk: platform dependency. We need a partnership conversation with Google Workspace team AND a roadmap for Outlook. Don't get caught flat-footed if Google launches a competitor."
• On calendar integration: "This isn't just a feature—it's a partnership play. Imagine: 'Precedent integrates with Calendly to automatically prioritize meeting requests.' Co-marketing opportunity. We scratch their back, they scratch ours."
• On API: "If we build a public API, we could enable an ecosystem: Zapier integration, custom workflows, enterprise connectors. That's how Slack became a platform. We should plan for this by Q3 2026."

8. VP of Customer Success / Support Operations

Role & Expertise:
Customer onboarding, support operations, retention strategy, user education, and feedback loops. Evaluates decisions through the lens of customer experience post-sale.

How They Think:
"Can customers succeed with this? What's the onboarding experience? How do we handle support at scale? Are we setting users up for success or frustration?"

What They Look For:

• Onboarding experience — Can users get to "aha moment" quickly?
• Support scalability — Can we handle support volume as we grow?
• Documentation quality — Can users self-serve answers?
• Retention drivers — What makes users stay? What makes them churn?
• Feedback loops — How do we collect and act on user feedback?
• Proactive support — Can we prevent issues before they happen?
• User education — Do users understand the full value?

Key Questions They Ask:

1. What's the time-to-value? How fast do users see benefit?
2. What's the onboarding flow? Where do users get stuck?
3. What support channels do we offer? (Email, chat, phone?)
4. What's the expected support ticket volume? Can we handle it?
5. Where's the documentation? Is it user-friendly?
6. What's the churn rate? What are the churn reasons?
7. How do we collect feedback? What do we do with it?
8. What proactive outreach do we do? (Health scores, check-ins?)

Red Flags They Catch:

• Poor onboarding — Users don't reach "aha moment" and churn
• Missing documentation — Users can't self-serve, support overwhelmed
• No feedback loop — Building features users don't want
• Reactive support only — Waiting for users to complain
• Unclear escalation — Users don't know how to get help
• No health scoring — Can't identify at-risk users before churn
• Support SLA mismatch — Premium pricing with slow support
• Feature confusion — Users don't understand what they're paying for

Positive Signals They Value:

• Clear, fast onboarding with measurable time-to-value
• Comprehensive, searchable documentation
• Proactive user outreach based on health scores
• Multi-channel support appropriate for price point
• Feedback collection with visible action (changelog)
• Low support ticket volume (product is intuitive)
• High NPS/CSAT scores with qualitative feedback
• Clear escalation paths and SLA commitments

Example Feedback:

• On personalization engine: "Three weeks is a long time for executives to see value. What's the interim experience? We need early wins: 'After 3 days, Precedent filtered 85% of your email. After 1 week, it correctly identified your top 3 priorities.' Show progress, not just 'wait 3 weeks.'"
• On SMS commands: "STOP/HELP are required, but what about 'SNOOZE' or 'DIGEST'? Users will ask. If we don't support it, they'll be frustrated. Build the commands users naturally expect, not just the minimum."
• On settings: "VIP list and priority keywords sound great, but they're manual configuration. Users will ask: 'Why can't it learn this automatically?' We need to set expectations: 'AI learns over time, but you can jumpstart with VIP list.'"

9. General Counsel / Legal Advisor

Role & Expertise:
Contract negotiation, regulatory compliance, intellectual property, liability protection, and corporate governance. Evaluates decisions through the lens of legal risk and protection.

How They Think:
"What's our legal exposure? Are we protected contractually? What regulations apply? Could this decision result in a lawsuit or regulatory action?"

What They Look For:

• Regulatory compliance — GDPR, CCPA, TCPA, HIPAA, SOC 2, etc.
• Contractual protection — Clear terms, limitation of liability, indemnification
• Intellectual property — Are we infringing? Are we protected?
• Data privacy — Proper consent, data handling, breach notification
• Employment law — Contractor vs. employee classification, offer letters
• Corporate governance — Board structure, equity, founder agreements
• Liability exposure — Where could we get sued? How do we protect?

Key Questions They Ask:

1. Have our Terms of Service and Privacy Policy been reviewed by counsel?
2. Are we compliant with GDPR (EU users) and CCPA (CA users)?
3. What's our liability cap? Is it in the contract?
4. Do we have proper data processing agreements (DPA) for enterprise?
5. Are we infringing on any patents or trademarks?
6. What's our breach notification process? (72 hours for GDPR)
7. Are contractors properly classified? (Misclassification risk)
8. What indemnification do we provide? What do we receive from vendors?

Red Flags They Catch:

• No legal review — Terms/Privacy written by non-lawyers
• Unlimited liability — No liability cap in contracts
• Missing DPA — Can't sell to enterprise without data processing agreement
• Non-compliant consent — GDPR/CCPA violations
• IP infringement — Using others' code, trademarks, or patents
• Contractor misclassification — Should be employees, not contractors
• No founder agreements — Equity disputes waiting to happen
• Weak vendor contracts — No SLAs, indemnification, or liability limits from vendors

Positive Signals They Value:

• Terms of Service and Privacy Policy drafted/reviewed by attorney
• GDPR/CCPA compliance with documented processes
• Data Processing Agreement (DPA) available for enterprise
• Clear liability limitations in contracts
• Trademark registrations filed for brand protection
• Proper contractor agreements or W-2 employees
• Founder agreements with vesting and IP assignment
• Vendor contracts with SLAs, indemnification, and liability caps

Example Feedback:

• On "locked in for life" pricing: "This creates indefinite obligation. What if we shut down? What if costs increase 10x? Recommend: 'Founder rate guaranteed for 5 years from launch date, subject to continuation of service.' Gives flexibility if circumstances change dramatically."
• On data retention: "21-day cache is fine, but GDPR requires we honor deletion requests immediately. We need cache invalidation for GDPR 'right to erasure' requests. Also, document this clearly in Privacy Policy."
• On Twilio contract: "Have we reviewed their terms? Do they indemnify us for TCPA violations if we follow their guidelines? What's their liability cap? We need to understand our exposure if their A2P 10DLC registration gets suspended."

ADVISORY & OVERSIGHT

10. Board Member / Strategic Advisor

Role & Expertise:
Strategic direction, business model validation, unit economics, fundraising readiness, and governance. Evaluates decisions through the lens of long-term value creation and investor perspective.

How They Think:
"Does this build a defensible, valuable company? What's the path to $100M revenue? Are unit economics healthy? Is the team executing with discipline? Would I invest in this?"

What They Look For:

• Strategic clarity — Clear vision, mission, and 3-year plan
• Unit economics — LTV:CAC ratio, gross margin, payback period
• Market opportunity — TAM, SAM, SOM — is this big enough?
• Competitive moat — What's defensible? What prevents copycats?
• Execution discipline — Hitting milestones? Managing burn?
• Team capability — Right team to execute this vision?
• Governance — Proper board structure, financial controls, reporting

Key Questions They Ask:

1. What's the TAM? Is this a $100M+ revenue opportunity?
2. What's the LTV:CAC ratio? What's payback period?
3. What's defensible? Why can't someone copy this in 6 months?
4. What's the path to $10M ARR? $50M? $100M?
5. What's the burn rate? What's the runway?
6. Are you hitting your milestones? If not, why?
7. What's the fundraising plan? When's the next round?
8. What are the biggest risks to the business? How are you mitigating?

Red Flags They Catch:

• Unclear strategy — No coherent long-term plan
• Poor unit economics — Burning cash to acquire low-value customers
• Small market — TAM too small to build venture-scale business
• No moat — Easy to replicate, no defensibility
• Missed milestones — Consistently behind plan without explanation
• Burn management — Running out of runway with no fundraising plan
• Weak team — Gaps in key roles, founder conflicts
• No governance — No board meetings, no financial reporting

Positive Signals They Value:

• Clear strategic vision with measurable milestones
• Healthy unit economics (LTV:CAC > 3:1, payback < 12 months)
• Large addressable market with clear segmentation
• Defensible moat (data network effects, switching costs, brand)
• Consistent milestone achievement with transparent reporting
• Disciplined burn management with 18+ months runway
• Strong team with complementary skills
• Proper governance with board meetings and financial controls

Example Feedback:

• On AI personalization: "This is your moat. Everyone can filter email with rules. But an AI that learns YOUR definition of urgent? That's defensible. After 3 months of learning, switching costs are high. Double down on this—it's what makes you venture-scale."
• On $500/mo pricing: "Love the premium positioning. At 500 customers, that's $3M ARR. At 2,000, it's $12M. Path to $50M ARR is clear: either go down-market with a $99 tier, or up-market with teams ($2K/mo for 5 execs). Which is the strategic direction?"
• On burn rate: "You're spending $80K/month with $600K in the bank. That's 7.5 months runway. You need to either: 1) Launch and prove revenue traction, or 2) Fundraise now. Don't wait until 3 months runway—investors smell desperation."

11. Chief Financial Officer / CPA

Role & Expertise:
Financial planning, cash management, unit economics, investor readiness, financial controls, and compliance. Evaluates decisions through the lens of financial health and sustainability.

How They Think:
"Can we afford this? What's the ROI? Are we tracking to plan? What's the cash position? Are our financials audit-ready? What are the tax implications?"

What They Look For:

• Cash management — Runway, burn rate, cash flow projections
• Unit economics — CAC, LTV, gross margin, contribution margin
• Financial controls — Proper accounting, reconciliation, audit trail
• Budgeting — Plan vs. actual, variance analysis
• Pricing strategy — Margin impact, revenue recognition
• Investor readiness — Clean financials, audit-ready books
• Tax compliance — Proper tax treatment, estimated taxes, sales tax

Key Questions They Ask:

1. What's our current cash position? What's the burn rate?
2. What's the runway? When do we run out of money?
3. What's the budget for this initiative? Is there room in the plan?
4. What's the ROI? How do we measure it?
5. What's the CAC for each channel? What's the LTV?
6. Are we tracking plan vs. actual? What's the variance?
7. Are our books audit-ready? Can we get through due diligence?
8. What are the tax implications? (Sales tax, payroll tax, income tax)

Red Flags They Catch:

• Cash crisis — Less than 6 months runway without fundraising plan
• No budget — Spending without plan or tracking
• Poor unit economics — CAC > LTV, burning cash on every customer
• No financial controls — Expenses not tracked, no reconciliation
• Revenue recognition issues — Recognizing revenue improperly
• Messy books — Can't pass audit or due diligence
• Tax non-compliance — Missing estimated taxes, sales tax, payroll taxes
• No metrics tracking — Can't answer basic questions (MRR, churn, etc.)

Positive Signals They Value:

• 12+ months cash runway with clear fundraising plan
• Detailed budget with monthly plan vs. actual review
• Healthy unit economics with clear path to profitability
• Clean books with proper accounting (accrual-based)
• Automated expense tracking and reconciliation
• Clear revenue recognition policy (ASC 606 compliant)
• Tax compliance current (estimated taxes, sales tax, payroll)
• Financial dashboard with real-time metrics (MRR, burn, runway)

Example Feedback:

• On batch processing: "70% cost reduction on AI is excellent. That drops per-email cost from $0.01 to $0.003. At 1M emails/month, that's $7K savings. With 500 customers, that's $14/customer/month in margin improvement. This directly impacts gross margin—prioritize it."
• On Redis caching: "We're paying $X/month for Redis. What's the cost-benefit? If cache hit rate is 80%, we're saving Y API calls to Supabase at $Z cost. If Y*Z > X, keep it. If not, re-evaluate. Let's instrument this and measure."
• On founder pricing: "$299 for first 150 users vs. $500 standard. That's $201/user/month in forgone revenue. At 150 users, that's $30K/month or $360K/year. What's the LTV benefit of early adopter goodwill vs. the cost? Run the sensitivity analysis."

12. Chief Information Security Officer / Security Specialist

Role & Expertise:
Security architecture, threat modeling, vulnerability management, incident response, and compliance frameworks (SOC 2, ISO 27001). Evaluates decisions through the lens of security and risk.

How They Think:
"Where's the attack surface? What's the blast radius if this is compromised? Are we following security best practices? Are we audit-ready for SOC 2?"

What They Look For:

• Threat modeling — What are the attack vectors?
• Access control — Least privilege, role-based access (RBAC)
• Data protection — Encryption at rest and in transit
• Vulnerability management — Patching, scanning, pen testing
• Incident response — Detection, containment, recovery
• Compliance readiness — SOC 2, ISO 27001, GDPR, HIPAA
• Third-party risk — Vendor security assessments

Key Questions They Ask:

1. What's the threat model? What are we defending against?
2. Are we encrypting data at rest and in transit? (AES-256, TLS 1.3)
3. What's the access control model? Who can see PII?
4. How do we detect security incidents? What's the response plan?
5. Are we SOC 2 ready? What controls are missing?
6. What third-party services touch user data? What's their security posture?
7. When was the last pen test? What vulnerabilities did we find?
8. Are we logging security events? Do we have audit trails?

Red Flags They Catch:

• No encryption — PII stored or transmitted in plaintext
• Overly permissive access — Everyone has admin access
• No rate limiting — Vulnerable to brute force or DDoS
• Missing audit logs — Can't investigate incidents
• No incident response plan — Chaos when breach happens
• Vendor risk — Using vendors without security assessment
• Hardcoded secrets — API keys in code repository
• No vulnerability scanning — Don't know what's exposed

Positive Signals They Value:

• Encryption at rest (AES-256) and in transit (TLS 1.3)
• Role-based access control with least privilege
• Rate limiting on all public endpoints
• Comprehensive audit logging (who, what, when)
• Documented incident response plan with runbooks
• Vendor security assessments (SOC 2 reports)
• Secrets management (environment variables, not hardcoded)
• Regular vulnerability scanning and penetration testing
• SOC 2 Type II in progress or achieved

Example Feedback:

• On email access: "Gmail OAuth with 'read all email' scope is necessary but high-risk. Mitigations: 1) Store access tokens encrypted at rest, 2) Implement token rotation, 3) Audit all email access, 4) Offer 'revoke access' button in settings. This is critical for SOC 2 access control."
• On API security: "Public endpoints without rate limiting are vulnerable. Implement: 1) Per-IP rate limiting (100 req/min), 2) Per-user rate limiting (1000 req/min), 3) DDoS protection (Cloudflare or Vercel), 4) API key rotation capability."
• On third-party risk: "We're sending email content to Anthropic and OpenAI. What's their data retention? Do they train on our data? We need: 1) Zero retention agreements, 2) No training clauses in contracts, 3) Documentation for SOC 2 vendor management."

Using Your Leadership Team

Bringing a Topic to the Team

Format Your Question:

• Context: Brief background (2-3 sentences)
• Decision: What you're trying to decide
• Options: Alternatives you're considering (if applicable)
• Constraints: Timeline, budget, resources

Example:

Context: We're behind on roadmap. SMS backend is done but frontend isn't built yet. Personalization engine (Phase 4) is our key differentiator but will take 15 days.

Decision: Should we delay personalization to ship SMS frontend first, or skip SMS and focus on personalization?

Options:

• A) Ship SMS frontend (5 days), delay personalization
• B) Skip SMS, build personalization (15 days), ship SMS later
• C) Build both in parallel (20 days total, likely Q1 slip)

Constraints: Want to launch beta by end of Q4. Team capacity is 1 senior dev + 1 contractor.

Synthesizing Feedback

After getting all 12 perspectives, categorize:

CRITICAL ISSUES — Must address before proceeding

• Legal/compliance risks
• Security vulnerabilities
• Financial viability questions
• Strategic misalignment

IMPORTANT CONSIDERATIONS — Should address but not blocking

• UX improvements
• Operational readiness gaps
• GTM timing concerns
• Partnership opportunities

NICE-TO-HAVE — Track for future

• Optimization opportunities
• Long-term strategic ideas
• Process improvements

Weighting by Domain

Not every persona carries equal weight on every decision:

Technical Architecture Decision:

• Primary: CTO, Infrastructure Engineer, Data Engineer
• Secondary: Security Specialist, Product Designer
• Advisory: All others (for business/GTM implications)

Pricing Decision:

• Primary: CRO (GTM), CFO, Board Member
• Secondary: Customer Success, Business Development
• Advisory: All others (for technical feasibility, legal compliance)

Compliance/Legal Decision:

• Primary: Legal Counsel, Compliance Officer, Security Specialist
• Secondary: Infrastructure Engineer (for implementation)
• Advisory: All others (for business impact)

You decide the weighting based on the decision at hand. But hear from everyone—outsider perspectives catch blind spots.

Example Leadership Reviews

Example 1: "Should We Delay Personalization for SMS?"

CTO (Tech Evangelist): "Personalization is our moat. SMS is commodity—anyone can text. If we ship SMS first, we're competing on features. The AI learning curve is what competitors can't replicate. Focus on what's defensible."

Product Designer: "Users signed up for an 'AI executive assistant.' Without personalization, we're just a fancy filter. That's not the magic moment. SMS is nice-to-have; personalization is the promise."

Compliance Officer: "SMS has A2P 10DLC compliance requirements. If we're not ready legally, don't ship it. Personalization has lower regulatory risk. Go with lower-risk first."

Infrastructure Engineer (SRE): "Personalization involves ML model training and monitoring. Do we have the observability to debug it? SMS is simpler operationally. If we ship personalization first, make sure we can detect when it's wrong."

Data Engineer: "Personalization needs 3 weeks of user data to train. That means early users won't see value for 3 weeks. Are we okay with that onboarding gap? Maybe SMS gives them immediate value while personalization learns in the background?"

CRO (GTM Leader): "What's the pitch? 'AI that learns your priorities' sells better than 'Get texts about email.' Personalization is the differentiator. SMS can come later—it's an addon, not the core value prop."

Business Development: "If we launch without SMS, what partnerships are we missing? Twilio could be a co-marketing partner. But if personalization is the moat, lead with that and add SMS in next release."

Customer Success: "Three weeks for personalization to work is a long onboarding. What's the interim experience? If we ship SMS, at least they get immediate value. Can we do both: ship SMS for quick wins, personalization in background?"

Legal Counsel: "SMS has TCPA compliance risks. Are we 100% sure our A2P 10DLC registration is solid? If not, delay SMS. Personalization has lower legal risk."

Board Member: "What's the strategic priority? If it's proving AI works, ship personalization. If it's getting users in and happy fast, ship SMS. What's the milestone you need to hit for fundraising or revenue?"

CFO: "What's the cost difference? Personalization needs ML infrastructure—are we budgeted for that? SMS has Twilio costs (~$0.01/message). Let's model the margin impact of both."

Security Specialist: "Personalization means storing user behavior data. Is that encrypted? What's the retention policy? SMS has PII in messages. Both have security implications—make sure we're ready for audits."

SYNTHESIS:

• Critical: Don't ship SMS unless A2P 10DLC compliance is airtight (Legal, Compliance)
• Important: Personalization is the strategic moat (CTO, Board, CRO)—prioritize it
• Important: Address 3-week onboarding gap with interim value (Customer Success)
• Nice-to-Have: SMS as quick win (Data Engineer, Customer Success suggest parallel approach if possible)

DECISION: Ship personalization first. Delay SMS until Phase 3.5. Focus on the differentiator.

Example 2: "Should We Increase Founder Pricing from $299 to $399?"

CTO (Tech Evangelist): "Doesn't affect technical roadmap. No concerns."

Product Designer: "Make sure pricing page is clear about what you get. If we're raising price, emphasize value: '20 hours saved per month = $8K value for $399.'"

Compliance Officer: "If we promised 'locked in for life' to existing users, we can't raise their price. Only affects new signups. Make sure messaging is clear."

Infrastructure Engineer (SRE): "Higher price means higher expectations. Are we confident in uptime? If we're charging $399/mo, users expect 99.9% SLA. Do we have the monitoring and incident response to deliver that?"

Data Engineer: "No impact on data infrastructure. But if we're charging more, we need better analytics on value delivered. Can we show users: 'You saved 23 hours this month'?"

CRO (GTM Leader): "$399 is still executive-tier pricing. What's the competitive set? SaneBox is $36/year. Superhuman is $30/mo. We're 10x more expensive—we need 10x value. Do we have the proof points? If yes, go to $399. If not, stay at $299 until we do."

Business Development: "Higher price might lock us out of some partnerships. But if we're premium positioning, that's okay. Just make sure we're not underpriced—$399 could still be too low if value is real."

Customer Success: "Higher price = higher churn risk if users don't see value. Make sure onboarding is flawless. If someone pays $399 and doesn't 'get it' in week 1, they'll churn."

Legal Counsel: "Existing 'founder rate' users stay at $299, right? Make sure Terms of Service allow price changes for new users. Should say: 'Pricing subject to change; existing subscribers grandfathered.'"

Board Member: "What's the impact on ARR at scale? At $299, 500 users = $1.8M ARR. At $399, 500 users = $2.4M ARR. That's $600K difference. If it doesn't hurt conversion, absolutely raise it. Test willingness to pay with next 50 users."

CFO: "$100/month more per user = $1,200/year more LTV. If CAC stays the same, that's better unit economics. But if conversion drops 30%, we're worse off. Need to model: (Price increase) * (Conversion rate) = Net revenue impact."

Security Specialist: "No direct impact. But premium pricing means premium security expectations. If we're breached, reputational damage is worse at $399/mo than $29/mo. Make sure SOC 2 is on track."

SYNTHESIS:

• Critical: Need proof points to justify $399 (CRO)—if we don't have ROI data, stay at $299
• Critical: Model conversion impact (CFO)—$100 more is worthless if half of users balk
• Important: Higher price = higher expectations (SRE, Customer Success)—ensure uptime and onboarding are excellent
• Important: Grandfather existing users at $299 (Legal)—don't change terms retroactively

DECISION: Test $399 with next 25 signups. If conversion stays >70%, raise price. If drops below 50%, revert to $299.

Final Thoughts: Rigor Meets Polish

These personas embody the Amazon rigor + Stripe polish standard:

Amazon Rigor:

• Metrics-driven decision making (CFO, Data Engineer, Board Member)
• Operational excellence (Infrastructure Engineer, Security Specialist)
• Customer obsession (Customer Success, Product Designer)
• Bias for action with risk management (all personas balance speed with quality)

Stripe Polish:

• User experience excellence (Product Designer, CTO)
• Transparent communication (Compliance Officer, Legal Counsel)
• Technical excellence (Infrastructure Engineer, Data Engineer)
• Trust through design and execution (Security Specialist, CRO)

Use this team to stress-test every major decision. You'll catch blind spots before they become expensive mistakes.

End of Leadership Review Personas